{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dUeKVCYTbcyT"
   },
   "source": [
    "#### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:28.878410Z",
     "iopub.status.busy": "2024-01-17T02:20:28.878184Z",
     "iopub.status.idle": "2024-01-17T02:20:28.881884Z",
     "shell.execute_reply": "2024-01-17T02:20:28.881344Z"
    },
    "id": "4ellrPx7tdxq"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7JfLUlawto_D"
   },
   "source": [
    "# Classification on imbalanced data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DwdpaTKJOoPu"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/structured_data/imbalanced_data\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/structured_data/imbalanced_data.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mthoSGBAOoX-"
   },
   "source": [
    "This tutorial demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use [Keras](https://www.tensorflow.org/guide/keras/overview) to define the model and [class weights](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model) to help the model learn from the imbalanced data. .\n",
    "\n",
    "This tutorial contains complete code to:\n",
    "\n",
    "* Load a CSV file using Pandas.\n",
    "* Create train, validation, and test sets.\n",
    "* Define and train a model using Keras (including setting class weights).\n",
    "* Evaluate the model using various metrics (including precision and recall).\n",
    "* Select a threshold for a probabilistic classifier to get a deterministic classifier.\n",
    "* Try and compare with class weighted modelling and oversampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kRHmSyHxEIhN"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:28.885633Z",
     "iopub.status.busy": "2024-01-17T02:20:28.885208Z",
     "iopub.status.idle": "2024-01-17T02:20:32.051398Z",
     "shell.execute_reply": "2024-01-17T02:20:32.050564Z"
    },
    "id": "JM7hDSNClfoK"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-17 02:20:29.309180: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-01-17 02:20:29.309224: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-01-17 02:20:29.310677: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:32.055886Z",
     "iopub.status.busy": "2024-01-17T02:20:32.055261Z",
     "iopub.status.idle": "2024-01-17T02:20:32.059646Z",
     "shell.execute_reply": "2024-01-17T02:20:32.059041Z"
    },
    "id": "c8o1FHzD-_y_"
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (12, 10)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z3iZVjziKHmX"
   },
   "source": [
    "## Data processing and exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4sA9WOcmzH2D"
   },
   "source": [
    "### Download the Kaggle Credit Card Fraud data set\n",
    "\n",
    "Pandas is a Python library with many helpful utilities for loading and working with structured data. It can be used to download CSVs into a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n",
    "\n",
    "Note: This dataset has been collected and analysed during a research collaboration of Worldline and the [Machine Learning Group](http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available [here](https://www.researchgate.net/project/Fraud-detection-5) and the page of the [DefeatFraud](https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/) project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:32.062937Z",
     "iopub.status.busy": "2024-01-17T02:20:32.062680Z",
     "iopub.status.idle": "2024-01-17T02:20:35.202303Z",
     "shell.execute_reply": "2024-01-17T02:20:35.201635Z"
    },
    "id": "pR_SnbMArXr7"
   },
   "outputs": [
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       "      <th>0</th>\n",
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       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
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       "      <td>0.166480</td>\n",
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       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
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       "      <td>-0.137458</td>\n",
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       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
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       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = tf.keras.utils\n",
    "raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
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       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
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       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
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       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   9.604066e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    1.380247e+00  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.137433e+02 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -6.915971e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%   -5.433583e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    6.119264e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    3.480167e+01  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xWKB_CVZFLpB"
   },
   "source": [
    "### Examine the class label imbalance\n",
    "\n",
    "Let's look at the dataset imbalance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:35.342147Z",
     "iopub.status.busy": "2024-01-17T02:20:35.341905Z",
     "iopub.status.idle": "2024-01-17T02:20:35.346615Z",
     "shell.execute_reply": "2024-01-17T02:20:35.346002Z"
    },
    "id": "HCJFrtuY2iLF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples:\n",
      "    Total: 284807\n",
      "    Positive: 492 (0.17% of total)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "neg, pos = np.bincount(raw_df['Class'])\n",
    "total = neg + pos\n",
    "print('Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n'.format(\n",
    "    total, pos, 100 * pos / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KnLKFQDsCBUg"
   },
   "source": [
    "This shows the small fraction of positive samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6qox6ryyzwdr"
   },
   "source": [
    "### Clean, split and normalize the data\n",
    "\n",
    "The raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:35.349777Z",
     "iopub.status.busy": "2024-01-17T02:20:35.349555Z",
     "iopub.status.idle": "2024-01-17T02:20:35.373728Z",
     "shell.execute_reply": "2024-01-17T02:20:35.373159Z"
    },
    "id": "Ef42jTuxEjnj"
   },
   "outputs": [],
   "source": [
    "cleaned_df = raw_df.copy()\n",
    "\n",
    "# You don't want the `Time` column.\n",
    "cleaned_df.pop('Time')\n",
    "\n",
    "# The `Amount` column covers a huge range. Convert to log-space.\n",
    "eps = 0.001 # 0 => 0.1¢\n",
    "cleaned_df['Log Amount'] = np.log(cleaned_df.pop('Amount')+eps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uSNgdQFFFQ6u"
   },
   "source": [
    "Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where [overfitting](https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting) is a significant concern from the lack of training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:35.377322Z",
     "iopub.status.busy": "2024-01-17T02:20:35.377069Z",
     "iopub.status.idle": "2024-01-17T02:20:35.585152Z",
     "shell.execute_reply": "2024-01-17T02:20:35.584295Z"
    },
    "id": "xfxhKg7Yr1-b"
   },
   "outputs": [],
   "source": [
    "# Use a utility from sklearn to split and shuffle your dataset.\n",
    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.2)\n",
    "\n",
    "# Form np arrays of labels and features.\n",
    "train_labels = np.array(train_df.pop('Class'))\n",
    "bool_train_labels = train_labels != 0\n",
    "val_labels = np.array(val_df.pop('Class'))\n",
    "test_labels = np.array(test_df.pop('Class'))\n",
    "\n",
    "train_features = np.array(train_df)\n",
    "val_features = np.array(val_df)\n",
    "test_features = np.array(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8a_Z_kBmr7Oh"
   },
   "source": [
    "We check whether the distribution of the classes in the three sets is about the same or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:35.589102Z",
     "iopub.status.busy": "2024-01-17T02:20:35.588805Z",
     "iopub.status.idle": "2024-01-17T02:20:35.593113Z",
     "shell.execute_reply": "2024-01-17T02:20:35.592471Z"
    },
    "id": "96520cffee66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average class probability in training set:   0.0016\n",
      "Average class probability in validation set: 0.0018\n",
      "Average class probability in test set:       0.0019\n"
     ]
    }
   ],
   "source": [
    "print(f'Average class probability in training set:   {train_labels.mean():.4f}')\n",
    "print(f'Average class probability in validation set: {val_labels.mean():.4f}')\n",
    "print(f'Average class probability in test set:       {test_labels.mean():.4f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "8a_Z_kBmr7Oh"
   },
   "source": [
    "Given the small number of positive labels, this seems about right.\n",
    "\n",
    "Normalize the input features using the sklearn StandardScaler.\n",
    "This will set the mean to 0 and standard deviation to 1.\n",
    "\n",
    "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:35.596416Z",
     "iopub.status.busy": "2024-01-17T02:20:35.596192Z",
     "iopub.status.idle": "2024-01-17T02:20:35.713878Z",
     "shell.execute_reply": "2024-01-17T02:20:35.713145Z"
    },
    "id": "IO-qEUmJ5JQg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training labels shape: (182276,)\n",
      "Validation labels shape: (45569,)\n",
      "Test labels shape: (56962,)\n",
      "Training features shape: (182276, 29)\n",
      "Validation features shape: (45569, 29)\n",
      "Test features shape: (56962, 29)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "train_features = scaler.fit_transform(train_features)\n",
    "\n",
    "val_features = scaler.transform(val_features)\n",
    "test_features = scaler.transform(test_features)\n",
    "\n",
    "train_features = np.clip(train_features, -5, 5)\n",
    "val_features = np.clip(val_features, -5, 5)\n",
    "test_features = np.clip(test_features, -5, 5)\n",
    "\n",
    "\n",
    "print('Training labels shape:', train_labels.shape)\n",
    "print('Validation labels shape:', val_labels.shape)\n",
    "print('Test labels shape:', test_labels.shape)\n",
    "\n",
    "print('Training features shape:', train_features.shape)\n",
    "print('Validation features shape:', val_features.shape)\n",
    "print('Test features shape:', test_features.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XF2nNfWKJ33w"
   },
   "source": [
    "Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uQ7m9nqDC3W6"
   },
   "source": [
    "### Look at the data distribution\n",
    "\n",
    "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n",
    "\n",
    "* Do these distributions make sense? \n",
    "    * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n",
    "* Can you see the difference between the distributions?\n",
    "    * Yes the positive examples contain a much higher rate of extreme values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:35.717572Z",
     "iopub.status.busy": "2024-01-17T02:20:35.717322Z",
     "iopub.status.idle": "2024-01-17T02:20:37.608709Z",
     "shell.execute_reply": "2024-01-17T02:20:37.608002Z"
    },
    "id": "raK7hyjd_vf6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n",
    "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n",
    "\n",
    "sns.jointplot(x=pos_df['V5'], y=pos_df['V6'],\n",
    "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
    "plt.suptitle(\"Positive distribution\")\n",
    "\n",
    "sns.jointplot(x=neg_df['V5'], y=neg_df['V6'],\n",
    "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
    "_ = plt.suptitle(\"Negative distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qFK1u4JX16D8"
   },
   "source": [
    "## Define the model and metrics\n",
    "\n",
    "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:37.612591Z",
     "iopub.status.busy": "2024-01-17T02:20:37.612333Z",
     "iopub.status.idle": "2024-01-17T02:20:39.944901Z",
     "shell.execute_reply": "2024-01-17T02:20:39.944180Z"
    },
    "id": "3JQDzUqT3UYG"
   },
   "outputs": [],
   "source": [
    "METRICS = [\n",
    "      keras.metrics.BinaryCrossentropy(name='cross entropy'),  # same as model's loss\n",
    "      keras.metrics.MeanSquaredError(name='Brier score'),\n",
    "      keras.metrics.TruePositives(name='tp'),\n",
    "      keras.metrics.FalsePositives(name='fp'),\n",
    "      keras.metrics.TrueNegatives(name='tn'),\n",
    "      keras.metrics.FalseNegatives(name='fn'), \n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "      keras.metrics.Precision(name='precision'),\n",
    "      keras.metrics.Recall(name='recall'),\n",
    "      keras.metrics.AUC(name='auc'),\n",
    "      keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve\n",
    "]\n",
    "\n",
    "def make_model(metrics=METRICS, output_bias=None):\n",
    "  if output_bias is not None:\n",
    "    output_bias = tf.keras.initializers.Constant(output_bias)\n",
    "  model = keras.Sequential([\n",
    "      keras.layers.Dense(\n",
    "          16, activation='relu',\n",
    "          input_shape=(train_features.shape[-1],)),\n",
    "      keras.layers.Dropout(0.5),\n",
    "      keras.layers.Dense(1, activation='sigmoid',\n",
    "                         bias_initializer=output_bias),\n",
    "  ])\n",
    "\n",
    "  model.compile(\n",
    "      optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "      loss=keras.losses.BinaryCrossentropy(),\n",
    "      metrics=metrics)\n",
    "\n",
    "  return model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "SU0GX6E6mieP"
   },
   "source": [
    "### Understanding useful metrics\n",
    "\n",
    "Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.\n",
    "These can be divided into three groups.\n",
    "\n",
    "#### Metrics for probability predictions\n",
    "\n",
    "As we train our network with the cross entropy as a loss function, it is fully capable of predicting class probabilities, i.e., it is a probabilistic classifier.\n",
    "Good metrics to assess probabilistic predictions are, in fact, **proper scoring rules**. Their key property is that predicting the true probability is optimal. We give two well-known examples:\n",
    "\n",
    "*   **cross entropy** also known as log loss\n",
    "*   **Mean squared error** also known as the Brier score\n",
    "\n",
    "#### Metrics for deterministic 0/1 predictions\n",
    "\n",
    "In the end, one often wants to predict a class label, 0 or 1, *no fraud* or *fraud*.\n",
    "This is called a deterministic classifier.\n",
    "To get a label prediction from our probabilistic classifier, one needs to choose a probability threshold $t$.\n",
    "The default is to predict label 1 (fraud) if the predicted probability is larger than $t=50\\%$ and all the following metrics implicitly use this default. \n",
    "\n",
    "*   **False** negatives and **false** positives are samples that were **incorrectly** classified\n",
    "*   **True** negatives and **true** positives are samples that were **correctly** classified\n",
    "*   **Accuracy** is the percentage of examples correctly classified\n",
    ">   $\\frac{\\text{true samples}}{\\text{total samples}}$\n",
    "*   **Precision** is the percentage of **predicted** positives that were correctly classified\n",
    ">   $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$\n",
    "*   **Recall** is the percentage of **actual** positives that were correctly classified\n",
    ">   $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$\n",
    "\n",
    "**Note:** Accuracy is not a helpful metric for this task. You can have 99.8%+ accuracy on this task by predicting False all the time.  \n",
    "\n",
    "#### Other metrices\n",
    "\n",
    "The following metrics take into account all possible choices of thresholds $t$.\n",
    "\n",
    "*   **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than a random negative sample.\n",
    "*   **AUPRC** refers to Area Under the Curve of the Precision-Recall Curve. This metric computes precision-recall pairs for different probability thresholds. \n",
    "\n",
    "\n",
    "#### Read more:\n",
    "*   [Strictly Proper Scoring Rules, Prediction, and Estimation](https://www.stat.washington.edu/people/raftery/Research/PDF/Gneiting2007jasa.pdf)\n",
    "*   [True vs. False and Positive vs. Negative](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)\n",
    "*   [Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy)\n",
    "*   [Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n",
    "*   [ROC-AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)\n",
    "*   [Relationship between Precision-Recall and ROC Curves](https://www.biostat.wisc.edu/~page/rocpr.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FYdhSAoaF_TK"
   },
   "source": [
    "## Baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IDbltVPg2m2q"
   },
   "source": [
    "### Build the model\n",
    "\n",
    "Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.\n",
    "\n",
    "\n",
    "Note: Fitting this model will not handle the class imbalance efficiently. You will improve it later in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:39.948974Z",
     "iopub.status.busy": "2024-01-17T02:20:39.948714Z",
     "iopub.status.idle": "2024-01-17T02:20:39.952326Z",
     "shell.execute_reply": "2024-01-17T02:20:39.951743Z"
    },
    "id": "ouUkwPcGQsy3"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 100\n",
    "BATCH_SIZE = 2048\n",
    "\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_prc', \n",
    "    verbose=1,\n",
    "    patience=10,\n",
    "    mode='max',\n",
    "    restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:39.955314Z",
     "iopub.status.busy": "2024-01-17T02:20:39.955071Z",
     "iopub.status.idle": "2024-01-17T02:20:40.026024Z",
     "shell.execute_reply": "2024-01-17T02:20:40.025341Z"
    },
    "id": "1xlR_dekzw7C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Layer (type)                Output Shape              Param #   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dense (Dense)               (None, 16)                480       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dropout (Dropout)           (None, 16)                0         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " dense_1 (Dense)             (None, 1)                 17        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total params: 497 (1.94 KB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable params: 497 (1.94 KB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-trainable params: 0 (0.00 Byte)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = make_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wx7ND3_SqckO"
   },
   "source": [
    "Test run the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:40.032603Z",
     "iopub.status.busy": "2024-01-17T02:20:40.032359Z",
     "iopub.status.idle": "2024-01-17T02:20:40.555201Z",
     "shell.execute_reply": "2024-01-17T02:20:40.554518Z"
    },
    "id": "LopSd-yQqO3a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 471ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.16263928],\n",
       "       [0.35204744],\n",
       "       [0.19377157],\n",
       "       [0.72603256],\n",
       "       [0.30116165],\n",
       "       [0.25605297],\n",
       "       [0.66053736],\n",
       "       [0.31973222],\n",
       "       [0.25077152],\n",
       "       [0.26151225]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YKIgWqHms_03"
   },
   "source": [
    "### Optional: Set the correct initial bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qk_3Ry6EoYDq"
   },
   "source": [
    "These initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that, see [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines). This can help with initial convergence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PdbfWDuVpo6k"
   },
   "source": [
    "With the default bias initialization the loss should be about `math.log(2) = 0.69314` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:40.558832Z",
     "iopub.status.busy": "2024-01-17T02:20:40.558574Z",
     "iopub.status.idle": "2024-01-17T02:20:43.909213Z",
     "shell.execute_reply": "2024-01-17T02:20:43.908408Z"
    },
    "id": "H-oPqh3SoGXk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.4088\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
    "print(\"Loss: {:0.4f}\".format(results[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hE-JRzfKqfhB"
   },
   "source": [
    "The correct bias to set can be derived from:\n",
    "\n",
    "$$ p_0 = pos/(pos + neg) = 1/(1+e^{-b_0}) $$\n",
    "$$ b_0 = -log_e(1/p_0 - 1) $$\n",
    "$$ b_0 = log_e(pos/neg)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:43.912739Z",
     "iopub.status.busy": "2024-01-17T02:20:43.912447Z",
     "iopub.status.idle": "2024-01-17T02:20:43.917553Z",
     "shell.execute_reply": "2024-01-17T02:20:43.916912Z"
    },
    "id": "F5KWPSjjstUS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.35935934])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_bias = np.log([pos/neg])\n",
    "initial_bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d1juXI9yY1KD"
   },
   "source": [
    "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n",
    "\n",
    "It should be near: `pos/total = 0.0018`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:43.920916Z",
     "iopub.status.busy": "2024-01-17T02:20:43.920321Z",
     "iopub.status.idle": "2024-01-17T02:20:44.069335Z",
     "shell.execute_reply": "2024-01-17T02:20:44.068603Z"
    },
    "id": "50oyu1uss0i-"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "1/1 [==============================] - 0s 75ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.00135984],\n",
       "       [0.00134607],\n",
       "       [0.00213977],\n",
       "       [0.01406598],\n",
       "       [0.0021732 ],\n",
       "       [0.00640495],\n",
       "       [0.00814889],\n",
       "       [0.00254694],\n",
       "       [0.00572464],\n",
       "       [0.00216844]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = make_model(output_bias=initial_bias)\n",
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4xqFYb2KqRHQ"
   },
   "source": [
    "With this initialization the initial loss should be approximately:\n",
    "\n",
    "$$-p_0log(p_0)-(1-p_0)log(1-p_0) = 0.01317$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:44.072701Z",
     "iopub.status.busy": "2024-01-17T02:20:44.072144Z",
     "iopub.status.idle": "2024-01-17T02:20:44.896282Z",
     "shell.execute_reply": "2024-01-17T02:20:44.895458Z"
    },
    "id": "xVDqCWXDqHSc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0087\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
    "print(\"Loss: {:0.4f}\".format(results[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FrDC8hvNr9yw"
   },
   "source": [
    "This initial loss is about 50 times less than it would have been with naive initialization.\n",
    "\n",
    "This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. It also makes it easier to read plots of the loss during training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0EJj9ixKVBMT"
   },
   "source": [
    "### Checkpoint the initial weights\n",
    "\n",
    "To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:44.900430Z",
     "iopub.status.busy": "2024-01-17T02:20:44.899687Z",
     "iopub.status.idle": "2024-01-17T02:20:44.932129Z",
     "shell.execute_reply": "2024-01-17T02:20:44.931458Z"
    },
    "id": "_tSUm4yAVIif"
   },
   "outputs": [],
   "source": [
    "initial_weights = os.path.join(tempfile.mkdtemp(), 'initial_weights')\n",
    "model.save_weights(initial_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EVXiLyqyZ8AX"
   },
   "source": [
    "### Confirm that the bias fix helps\n",
    "\n",
    "Before moving on, confirm quick that the careful bias initialization actually helped.\n",
    "\n",
    "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:44.935620Z",
     "iopub.status.busy": "2024-01-17T02:20:44.935073Z",
     "iopub.status.idle": "2024-01-17T02:20:55.604812Z",
     "shell.execute_reply": "2024-01-17T02:20:55.604012Z"
    },
    "id": "Dm4-4K5RZ63Q"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1705458046.535087   10301 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    }
   ],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "model.layers[-1].bias.assign([0.0])\n",
    "zero_bias_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels), \n",
    "    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:20:55.609119Z",
     "iopub.status.busy": "2024-01-17T02:20:55.608522Z",
     "iopub.status.idle": "2024-01-17T02:21:06.070242Z",
     "shell.execute_reply": "2024-01-17T02:21:06.069439Z"
    },
    "id": "j8DsLXHQaSql"
   },
   "outputs": [],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "careful_bias_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels), \n",
    "    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:06.074799Z",
     "iopub.status.busy": "2024-01-17T02:21:06.074236Z",
     "iopub.status.idle": "2024-01-17T02:21:06.079018Z",
     "shell.execute_reply": "2024-01-17T02:21:06.078401Z"
    },
    "id": "E3XsMBjhauFV"
   },
   "outputs": [],
   "source": [
    "def plot_loss(history, label, n):\n",
    "  # Use a log scale on y-axis to show the wide range of values.\n",
    "  plt.semilogy(history.epoch, history.history['loss'],\n",
    "               color=colors[n], label='Train ' + label)\n",
    "  plt.semilogy(history.epoch, history.history['val_loss'],\n",
    "               color=colors[n], label='Val ' + label,\n",
    "               linestyle=\"--\")\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:06.082403Z",
     "iopub.status.busy": "2024-01-17T02:21:06.081933Z",
     "iopub.status.idle": "2024-01-17T02:21:06.476072Z",
     "shell.execute_reply": "2024-01-17T02:21:06.475377Z"
    },
    "id": "dxFaskm7beC7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mTpyoW265RWvWrKn1+zFu3Djl5eXp008/laurq/3xa6+9VllZWfrxxx+1fv16de/eXUOHDlV29p8dY8+ePfr888/1xRdfKDExUVarVaNHj1Z2draWLVumhQsXat++fbr++utrlCkpKUm//PKL+vTpc8pjCgoKdNttt2n58uVavXq1YmNjddlll6mgoECS9Pnnn+s///mP3njjDe3evVtfffWVunTpUsN3p/5wPfMhqA/C/D11fc9Ivbf6gF75Zbf6tWFaCgAAAPCHjk/8fMrnLmrXRPPG9bbf7/HMIpX8pbj/oU+rYH18Vz/7/QHPL1F2UfVp58n/d3mNst1xxx164YUXtGzZMg0ePFhS1VT0q6++WgEBAQoICNCDDz5oP37KlCn6+eef9cknn6h3796nOOupPffcc/r++++1YsUKhYaG2h9fvny51qxZo6ysLHl4eEiSXnzxRX311Vf67LPPNHHiRElV0+zfffddNWnSRJK0cOFCbdmyRfv371dkZKQk6d1331WnTp20du1a9erV65RZxo4dKxcXF1VWVqqsrEwjR47Uo48+esrjhwwZUu3+nDlzFBgYqGXLlmnkyJFKSUlReHi4hg0bJjc3N0VFRdXqPaovGKFvQCYNbiM3F5NW7j2q9QcYpQcAAADqg/bt2+uCCy7Q22+/LalqBPy3337T+PHjJUkWi0XPPPOMunTpouDgYPn6+urnn39WSkpKjV/rhx9+0OOPP6558+YpLi6u2nObNm1SYWGhQkJC5Ovra7/t379fe/futR/XsmVLe5mXpB07digyMtJe5iWpY8eOCgwM1I4dO06b5z//+Y8SExO1adMmfffdd0pKStItt9xyyuMzMzN15513KjY2VgEBAfL391dhYaH9vbj22mtVUlKi1q1b684779SXX36pysqazZqoTxihb0CaB3rp6u4t9NHaVM1evEfv3NFwP4kCAAAAamL708NP+Zz5L6u8r3982Fkfu/zhi84t2DHjx4/XlClTlJCQoHnz5qlNmzYaNGiQJOmFF17Qyy+/rFmzZqlLly7y8fHRtGnTarwgXVJSkm688UY98sgjuvbaa094vrCwUM2aNdPSpUtPeC4wMND+dx8fnxq97umEh4crJiZGktSuXTsVFBRo7NixmjFjhv3x49122206evSoXn75ZbVs2VIeHh7q16+f/b2IjIzUrl27tGjRIi1cuFD33HOPffaDm5ubw3I7Cwp9A3PP4Bh9uv6gliUdVmJqruIjA42OBAAAABjO2/3sq09dHXs61113ne677z598MEHevfdd3X33Xfbt5NbsWKFRo8erZtvvlmSZLValZSUpI4dO571+fPz8zV69GhdeOGFeuaZZ056TPfu3ZWRkSFXV1dFR0ef9bk7dOig1NRUpaam2kfpt2/frtzc3BpllCQXFxdJUklJyUmfX7FihV577TVddtllkqTU1FQdOVJ9py8vLy+NGjVKo0aN0uTJk9W+fXtt2bJF3bt3r1GW+oAp9yeRkJCgjh07nvZaD2cVFeKt0fERkqRXf9ltcBoAAAAAZ8PX11fXX3+9Hn30UaWnp+v222+3PxcbG6uFCxdq5cqV2rFjh+66666zWkH+DzabTTfddJOKi4s1c+ZMZWZmKiMjo9rNYrFo2LBh6tevn6688kotWLBAycnJWrlypR577DGtW7fulOcfNmyYunTpoptuukkbNmzQmjVrdOutt2rQoEHq2bPnabPl5uYqIyNDaWlpWrZsmZ5++mm1bdtWHTp0OOnxsbGxeu+997Rjxw79/vvvuummm+Tl5WV/fv78+Xrrrbe0detW7du3T//73//k5eWlli1bnvX7VZ9Q6E9i8uTJ2r59u9auXWt0lFqZfFGMzCZp0Y4sbUvLMzoOAAAAgLMwfvx45eTkaPjw4YqIiLA//s9//lPdu3fX8OHDNXjwYIWHh+vKK6886/OmpKTou+++U0pKitq2batmzZqdcEtNTZXJZNIPP/ygCy+8UOPGjVPbtm11ww036MCBAwoLCzvl+U0mk77++msFBQXpwgsv1LBhw9S6dWt9/PHHZ8w2btw4NWvWTC1atNDYsWPVqVMn/fjjj9VW3j/eW2+9pZycHHXv3l233HKLpk6dqqZNm9qfDwwM1Ny5c9W/f3917dpVixYt0rfffttg97I32di0/JTy8/MVEBCgvLw8+fv7Gx2nRqZ+uFHfbErTpZ3D9d+bexgdBwAAAKhzpaWl2r9/f7X90QFndaqf15r0UEboG6h7h1QtIPHj1gwlZRYYnAYAAAAA4GgU+gaqbZifLu0cLkl69Zc9BqcBAAAAADgahb4B+2OU/rvNadp7uNDgNAAAAAAAR6LQN2CdIgI0rENTWW1SwhJG6QEAAACgIaHQN3BThsRKkr5OTFPK0WKD0wAAAAAAHIVC38DFRQbqwrZNZLHa9NpSRukBAAAAoKGg0DcC9w2tupb+8w0HdSi3xOA0AAAAAABHoNA3Aj1aBuuCNiGqsNj0+tK9RscBAAAAADgAhb6R+ONa+o/XpSozv9TgNAAAAACAc0WhbyT6tg5Wr+gglVda9cayfUbHAQAAAFCHoqOjNWvWLKNjnLM5c+YoMjJSZrP5rL+fwYMHa9q0aef82vXhPaTQNxImk8k+Sv/+7wd0uKDM4EQAAAAATCbTaW/Tp0+v1XnXrl2riRMnnnO+PXv2aNy4cWrRooU8PDzUqlUrjR07VuvWrTvnc59Jfn6+7r33Xj388MM6dOiQQ74fSZo/f36199jX11c9evTQF198Ue04R72HdYlC34gMjA1VXGSgyiqtevM3RukBAAAAo6Wnp9tvs2bNkr+/f7XHHnzwQfuxNptNlZWVZ3XeJk2ayNvb+5yyrVu3Tj169FBSUpLeeOMNbd++XV9++aXat2+vBx54oNbnLS8vP6vjUlJSVFFRocsvv1zNmjU75+/neMe/zxs3btTw4cN13XXXadeuXfZjHPEe1jUKfSNiMpk0dUjVivfvrT6g7KKz+4cEAAAAoG6Eh4fbbwEBATKZTPb7O3fulJ+fn3788Uf16NFDHh4eWr58ufbu3avRo0crLCxMvr6+6tWrlxYtWlTtvH+dLm4ymfTmm29qzJgx8vb2VmxsrL755ptT5rLZbLr99tsVGxur3377TZdffrnatGmj+Ph4Pfnkk/r666/txz788MNq27atvL291bp1az3++OOqqKiwPz99+nTFx8frzTffVKtWreTp6SlJys3N1YQJE9SkSRP5+/tryJAh2rRpk6SqUfQuXbpIklq3bi2TyaTk5GTdfvvtuvLKK6tlnTZtmgYPHlyj9/349zk2NlYzZsyQ2WzW5s2bT/kevvTSS+rSpYt8fHwUGRmpe+65R4WFhfbnDxw4oFGjRikoKEg+Pj7q1KmTfvjhhxrlqikK/UkkJCSoY8eO6tWrl9FRHG5I+6bqFOGv4nKL3l6+3+g4AAAAQN2y2aTyovN/s9kc9i088sgj+r//+z/t2LFDXbt2VWFhoS677DItXrxYGzdu1IgRIzRq1CilpKSc9jxPPfWUrrvuOm3evFmXXXaZbrrpJmVnZ5/02MTERG3btk0PPPCAzOYTa2NgYKD9735+fpo/f762b9+ul19+WXPnztV//vOfasfv2bNHn3/+ub744gslJiZKkq699lplZWXpxx9/1Pr169W9e3cNHTpU2dnZuv766+0fUqxZs0bp6emKjIyswbt29iwWi9555x1JUvfu3U95nNls1uzZs7Vt2za98847+uWXX/TQQw/Zn588ebLKysr066+/asuWLXr++efl6+tbJ5n/4FqnZ6+nJk+erMmTJys/P18BAQFGx3GoP66ln/S/9XpnZbLuvLC1ArzcjI4FAAAA1I2KYulfEef/df+RJrn7OORUTz/9tC6++GL7/eDgYMXFxdnvP/PMM/ryyy/1zTff6N577z3leW6//XaNHTtWkvSvf/1Ls2fP1po1azRixIgTjt29e7ckqX379mfM989//tP+9+joaD344IP66KOPqpXd8vJyvfvuu2rSpIkkafny5VqzZo2ysrLk4eEhSXrxxRf11Vdf6bPPPtPEiRMVEhIiqWrqe3h4+Blz1EReXp69bJeUlMjNzU1z5sxRmzZtTvk1xy+0Fx0drRkzZmjSpEl67bXXJFVdInD11VdXm1lQ1yj0jdAlHcPULsxPuzILNH9Fsu4bFmt0JAAAAACn0LNnz2r3CwsLNX36dH3//fdKT09XZWWlSkpKzjhC37VrV/vffXx85O/vr6ysrJMea6vBDIOPP/5Ys2fP1t69e1VYWKjKykr5+/tXO6Zly5b2Mi9JmzZtUmFhob20/6GkpER79+4969euLT8/P23YsEGSVFxcrEWLFmnSpEkKCQnRqFGjTvo1ixYt0nPPPaedO3cqPz9flZWVKi0tVXFxsby9vTV16lTdfffdWrBggYYNG6arr7662nteFyj0jZDZbNK9Q2I05cONenvFft0xIFp+nozSAwAAoAFy864aLTfidR3Ex6f6SP+DDz6ohQsX6sUXX1RMTIy8vLx0zTXXnHGxOTe36r/zm0wmWa3Wkx7btm1bSdLOnTvVrVu3U55z1apVuummm/TUU09p+PDhCggI0EcffaSZM2ee9nsoLCxUs2bNtHTp0hPOefx0/r8ym80nfNhw/PX6Z8tsNismJsZ+v2vXrlqwYIGef/75kxb65ORkjRw5UnfffbeeffZZBQcHa/ny5Ro/frzKy8vl7e2tCRMmaPjw4fr++++1YMECPffcc5o5c6amTJlS43xni0LfSF3WpZn+syhJ+w4X6d1VBzT5opgzfxEAAABQ35hMDpv67ixWrFih22+/XWPGjJFUVY6Tk5Md+hrx8fHq2LGjZs6cqeuvv/6E6+hzc3MVGBiolStXqmXLlnrsscfszx04cOCM5+/evbsyMjLk6uqq6Ojos87VpEkTbd26tdpjiYmJJ3xYURsuLi4qKSk56XPr16+X1WrVzJkz7e/FJ598csJxkZGRmjRpkiZNmqRHH31Uc+fOrdNCz6J4jZSL2aR7j5X4t5bvV3H52W1/AQAAAMBYsbGx9sXlNm3apBtvvPGUI+21ZTKZNG/ePCUlJWngwIH64YcftG/fPm3evFnPPvusRo8ebc+SkpKijz76SHv37tXs2bP15ZdfnvH8w4YNU79+/XTllVdqwYIFSk5O1sqVK/XYY4+ddo/7IUOGaN26dXr33Xe1e/duPfnkkycU/LNhs9mUkZGhjIwM7d+/X3PmzNHPP/9s/77+KiYmRhUVFXrllVe0b98+vffee3r99derHTNt2jT9/PPP2r9/vzZs2KAlS5aoQ4cONc5WExT6RuyKuAi1DPFWdlG53l99+uttAAAAADiHl156SUFBQbrgggs0atQoDR8+/LSrs9dW7969tW7dOsXExOjOO+9Uhw4ddMUVV2jbtm327dyuuOIK/e1vf9O9996r+Ph4rVy5Uo8//vgZz20ymfTDDz/owgsv1Lhx49S2bVvdcMMNOnDggMLCwk75dcOHD9fjjz+uhx56SL169VJBQYFuvfXWGn9v+fn5atasmZo1a6YOHTpo5syZevrpp6vNNDheXFycXnrpJT3//PPq3Lmz3n//fT333HPVjrFYLJo8ebI6dOigESNGqG3btvYF8+qKyVaT1Q4amT9Wuc/LyzthUYeG4pO1qXro880K9fXQ8ocvkqebi9GRAAAAgFopLS3V/v37q+11DjirU/281qSHMkLfyI3p3lzNA710pLBMH61hlB4AAAAA6gsKfSPn5mLW3YOr9lp8fdk+lVVaDE4EAAAAADgbFHro2p4tFO7vqYz8Un22/qDRcQAAAAAAZ4FCD3m4uuiuQa0lSa8t2asKi2NXyAQAAAAAOB6FHpKksb2jFOrroUO5JfpywyGj4wAAAAAAzoBCD0mSp5uLJl7YSpKUsHSPKhmlBwAAAACnRqGH3U19WirI200Hjhbr281pRscBAAAAAJwGhR52Ph6umjCw6lr6V3/ZI4vVZnAiAAAAAMCpUOhRza39WirAy017Dxfpx63pRscBAAAAAJwChR7V+Hm6aVz/aElVo/RWRukBAAAApzd48GBNmzbN6BintXPnTvXt21eenp6Kj48/q6+ZP3++AgMDz/m1p0+fftavWZ9Q6HGCcRe0kq+Hq3ZmFGjB9kyj4wAAAAAN1qhRozRixIiTPvfbb7/JZDJp8+bNDnmt8vJy/fvf/1ZcXJy8vb0VGhqq/v37a968eaqoqHDIa5zOk08+KR8fH+3atUuLFy922HlNJpP95urqqqioKN1///0qKyuzH/Pggw869DWdBYX+JBISEtSxY0f16tXL6CiGCPB2020XtJQkvfLLbtlsjNIDAAAAdWH8+PFauHChDh48eMJz8+bNU8+ePdW1a9dzfp3y8nINHz5c//d//6eJEydq5cqVWrNmjSZPnqxXXnlF27Ztq/W5z/bDgL1792rAgAFq2bKlQkJCav16JzNv3jylp6dr//79eu211/Tee+9pxowZ9ud9fX0d/prOgEJ/EpMnT9b27du1du1ao6MYZvyA1vJ2d9G2tHwt2ZVldBwAAACgQRo5cqSaNGmi+fPnV3u8sLBQn376qcaPH6+jR49q7Nixat68uby9vdWlSxd9+OGHNXqdWbNm6ddff9XixYs1efJkxcfHq3Xr1rrxxhv1+++/KzY2VpL0008/acCAAQoMDFRISIhGjhypvXv32s+TnJwsk8mkjz/+WIMGDZKnp6fef/99SdKbb76pDh06yNPTU+3bt9drr71m/zqTyaT169fr6aeflslk0vTp07V06VKZTCbl5ubaj0tMTJTJZFJycnKNvr/AwECFh4crMjJSI0eO1OjRo7Vhwwb783+dcr927VpdfPHFCg0NVUBAgAYNGlTteJvNpunTpysqKkoeHh6KiIjQ1KlTa5TpfKDQ46SCfdx1c9+qUfrZi/cwSg8AAID6rbzo1LeK0hocW3LmY2vA1dVVt956q+bPn1/td+5PP/1UFotFY8eOVWlpqXr06KHvv/9eW7du1cSJE3XLLbdozZo1Z/0677//voYNG6Zu3bqd8Jybm5t8fHwkSUVFRbr//vu1bt06LV68WGazWWPGjJHVaq32NY888ojuu+8+7dixQ8OHD9f777+vJ554Qs8++6x27Nihf/3rX3r88cf1zjvvSJLS09PVqVMnPfDAA0pPT9eDDz5Yo/epJpKSkvTLL7+oT58+pzymoKBAt912m5YvX67Vq1crNjZWl112mQoKCiRJn3/+uf7zn//ojTfe0O7du/XVV1+pS5cudZa5tlyNDgDndefA1npnZbISU3O1fM8RDYxtYnQkAAAAoHb+FXHq52IvkW769M/7L8RIFcUnP7blAGnc93/en9VFKj5a/ZjpeTWKdscdd+iFF17QsmXLNHjwYElVU8ivvvpqBQQEKCAgoFoBnjJlin7++Wd98skn6t2791m9xu7du+3nPp2rr7662v23335bTZo00fbt29W5c2f749OmTdNVV11lv//kk09q5syZ9sdatWql7du364033tBtt92m8PBwubq6ytfXV+Hh4WeVuSbGjh0rFxcXVVZWqqysTCNHjtSjjz56yuOHDBlS7f6cOXMUGBioZcuWaeTIkUpJSVF4eLiGDRsmNzc3RUVFnfV7fT4xQo9TauLnoRv7REmSXlm8x+A0AAAAQMPUvn17XXDBBXr77bclSXv27NFvv/2m8ePHS5IsFoueeeYZdenSRcHBwfL19dXPP/+slJSUs36Ns51xu3v3bo0dO1atW7eWv7+/oqOjJemE1+rZs6f970VFRdq7d6/Gjx8vX19f+23GjBnVpuvXpf/85z9KTEzUpk2b9N133ykpKUm33HLLKY/PzMzUnXfeqdjYWAUEBMjf31+FhYX27/Paa69VSUmJWrdurTvvvFNffvmlKisrz8v3UhOM0OO07rqwjd5fnaI1ydlave+o+rZueAtJAAAAoBH4R9qpnzO5VL//99MMZpn+MiY6bUvtMx1n/PjxmjJlihISEjRv3jy1adNGgwYNkiS98MILevnllzVr1ix16dJFPj4+mjZtmsrLy8/6/G3bttXOnTvPeNyoUaPUsmVLzZ07VxEREbJarercufMJr/XHFH2p6np/SZo7d+4J09xdXP7y3h7HbK56L4//sKG2q+2Hh4crJiZGktSuXTsVFBRo7NixmjFjhv3x49122206evSoXn75ZbVs2VIeHh7q16+f/fuMjIzUrl27tGjRIi1cuFD33HOPfRaFm5tbrTLWBUbocVrhAZ66rlcLSdLsxbsNTgMAAADUkrvPqW9unjU41uvMx9bCddddJ7PZrA8++EDvvvuu7rjjDplMJknSihUrNHr0aN18882Ki4tT69atlZSUVKPz33jjjVq0aJE2btx4wnMVFRUqKirS0aNHtWvXLv3zn//U0KFD1aFDB+Xk5Jzx3GFhYYqIiNC+ffsUExNT7daqVatTfl2TJlWX9Kanp9sfS0xMrNH3dSp/fJBQUlJy0udXrFihqVOn6rLLLlOnTp3k4eGhI0eOVDvGy8tLo0aN0uzZs7V06VKtWrVKW7Y45gMcR2GEHmc0aVAbfbQmVSv3HtX6A9nq0TLY6EgAAABAg+Lr66vrr79ejz76qPLz83X77bfbn4uNjdVnn32mlStXKigoSC+99JIyMzPVsWPHsz7/tGnT9P3332vo0KF65plnNGDAAPn5+WndunV6/vnn9dZbb6lr164KCQnRnDlz1KxZM6WkpOiRRx45q/M/9dRTmjp1qgICAjRixAiVlZVp3bp1ysnJ0f3333/Sr4mJiVFkZKSmT5+uZ599VklJSZo5c+ZZf0/Hy83NVUZGhqxWq3bv3q2nn35abdu2VYcOHU56fGxsrN577z317NlT+fn5+vvf/y4vrz8/rJk/f74sFov69Okjb29v/e9//5OXl5datmxZq3x1hRF6nFGLIG9d3f2PUXqupQcAAADqwvjx45WTk6Phw4crIuLPRfz++c9/qnv37ho+fLgGDx6s8PBwXXnllTU6t4eHhxYuXKiHHnpIb7zxhvr27atevXpp9uzZmjp1qjp37iyz2ayPPvpI69evV+fOnfW3v/1NL7zwwlmdf8KECXrzzTc1b948denSRYMGDdL8+fNPO0Lv5uamDz/8UDt37lTXrl31/PPPV9s7vibGjRunZs2aqUWLFho7dqw6deqkH3/8Ua6uJx/Dfuutt5STk6Pu3bvrlltu0dSpU9W0aVP784GBgZo7d6769++vrl27atGiRfr222+dbi97k439yE4pPz9fAQEBysvLk7+/v9FxDHXgaJGGzFwmi9Wmryf3V1xkoNGRAAAAgGpKS0u1f/9+tWrVSp6enmf+AsBAp/p5rUkPZYQeZ6VliI9Gx1V9SvjKL4zSAwAAAIDRKPQ4a5OHxMhkkhbtyNT2tHyj4wAAAABAo0ahx1lr08RXI7tWjdK/uoQV7wEAAADASBR61Mi9F1Xt4fjDlgwlZRYYnAYAAAAAGi8KPWqkXbifRnQKlyS9yrX0AAAAAGAYCj1q7N4hVaP0321O077DhQanAQAAAKpjIy/UB474OaXQo8Y6Nw/Q0PZNZbVJCUv2Gh0HAAAAkFS1r7kkFRcXG5wEOLM/fk7/+LmtDVdHhUHjMmVorBbvzNJXiYd039BYRYV4Gx0JAAAAjZyLi4sCAwOVlZUlSfL29pbJZDI4FVCdzWZTcXGxsrKyFBgYKBcXl1qfi0KPWomPDNSFbZvo16TD+u+yPXruqq5GRwIAAAAUHl613tMfpR5wVoGBgfaf19qi0KPWpg6J0a9Jh/XZ+oO6d0ismgd6GR0JAAAAjZzJZFKzZs3UtGlTVVRUGB0HOCk3N7dzGpn/A4UetdYzOlj9Wodo1b6jen3pXj1zZWejIwEAAACSqqbfO6IwAc6MRfFwTqYMrVrx/uN1qcrMLzU4DQAAAAA0HhT6k0hISFDHjh3Vq1cvo6M4vX6tQ9SzZZDKK616Y9k+o+MAAAAAQKNhsrFJ4ynl5+crICBAeXl58vf3NzqO01qWdFi3vb1Gnm5mLX94iEJ9PYyOBAAAAAD1Uk16KCP0OGcXxoYqrkWASiusmvsbo/QAAAAAcD5Q6HHOTCaTpg6NlSS9t+qAcorKDU4EAAAAAA0fhR4OMaR9U3WK8FdxuUVvr9hvdBwAAAAAaPAo9HAIk8mkKUOqVryfvyJZeSXs+QkAAAAAdYlCD4e5pGO42oX5qaCsUvNXJBsdBwAAAAAaNAo9HMZsNmnysVH6t1fsV0Epo/QAAAAAUFco9HCoy7s0U+smPsorqdB7qw8YHQcAAAAAGiwKfQOxO7NAMxfsks1mMzSHi9mkyYOrRunf/G2/issrDc0DAAAAAA0Vhb4BKCyr1OiEFXrllz36dfcRo+NodHyEooK9lV1Urg9+TzE6DgAAAAA0SBT6BsDXw1Vje0dJkl5elGT4KL2ri1mTL2ojSXrj130qrbAYmgcAAAAAGiIKfQNx14Wt5eFq1oaUXC3fY/wo/ZhuLdQ80EuHC8r00RpG6QEAAADA0Sj0DURTf8/jRul3Gz5K7+5q1qTBVaP0ry/bp7JKRukBAAAAwJEo9A3I3YPbyN3VrHUHcrRq71Gj4+jaHi0U5u+hjPxSfbb+oNFxAAAAAKBBodA3IGH+nhrbK1KSNGvxboPTSJ5uLrrrwqpR+v8u3asKi9XgRAAAAADQcFDoG5hJg9so3N9Tg9s1kcVq7LR7SRrbO0qhvu46mFOiLzceMjoOAAAAADQYFPoGplmAl5Y/fJHuGRwjF7PJ6DjycnfRnQNbS5JeW7JHlYzSAwAAAIBDUOgbIFcX5/qf9ea+LRXk7abko8X6bnO60XEAAAAAoEFwruYHh7HZbFqwLUPTv9lmdBT5eLhqwrFR+ld+2e0UlwIAAAAAQH1HoW+gMvJLNfmDDZq/MlnrkrONjqNb+7WUv6er9h4u0o9bGaUHAAAAgHNFoW+gmgV46ZoeLSRJLzvBivd+nm4a17+VJOnVX/bIyig9AAAAAJwTCn0Dds/gGLmaTfpt9xFtSMkxOo7u6N9Kvh6u2plRoIU7Mo2OAwAAAAD1GoW+AYsM9tZV3ZtLkl5eZPwofYC3m27t11JS1bX0Nhuj9AAAAABQWxT6Bu7ei2LlYjZpWdJhJabmGh1H4we0kpebi7YeytfSXYeNjgMAAAAA9RaFvoGLCvHWmG5/jNInGZxGCvH10M19oyRVXdvPKD0AAAAA1A6FvhG496IYxbUI0A29o4yOIkm688LW8nA1KzE1V9+yLz0AAAAA1AqF/iQSEhLUsWNH9erVy+goDhEd6qOv7x2g4Z3CjY4iSWrq56m7Lqzal/6xL7YoNbvY4EQAAAAAUP+YbMx5PqX8/HwFBAQoLy9P/v7+RsdpUCosVl3/xiptSMlVfGSgPp3UT24ufL4EAAAAoHGrSQ+lQTUiRWWV+u/SvXr4s81GR5Gbi1kv39BNfp6uSkzN1UsLjb++HwAAAADqEwp9I5KeV6J//7xTH69L1ba0PKPjKDLYW89f3VWS9PqyvVq++4jBiQAAAACg/qDQNyIxTf00smuEJGn2YuP3pZeky7o009jeUbLZpL99kqgjhWVGRwIAAACAeoFC38hMHRIjk0n6eVumdqTnGx1HkvTEyI5qG+arwwVleuCTTbJaWdYBAAAAAM6EQt/IxIb56bIuzSRJr/ziHKP0Xu4uemVsd3m4mrUs6bDeXrHf6EgAAAAA4PQo9I3Q1CGxkqQftmRoV0aBwWmqtAv30z9HdpQkPf/TTm05aPw1/gAAAADgzCj0jVC7cD9d1qVqT/rZTjJKL0k394nS8E5hqrDYNOXDDSosqzQ6EgAAAAA4LQp9IzV1aKxGxUXovqGxRkexM5lMev7qrooI8FTy0WI9/tVWoyMBAAAAgNOi0DdS7cP99crYbmob5md0lGoCvd318thuMpukLzce0ufrDxodCQAAAACcEoUekiSbzXlWlu8VHaxpw9pKkh7/eqv2HS40OBEAAAAAOB8KfSN3MKdYD366SX//bLPRUaqZfFGM+rYOVnG5RVM+3KiySovRkQAAAADAqVDoG7nc4gp9tv6gvthw0KlGwl3MJs26vpuCvN20LS1fz/+4y+hIAAAAAOBUKPSNXOfmARrWoamsNunVX/YYHaea8ABPvXBNnCTp7RX79cvOTIMTAQAAAIDzoNBDU4+tdP9V4iElHykyOE11wzqG6fYLoiVJD366WZn5pcYGAgAAAAAnQaGHurYI1EXtmlSN0i9xrlF6SXrk0vbq0Mxf2UXl+tvHibJYnWcBPwAAAAAwCoUekqT7jq0q/+XGQzpw1LlG6T3dXPTqjd3k5eailXuP6vVle42OBAAAAACGo9BDkhQfGahBbZvIYrVpzq/7jI5zgjZNfPXU6E6SpJcWJmn9gWyDEwEAAACAsSj0sPvbxW1139BYPTS8vdFRTuraHi10RVyELFabpn6YqLySCqMjAQAAAIBhKPSwi48M1N8ubqsAbzejo5yUyWTSs2M6KyrYW4dyS/ToF5tls3E9PQAAAIDGiUKPk7LZbCqtsBgd4wR+nm6aPbabXM0m/bAlQx+uSTU6EgAAAAAYgkKPE2xMydHV/12pp7/bbnSUk4qPDNTfh7eTJD317TYlZRYYnAgAAAAAzj8KPU5QXmnVhpRcfbouVYdyS4yOc1J3DmytC9s2UVmlVVM+2OiUswkAAAAAoC5R6HGCPq1D1Ld1sCosNr2+1Dm3iDObTZp5bZxCfd21K7NAM753ztkEAAAAAFBXKPQ4qfuGVu1L//HaVKXnOecofRM/D710Xbwk6X+rU/TT1nRjAwEAAADAeUShx0n1axOi3q2CVW6xOu0ovSRd2LaJ7rqwtSTpoc82O+0lAgAAAADgaBR6nNK0obGSpA/Xpiozv9TgNKf2wCXtFNciQPmllbrvw42qtFiNjgQAAAAAdY5Cj1Pq1yZEPVsGqbzSqo+ceHs4d1ezXhnbXb4erlp3IEezF+82OhIAAAAA1DkKPU7JZDLpkUvba/bYbrp3SIzRcU4rKsRbz47pLEl6Zckerdp71OBEAAAAAFC3KPQ4rZ7RwboiLkIuZpPRUc5odHxzXdujhWw2adrHG5VdVG50JAAAAACoMxR6nLXSCosKSiuMjnFaT43upNZNfJSZX6aHPtskm81mdCQAAAAAqBMUepyVH7ak68J/L3H669O93V31ythucncxa9GOLL2zMtnoSAAAAABQJyj0OCte7i7KKijTe6sP6EhhmdFxTqtTRID+cVl7SdK/ftipbWl5BicCAAAAAMej0OOsDG7bRF1bBKi0wqq5v+4zOs4Z3XZBtIZ1aKpyi1VTPtyo4vJKoyMBAAAAgENR6HFWTCaT7ju2L/27qw7oqJOP0ptMJv37mjiF+Xto3+EiPfn1NqMjAQAAAIBDUehPIiEhQR07dlSvXr2MjuJUhrRvqi7NA1RSYdGby/cbHeeMgn3cNev6bjKZpE/XH9TXiYeMjgQAAAAADkOhP4nJkydr+/btWrt2rdFRnIrJZNLUP0bpVyYrpx5sC9evTYimXBQjSXrsy606cLTI4EQAAAAA4BgUetTIsA5N1SnCX0XlFv2yM8voOGdl6tBY9YoOUmFZpaZ+uFHllVajIwEAAADAOaPQo0ZMJpOeHt1Z300ZoKt7tDA6zllxdTFr1g3dFODlpk0H8zRz4S6jIwEAAADAOaPQo8Z6tAxS5+YBRseokeaBXnr+6q6SpDeW7dOvSYcNTgQAAAAA54ZCj3OSllui/NIKo2OclRGdw3Vz3yhJ0v2fbNLhAudeqR8AAAAATodCj1p7fdleDX5hqd6uByve/+Gfl3dU+3A/HSks0/2fJMpqtRkdCQAAAABqhUKPWmsR5KVyi1VvL99fb0bpPd1c9MrYbvJ0M+u33Uc097d9RkcCAAAAgFqh0KPWLuvcTLFNfZVfWqn5K5KNjnPWYsP89MTITpKkF37epcTUXGMDAQAAAEAtUOhRa2azSVOO7Uv/1vL9Kqgno/SSNLZ3pC7v0kyVVpumfrixXmUHAAAAAIlCj3N0eZdmatPER3klFXpnZbLRcc6ayWTSv67qouaBXkrJLtZjX26Vzcb19AAAAADqDwo9zomL2aQpQ6pG6d9cvl+FZZUGJzp7AV5umj22m1zMJn2zKU2frT9odCQAAAAAOGsUepyzUXERah3qo7IKqzYfzDU6To30aBmk+y9uK0l64utt2nu40OBEAAAAAHB2KPQ4Zy5mk16+oZuWP3yRLmgTanScGps0qI0uaBOikgqLpnywUWWVFqMjAQAAAMAZUejhEF1aBCjE18PoGLXiYjbpP9fHK9jHXdvT8/XcDzuNjgQAAAAAZ0Shh8Ot2Z+t4vL6cy29JIX5e2rmtXGSpPkrk7Voe6bBiQAAAADg9Cj0cKi/f7pJ172xSv9bfcDoKDV2UfumGj+glSTp759tUkZeqcGJAAAAAODUKPRwqF7RwZKkOb/uU0l5/bsW/aER7dQpwl85xRW676ONsljZyg4AAACAc6LQw6HGdG+uyGAvHSks1/u/179Reg9XF70ytpu83V30+/5svbZkj9GRAAAAAOCkKPRwKDcXsyYPjpEkvfHrPpVW1L9R+tZNfPXM6M6SpFmLd2tdcrbBiQAAAADgRBR6ONxV3VuoeaCXDheU6YPfU4yOUytX92ihMd2ay2K16b6PEpVXXGF0JAAAAACohkIPh3N3NWvyRVWj9K8v21svR+kl6ZkrOys6xFuHckv08OebZbNxPT0AAAAA50GhR524pkcLRQR4ytPNRanZxUbHqRVfD1e9Mra73FxM+mlbht6vp7MNAAAAADRMFHrUCXdXs96b0EeLHxik2DA/o+PUWpcWAXp4RHtJ0jPfbdeujAKDEwEAAABAFQo96kybJr5yc6n/P2J39G+lwe2aqKzSqjvmr9WBo0VGRwIAAAAACj3qXnmlVZ+tP6iyyvp5Lb3ZbNLMa+PUOtRHh3JLdN0bq7Qnq9DoWAAAAAAaOQo96tyNc1frwU836dN1B42OUmshvh766K6+ahvmq8z8Mt0wZ5V2ZuQbHQsAAABAI0ahR50b2bWZJOm/S/eqvNJqcJraa+rnqY8m9lOnCH8dKSzXDXNWa+uhPKNjAQAAAGikKPSoczf0jlITPw8dyi3R5xvq7yi9JAX7uOuDCX0VHxmo3OIKjZ27WhtScoyOBQAAAKARotCjznm6uWjSoDaSpIQle1Rhqb+j9JIU4O2m/03oo97RwSoordQtb/6u3/cdNToWAAAAgEaGQo/z4sbeUQr19dDBnBJ9Uc9H6aWqPern39FLA2JCVVRu0W3z1mj57iNGxwIAAADQiFDocV54ubvorgtbS5JebQCj9JLk7e6qN2/rqYvaNVFphVV3vLNWi3dkGh0LAAAAQCNBocd5c1PfKIX6uis6xEc5xeVGx3EITzcXvXFLTw3vFKbySqsm/W+9ftqabnQsAAAAAI2AyWaz2YwO4azy8/MVEBCgvLw8+fv7Gx2nQThSWKZQXw+jYzhchcWq+z/ZpG83pcnFbNJL18VpdHxzo2MBAAAAqGdq0kMZocd51RDLvCS5uZg16/p4XdOjhSxWm6Z9nKhP1qUaHQsAAABAA0ahhyEOF5Tp1V92q7IBXEv/BxezSf++uqtu6hMlm0166LPNem/1AaNjAQAAAGigXI0OgMbHYrVp9KvLlZZXKn8vN93aL9roSA5jNps048rO8nB10dsr9uvxr7aqrMKiCQNbGx0NAAAAQAPDCD3OOxezSZMGV+1L/8JPu5SZX2pwIscymUx6fGQH3XPse5zx/Q4lLNljcCoAAAAADQ2FHoa4qU9LxUUGqqCsUk9/u93oOA5nMpn09+HtdP/FbSVJL/y8Sy8t2CXWoAQAAADgKBR6GMLFbNK/xnSWi9mk77ek65edDW//dpPJpKlDY/Xope0lSbN/2aPnftxJqQcAAADgEBR6GKZTRIDGD2glSXr8q20qLq80OFHduGtQG00f1VGSNOfXfZr+zTZZrZR6AAAAAOeGQg9DTRsWq+aBXjqUW6I3lu0zOk6dub1/Kz13VReZTNI7qw7oH19ukYVSDwAAAOAcsMo9DOXt7qpnruykpbsOa/zAVkbHqVNje0fJw9WsBz/dpI/Wpqqs0qoXrukqVxc+VwMAAABQcxR6GG5I+zANaR9mdIzz4qruLeTuata0jxL15cZDKq+0atYN8XKj1AMAAACoIVoEnIrNZtO+w4VGx6hTI7tG6LWbusvdxazvt6Tr7v9tUFmlxehYAAAAAOoZCj2cRm5xuW6c+7uueHWFMvIa1t70f3VJp3DNubWHPFzNWrQjU3e+u14l5ZR6AAAAAGePQg+n4efpppIKiwrLKvXUt9uMjlPnBrdrqnm395KXm4t+TTqscfPXqKisYa70DwAAAMDxKPRwGi5mk567qotczCb9uDVDi7Y3vL3p/+qCmFC9N763fD1ctXpftm59e43ySyuMjgUAAACgHqDQw6l0aOavCcdWu3/ym22NYsS6Z3Sw/jehj/w9XbX+QI5ufvN35RaXGx0LAAAAgJOj0J9EQkKCOnbsqF69ehkdpVG6b2isWgRV7U0/a1GS0XHOi/jIQH04sa+Cfdy1+WCexs79XUcLy4yOBQAAAMCJmWw2m83oEM4qPz9fAQEBysvLk7+/v9FxGpUlu7I0bt5auZhN+npyf3VuHmB0pPMiKbNAN735uw4XlCmmqa8+mNBHTf09jY4FAAAA4DypSQ9lhB5O6aJ2TXV512ZqHeojayP6zKltmJ8+nthXzQI8tSerUNe9sUppuSVGxwIAAADghBihPw1G6I2VV1IhLzcXubs2vs+dUrOLNXbuah3MKVGLIC99eGdfRQZ7Gx0LAAAAQB1jhB4NQoCXW7Uy35g+e4oM9tYnd/VTq1AfHcwp0bWvr9K+w4VGxwIAAADgRCj0cHrllVYlLNmjv32caHSU8yoi0EsfT+yr2Ka+ysgv1XVvrFZSZoHRsQAAAAA4CQo9nN6Bo0X6z8IkfZWYpoWNYG/64zX199RHE/uqQzN/HSks0w1zVmtbWp7RsQAAAAA4AQo9nF5smJ8mDGwtSXry662NYm/644X4eujDO/sorkWAsovKNXbOaiWm5hodCwAAAIDBKPSoF/7Ymz4tr1QvLWwce9MfL9DbXe9N6KMeLYOUX1qpm9/8XWuTs42OBQAAAMBAFHrUC17uLppxZWdJ0rwV+7X1UOObdu7v6aZ37+itfq1DVFhWqVvfWqOVe44YHQsAAACAQSj0qDcGt2uqUXERstqkR7/YIou18ax6/wcfD1fNG9dLF7ZtopIKi8bNX6ulu7KMjgUAAADAABR61CuPj+wgP09X7coo0OaDuUbHMYSnm4vm3tpDwzqEqazSqjvfXacF2zKMjgUAAADgPDPZGtPm3jWUn5+vgIAA5eXlyd/f3+g4OGbxjkxFh/qoTRNfo6MYqsJi1bSPEvX9lnS5mk2adUO8RnaNMDoWAAAAgHNQkx7qep4yAQ4ztEOY0RGcgpuLWS/fEC8PV7O+2HhIUz/cqLIKq67u0cLoaAAAAADOA6bco15LTM3Vyr2Nd2E4VxezXrw2Tjf0ipTVJj342SZ98HuK0bEAAAAAnAcUetRbC7dnasxrK/TAJ5tU2Mj2pj+e2WzSv8Z00W39Wspmk/7x5RbNX7Hf6FgAAAAA6hiFHvXWgJhQtQjyUnpeqf7TCPemP57ZbNL0KzrprgtbS5Kmf7tdry/ba3AqAAAAAHWJQo96y8vdRc+Mbtx70x/PZDLpkUvba+rQWEnS//24U7MWJYl1LwEAAICGiUKPeu2ve9NXWqxGRzKUyWTS/Re31UMj2kmSZi3arbv/t0G5xeUGJwMAAADgaBR61HuPj+wgf09XbTmUp3dXHTA6jlO4Z3CMnhndSW4uJv20LUOXvvybVu87anQsAAAAAA5EoUe919TPU49c2kGSNHPBLmXmlxqcyDnc0i9aX9zdX61CfZSeV6ob567WSwt2NfpZDAAAAEBDQaFHg3BDr0gNbd9U/xzZUU18PYyO4zS6tAjQd1MG6NoeLWS1SbN/2aPr56xWanax0dEAAAAAnCOTjRWzTik/P18BAQHKy8uTv7+/0XGAc/LNpjQ99sUWFZRVys/TVf8a00Wj4iKMjgUAAADgODXpoYzQo0EqKqtUUSPem/5kroiL0A/3DVT3qEAVlFZqyocb9eCnm3ifAAAAgHqKQo8G57fdh3XxS8v04oJdRkdxOpHB3vrkrn6aOiRGZpP02fqDGvnK8ka/5R8AAABQH1Ho0eBYbVJaXqneWZmszQdzjY7jdFxdzLr/knb68M6+ahbgqf1HijTmtRWa++s+Wa1cgQMAAADUFxR6NDiD2jbRFcf2pv/Hl+xNfyp9Wofox/sGakSncFVYbHr2hx26bd4aZRWwSwAAAABQH1Do0SA9PrKj/D1dtfVQvt5hb/pTCvR2139v7q5nx3SWp5tZv+0+oktn/aYlO7OMjgYAAADgDCj0aJCa+Hno0cv+3Js+LbfE4ETOy2Qy6aY+LfXtvQPUPtxPR4vKNW7+Wj317TaVVVqMjgcAAADgFCj0aLCu7xmpni2DVFxu0ZPfbDM6jtOLDfPTV5P76/YLoiVJ81Yk68qEldqTVWBsMAAAAAAnRaFHg2U2m/Svq7rIzcUkd1ezSisYbT4TTzcXTb+ik966raeCfdy1Iz1fI19Zrg/XpMhmY8E8AAAAwJmYbPyWfkr5+fkKCAhQXl6e/P39jY6DWko+UqToUB+jY9Q7Wfmluv+TTVq+54gk6bIu4XpuTFcFeLsZnAwAAABouGrSQxmhR4NHma+dpv6eeveO3nr00vZyNZv0w5YMXfryr1qzP9voaAAAAABEoUcjkpVfqns/2KDE1Fyjo9QbZrNJdw1qoy/uuUDRId5KyyvVDXNW6aWFSWwHCAAAABiMQo9G4z+LkvTd5nT94wv2pq+pri0C9d3Ugbq6ewtZbdLsxbt1w5zVOphTbHQ0AAAAoNGi0KPReOCSdgrwctP29HzNX5lsdJx6x9fDVTOvi9PLN8TLz8NV6w7k6NKXf9N3m9OMjgYAAAA0ShR6NBqhvh76x2XtJUkzFyQxulxLo+Ob64f7BqpbVKAKSit17wcb9dBnm1RcXml0NAAAAKBRodCjUbm2R6R6RwerpMKiJ7/exlZstRQZ7K1P7uqney+KkckkfbLuoEbOXq6th/KMjgYAAAA0GhR6NCpms0nPjuksNxeTFu/M0s/bMoyOVG+5uZj14PB2en9CH4X7e2rfkSKNeW2F3vxtn6xWPigBAAAA6hqFHo1ObJifJg1qI0l649d9jNKfowvahOrH+wbqko5hqrDYNOP7Hbp9/lodLigzOhoAAADQoJlstJlTys/PV0BAgPLy8uTv7290HDhQaYVFc37dpzsGtJKvh6vRcRoEm82m939P0TPfbVdZpVWhvu568do4DW7X1OhoAAAAQL1Rkx5KoT8NCj1Qc0mZBZr64UbtzCiQJI0f0EoPjWgnD1cXg5MBAAAAzq8mPZQp92j0rFabvtucxt70DtI2zE9fTe6v2/q1lCS9tXy/rnptpfYeLjQ4GQAAANCwUOjR6E3633rd+8FGzVuRbHSUBsPTzUVPje6sN2/tqSBvN21Ly9fI2cv18doU1iwAAAAAHIRCj0ZvWIcwSdJLC9mb3tGGdQzTT9MuVP+YEJVUWPTw51t07wcblVdSYXQ0AAAAoN6j0KPRu7ZnC/VuVbU3/RPsTe9wYf6eeu+OPnp4RHu5mk36fku6Lnv5N61NzjY6GgAAAFCvUejR6JlMJv3r2N70v+zM0k9b2Zve0cxmk+4e3Eaf3X2BWoZ461Buia5/Y5VmLUpi7QIAAACglij0gKSYpn66+9je9E9+s035pUwJrwvxkYH6fupAXdWtuaw2adai3Ro7d7UO5ZYYHQ0AAACodyj0wDH3XBSj6BBvZRWU6cWfdxkdp8Hy9XDVS9fHa9b18fL1cNXa5BxdOutX/bAl3ehoAAAAQL1CoQeO8XRz0bNjuqhDM3+N6dbc6DgN3pXdmuv7qQMUFxmo/NJK3fP+Bj382WYVl1caHQ0AAACoF0w2VgA7pfz8fAUEBCgvL0/+/v5Gx8F5YrXaZDabjI7RaFRYrPrPwiT9d9le2WxSZLCXnr6isy5q39ToaAAAAMB5V5Meygg98BfHl/miMkaL65qbi1kPjWiv98f3UbMAT6Vml2jc/LWa9N56pedxbT0AAABwKhR64CQqLVa9vGi3+j//i1Kz2Zv+fLggJlSL7h+kiRe2lovZpJ+2ZWjYzGV687d9rIQPAAAAnASFHjgJF7NJK/YeUW5xhZ74eit7058nPh6u+sdlHfTdlAHqHhWoonKLZny/Q6NeXaH1B3KMjgcAAAA4FQo9cBJVe9N3kZuLSUt2HdYPW9ib/nzq0Mxfn026QP93VRcFeLlpR3q+rv7vSj36xRblFpcbHQ8AAABwChR64BRimvrq7sExkqTp37I3/flmNpt0Q+8o/fLAIF3To4Uk6cM1KRo6c5k+X3+QWRMAAABo9Cj0wGncM7iNWof66HBBmV74ib3pjRDi66EXr43TxxP7Krapr44WleuBTzfphjmrtSerwOh4AAAAgGEo9MBpeLq5aMaYzpKk//1+QBtSuI7bKH1ah+j7qQP18Ij28nQz6/f92br05d/07592qqTcYnQ8AAAA4Lyj0ANncEGbUF3dvYXcXcw6mMM2akZydzXr7sFttPBvgzS0fVNVWGx6beleXfyfZfplZ6bR8QAAAIDzymTjQtRTys/PV0BAgPLy8uTv7290HBgou6hchwvK1C7cz+goOMZms2nB9kw99c02peWVSpJGdArXE6M6KiLQy+B0AAAAQO3UpIdS6E+DQo9TsdlsMplMRseApKKySr28eLfeWr5fFqtN3u4u+tuwtrq9f7TcXJiEBAAAgPqlJj2U33aBGtqQkqNRry7XoVym3zuDP/au/37qAPVsGaTicoue/WGHRr2ynL3rAQAA0KBR6IEasNls+tf3O7T1UL6mfbRRlRar0ZFwTPtwf31yVz89f3UXBXq7aWdGwbG96zezdz0AAAAaJAo9UAMmk0kzr4uTr4er1ibn6JVf9hgdCccxm026vleUfnlgsK61712fqiEzl+kz9q4HAABAA0OhB2qoZYiPnj22ld0rv+zWmv3ZBifCXwX7uOuFa+P0yV391DbMV9lF5Xrw2N71uzPZux4AAAANA4UeqIXR8c11dfcWstqkaR9tZEq3k+rdKljfTx2oRy5tLy83F/auBwAAQINCoQdq6anRndQq1EdpeaV65PMtTOd2Um4uZk0a1EYL779QwzqEqdLK3vUAAABoGCj0QC35erhq9g3d5OZiUoXFqtIKFshzZi2CvPXmbT0155Yeigjw1MGcEt0xf53uem+d0tixAAAAAPUQ+9CfBvvQ42xsPZSnThH+7EtfjxSVVWr2sb3rK9m7HgAAAE6kJj2UQn8aFHrUhsVqk4uZcl8f7MzI1z+/3Kp1x/arbx/up2fHdFaPlsEGJwMAAEBjVZMeylAU4CAFpRWa9tFGPfXtNqOj4Cz9sXf9v6/uqiD73vWr9Mjnm5VTxEKHAAAAcG4UesBBNh/M01eJaXp31QEt2JZhdBycJbPZpOt6RWrxA4N1Xc+qves/WpuqoS+xdz0AAACcG4UecJD+MaG6c2ArSdJDn29Weh4LrdUnwT7u+vc1cfp0UvW9669n73oAAAA4KQo94EB/H95eXZoHKLe4QtM+SpTFyuhufdMrumrv+keP7V2/5tje9c+zdz0AAACcDIUecCB3V7Nmj+0mb3cX/b4/W68t2WN0JNSCm4tZdx3bu/7ijlV71//32N71i3ewdz0AAACcA4UecLBWoT56enRnSdKsxbu1/kC2wYlQWy2CvDX31p6ae2tPNQ/00sGcEo1/Z50mvrtOh9i7HgAAAAaj0AN14OruzTU6PkLBPu4qr2TafX13cccwLbz/Qk0a1EauZpMWbM/UxS8t05xf96rCYjU6HgAAABop9qE/Dfahx7koKK1QWaVVob4eRkeBA+3KKNA/v9qitclVe9e3C/PT3y5uq0s6hslsNhmcDgAAAPVdTXoohf40KPRwpNIKizzdXIyOAQewWm36bMNBPffDDuUUV0iS2of7aerQWI3oFE6xBwAAQK3VpIc2iin3Y8aMUVBQkK655hqjo6CR+jrxkC789xLtySo0OgocwGw26bqekVry4GBNGRIjPw9X7cwo0D3vb9CIl3/Vt5vS2OEAAAAAda5RFPr77rtP7777rtEx0EjZbDZ9si5VWQVlmvLhRpVWsPVZQxHo7a4HLmmn5Q8P0X1DY+Xn6aqkzEJN+XCjhs/6VV8nHqLYAwAAoM40ikI/ePBg+fn5GR0DjZTJZNJL18Ur2MddO9Lz9fxPO42OBAcL8HbT3y5uq+UPD9H9F7dVgJeb9mQV6r6PEnXxS8v0xYaDqmTxPAAAADiY4YX+119/1ahRoxQRESGTyaSvvvrqhGMSEhIUHR0tT09P9enTR2vWrDn/QYFzEObvqRev7SpJmrciWb/sZC/zhijAy01Th8Zq+cMX6e/D2ynQ2037jhTp/k82adhLy/TpulRWxQcAAIDDGF7oi4qKFBcXp4SEhJM+//HHH+v+++/Xk08+qQ0bNiguLk7Dhw9XVlaW/Zj4+Hh17tz5hFtaWtr5+jaAMxrSPky3XxAtSXrw083Kyi81NhDqjJ+nmyZfFKPlDw/RwyPaK9jHXclHi/X3zzZr6Mxl+nhtisorKfYAAAA4N061yr3JZNKXX36pK6+80v5Ynz591KtXL7366quSJKvVqsjISE2ZMkWPPPLIWZ976dKlevXVV/XZZ5+d8piysjKVlZXZ7+fn5ysyMpJV7uEwpRUWjXltpXak56t/TIjeu6MPK6I3AsXllfrf6gOa8+s+HSkslyQ1D/TSPRe10bU9IuXuavhnqwAAAHASDWaV+/Lycq1fv17Dhg2zP2Y2mzVs2DCtWrXK4a/33HPPKSAgwH6LjIx0+GugcfN0c9ErY7vJ291FHZv5y+I8n6ehDnm7u2rihW3020ND9M/LO6iJn4cO5ZbosS+3avALS/TeqmSVVbJYIgAAAGrGqQv9kSNHZLFYFBYWVu3xsLAwZWRknPV5hg0bpmuvvVY//PCDWrRoccoPAx599FHl5eXZb6mpqeeUHziZmKa+WvrgYD12eUe5uTj1P0E4mJe7iyYMbK3fHrpIT47qqDB/D6Xllerxr7dp0L+X6p2VyeyCAAAAgLPmanSA82HRokVndZyHh4c8PDzqOA0gNfX3tP+90mJVucUqb/dG8c8RqpqpMa5/K43tHaVP1qXqv0v3Kj2vVE9+s00JS/borkFtdGPvKHm5uxgdFQAAAE7MqYcHQ0ND5eLioszM6iuCZ2ZmKjw83KBUgOOkZhfrujdW6R9fbDE6Cgzg6eaiW/tFa+nfB2vGlZ3VPNBLWQVleua77Rr47yWa++s+FZdXGh0TAAAATsqpC727u7t69OihxYsX2x+zWq1avHix+vXrZ2AywDGyCkq16WCevkpM0xcbDhodBwbxcHXRzX1basmDg/XcVV3UIshLRwrL9OwPOzTw+SV6fdleFZVR7AEAAFCd4YW+sLBQiYmJSkxMlCTt379fiYmJSklJkSTdf//9mjt3rt555x3t2LFDd999t4qKijRu3DgDUwOO0aNlsKYNjZUkPf7VVu0/UmRwIhjJ3dWssb2jtOTBwfr31V0VFeyto0Xl+r8fd2rA878oYckeFZRWGB0TAAAATsLwbeuWLl2qiy666ITHb7vtNs2fP1+S9Oqrr+qFF15QRkaG4uPjNXv2bPXp06fOs9VkuwCgtixWm26cu1q/789Wl+YB+vzuC9jGDJKq1lf4KjFNr/6yW8lHiyVJAV5umjCglW7rHy1/TzeDEwIAAMDRatJDDS/0zoxCj/MlPa9El778m3KLKzTxwtb6x2UdjI4EJ1JpserbzWl65Zc92ne4ahaHv6er7hjQSuP6t1KAF8UeAACgoaDQOwiFHufTz9sydNd76yVJ79zRW4PaNjE4EZyNxWrTd8eK/Z6sQkmSn4erxvWP1h0DWinQ293ghAAAADhXFHoHodDjfHv8q61afyBHs8d2U0xTX6PjwElZrTb9uDVDsxfv1q7MAkmSr4erbrugpcYPaK1gH4o9AABAfUWhdxAKPc630gqLTKaqVc+BM7FabVqwPUMvL96jHen5kiRvdxfd0q+lJg5srRBfD4MTAgAAoKYo9A5CoYfRDheUqYkfpQynZ7XatGhHpl5evFvb0qqKvZebi27uG6WJF7bhZwgAAKAeodA7CIUeRrFabZr9y269vmyvPpt0gTo3DzA6EuoBm82mX3Zm6eXFu7X5YJ4kydPNrBt7t9SkQa3V1N/T4IQAAAA4k5r0UPbGOomEhAR17NhRvXr1MjoKGimTSdqRnq/SCqumfLhRRWWVRkdCPWAymTS0Q5i+ntxf88b1UnxkoEorrHp7xX4N+PcSTf9mmzLySo2OCQAAAAdhhP40GKGHkXKLy3Xpy78pPa9U1/RooRevjTM6EuoZm82m33Yf0cuLd2v9gRxJkruLWdf0bKGJA1srOtTH4IQAAAD4K6bcOwiFHkZbve+obpy7Wlab9PIN8Rod39zoSKiHbDabVu49qpcX7daa5GxJktkkXdqlme4e1IZLOgAAAJwIhd5BKPRwBi8tTNLsxbvl5+Gq76cOVFSIt9GRUI+t3ndUry/bq6W7DtsfGxgbqkmD2uiCNiEymUwGpgMAAACF3kEo9HAGlRarbpizWusO5Cg+MlCfTeonVxeWv8C52Z6Wrzd+3avvNqfLYq36z0DXFgGaNKiNhncKl4uZYg8AAGAECr2DUOjhLA7mFOvq/67U1KGxurF3FKOocJjU7GLN/W2fPl6bqrJKqySpVaiPJl7YWld1by4PVxeDEwIAADQuFHoHodDDmZRWWOTpRrlC3ThaWKZ3VibrnVUHlFdSIUlq4ueh8QNa6aY+UfLzdDM4IQAAQONAoXcQCj2cVV5JhaxWm4J83I2OggamqKxSH65J0VvL9yv92BZ3fp6uurlvS43rH62mfuxlDwAAUJco9A5CoYcz2pSaq3ve36D24X5687aeTL9HnSivtOrrxEN649d92pNVKElydzXrmh5seQcAAFCXatJDWVkLqGfcXMw6XFCmxTuz9M7KZKPjoIFydzXr2p6RWjDtQs25pYe6RwWqvNKqD35P0ZCZSzX5/Q3acjDP6JgAAACNGiP0p8EIPZzV/BX7Nf3b7XJ3Neure/qrYwQ/n6hbNptNa5Nz9N+le7TkuC3vBsSE6u7BbHkHAADgKEy5dxAKPZyVzWbThHfWafHOLLVp4qNvpwyQt7ur0bHQSOzMyNcby/bpm01p9i3vujSv2vJuRGe2vAMAADgXFHoHodDDmWUXlWvErF+VVVCmsb0j9dxVXY2OhEYmNbtYby3fr4/Wpqi0omrLu+gQb028sI2u6t6cXRkAAABqgULvIBR6OLuVe47oprd+l80mvX5zD43oHG50JDRCRwvL9M6qA3pnZXK1Le/u6N9KN/WNkj9b3gEAAJw1Cv05SkhIUEJCgiwWi5KSkij0cGr//mmndmcV6t9Xd2UbOxiqqKxSH61N1Zu/7ftzyzsPV93YN0rj+7dSU3+2vAMAADgTCr2DMEKP+sBitclsEguSwWmUV1r1zaY0vbFsr3b/seWdi1lX92iuiRe2USu2vAMAADglCr2DUOhR39hsNu1IL2DVezgFq9WmxTuz9PqyvVp/IEeSZDJJl3YO16RBbdS1RaCxAQEAAJwQhd5BKPSoTyotVj3w6SZ9uylNH97ZV31ahxgdCbBbm5yt/y7dq192Ztkf6x8TorsHxah/DFveAQAA/KEmPdR8njIBqGOuLma5uZhltUnTPk5UbnG50ZEAu17RwXr79l76adpAjenWXC5mk1bsOaqb3/pdo15dru82/7kFHgAAAM4OI/SnwQg96puiskqNfGW59h8p0vBOYXr95h6MfMIpHcwp1pu/nbjl3Z0XttbV3Vuw5R0AAGi0mHLvIBR61EdbD+VpzGsrVGGxacaVnXVz35ZGRwJOKbuoXO+sTNY7q5KVW1y15V2or4fuGBCtm/u2ZMs7AADQ6FDoHYRCj/rqzd/2acb3O+ThatYX91ygThEBRkcCTqu4vFIfrana8i7t2JZ3vh6uuqlPlO4Y0EphbHkHAAAaCQq9g1DoUV9ZrTaNf2etluw6rJYh3lp0/yC5ubBkBpxfhcWqbxLT9Mave5WUWbXlnavZpAvbNtHo+Ahd3DFM3u6uBqcEAACoOxR6B6HQoz7LK6nQHfPX6oFL2uqCNqFGxwFqxGq16ZdjW96tO7blnSR5u7voko5hGt2tuQbEhPJBFQAAaHAo9A5CoUd9Z7PZWBQP9d6erEJ9k3hIX29K04GjxfbHQ3zcdXnXZhod31zdowL5WQcAAA0Chd5BKPRoSPZkFWjLoTyN6dbC6ChArdhsNiWm5urrxDR9tzlNRwr/3JoxMthLo+Oa68puEYpp6mdgSgAAgHNDoXcQCj0aipSjxbp89m8qrrDo3Tt6q38MU/BRv1VarFq+54i+SUzTz9syVFRusT/XKcJfo+MjdEVcc4UHsJgeAACoXyj0DkKhR0Nhs9n0wCeb9MXGQwrwctM39/ZXyxAfo2MBDlFSbtHCHZn6JvGQlu46rEpr1X/WTCapb6sQXdktQiM6N1OAF1vgAQAA50ehdxAKPRqS0gqLrp+zWptScxXb1Fdf3HOB/NjjGw1MTlG5vt+Srq8TD2lt8p+L6bm7mHVR+ya6Mr65LmrfVJ5uLgamBAAAODUKvYNQ6NHQZOaX6opXlyszv0zDOjTVnFt6ymxmITE0TAdzivXNpjR9vTFNuzIL7I/7ebhqROdwXdmtufq2DpEL/wYAAIATodA7CIUeDdGm1Fxd+8YqlVdaNfmiNvr78PZGRwLq3M6MfH21MU3fJB5SWl6p/fGmfh4aFRehK+Obq3Nzf1bKBwAAhqPQn6OEhAQlJCTIYrEoKSmJQo8G56uNhzTt40T1aRWs/03ow17eaDSsVpvWJmfr601p+n5zuvJKKuzPtW7iY18pnzUmAACAUSj0DsIIPRqyn7dl6KJ2TeXuSplH41ReadWypMP6KvGQFm3PVFml1f5cfGSgroyP0OVdI9TEz8PAlAAAoLGh0DsIhR6NSWmFhYXC0GgVllXq560Z+irxkFbsOaJjC+XLxWxS/5hQXRkfoUs6hcvXw9XYoAAAoMGj0DsIhR6NQaXFqn/9sFOJqTn6cGJfebhS6tG4HS4o03eb0/RVYpo2pebaH/d0M2tYhzBdGd9cF7ZtwuwWAABQJyj0DkKhR2NwMKdYl738m/JLK3VNjxZ64ZquLAwGHLP/SJG+SUzT14mHtO9Ikf3xQG83Xdalma6Mb66eLYPYLQIAADgMhd5BKPRoLH7bfVi3vb1GVpv0+MiOGj+gldGRAKdis9m05VCevtqYpm83p+lwQZn9ueaBXlUr5XeLUPtw/lsBAADODYXeQSj0aEzeXr5fT3+3XWaTNH9cb13YtonRkQCnZLHatGrvUX2VeEg/bc1QYVml/bn24X4aHd9cV8RHqHmgl4EpAQBAfUWhdxAKPRoTm82mhz/frE/WHZS/p6u+mtxfrZv4Gh0LcGqlFRb9sjNLX208pKW7Dqvc8udK+T1bBml0fIQu69JMIb6slA8AAM4Ohd5BKPRobMoqLbpx7u9afyBHMU199eN9A9mjHjhLecUV+nFrur5KPKTf92fLdtxK+QNiQnVFXIQu6RQmP083Y4MCAACnRqF3EAo9GqPDBWUaO3e1HryknUZ0Djc6DlAvZeSV6rvNafpmU5o2H8yzP+7hatbQDk11RVyEBrdrylaRAADgBBR6B6HQo7GqtFjlysg84BD2lfI3HdK+w3+ulO/n4arhncN1RVyELmgTwr85AAAgiULvMBR6QErLLdGO9HwN7RBmdBSgXrPZbNqWlq9vN1WN3KfnldqfC/V11+VdmumK+Ah1jwpi60gAABoxCr2DUOjR2KVmF2vMayuUX1qpT+7qp/jIQKMjAQ2C1WrTugM5+mbTIX2/OV05xRX251oEVW2DNzqebfAAAGiMKPQOQqFHY2e12nTX/9Zr4fZMNfXz0LdTBijM39PoWECDUmGxavmeI/omMU0LtmWoqNxif65tmK+uiIvQFXHNFRXibWBKAABwvlDoHYRCD0iFZZW66rUVSsosVFyLAH18Vz8W8gLqSEl51TZ4XyeeuA1efGSgRsdH6PKuzdTUjw/WAABoqCj0DkKhB6qkHC3WFQnLlVtcoSvjI/Sf6+O5xheoY3klFfp5a4a+2ZSmlXuPyHrsv9Zmk9SvTYhGxzXX8M7hCvBiGzwAABoSCr2DUOiBP63ce0S3vLVGFqtNj1zaXpMGtTE6EtBoZBWU6ofN6fp6U5o2puTaH3d3MWtwuya6Ij5CQ9uHycud2TMAANR3FHoHodAD1b27KllPfL1NnZv764u7+8vdlW22gPMt5Wixvt2cpm8S07Qrs8D+uI+7iy7pVLUN3oDYULmxDR4AAPUShd5BKPRAdTabTR+vTdUV8RHydnc1Og7Q6O3MyNc3iVXb4B3MKbE/HuTtpsu6NNMVcRHqFR0ss5lLZAAAqC8o9OcoISFBCQkJslgsSkpKotADp2Gx2uRCWQAMZbPZtCElV99uStN3m9N0pLDc/lyzAE+NiovQFXER6hThz/oXAAA4OQq9gzBCD5yazWZTwpI9WpOco7dv6ylXpvcCTqHSYtWqfUf1TWKaftqaoYKySvtzrZv4HNsGL0Ktm/gamBIAAJwKhd5BKPTAqaUcLdbwWb+qpMKiO/q30hOjOhodCcBflFZYtHTXYX2z6ZAW78hSWeWf2+B1aR6gK+IiNDKumZoFeBmYEgAAHI9C7yAUeuD0ftySrrvf3yBJ+vfVXXVdr0iDEwE4lYLSCi3cnqmvE9O0fM8RWY7tg2cySb2ig3VJxzBd0jFcUSHeBicFAKBxo9A7CIUeOLNZi5I0a9FuubmY9OGdfdUzOtjoSADO4GhhmX7Ykq5vNqVpbXJOtefahvnq4o5hGtYhTHEtAllQDwCA84xC7yAUeuDMrFabJn+wQT9uzVCor7u+vneAmgcyfReoLw7llujnrRlauD1Ta5Kz7SP3ktTUz0NDO4Tp4o5NdUGbUHm6sc89AAB1jULvIBR64OwUlVXq6v+u1M6MAnWK8NeX97BHPVAf5RVXaMmuLC3cnqllSYdVeNyCet7uLhoYG6qLO4ZrSPumCvZxNzApAAANF4XeQSj0wNlLzS7WNa+v1N+GtdUNvaOMjgPgHJVVWrR6X7YWbc/Uoh2ZSs8rtT9nNkk9WwZXTc3vGKZWoT4GJgUAoGGh0DsIhR6omZJyi7zcmZILNDQ2m03b0vK1YHumFm7P1I70/GrPxzT11bAOYbq4Y5i6RXLdPQAA54JC7yAUeqD2corKlZRZoD6tQ4yOAsDBDuYUHxu5z9LqfUdVedx196G+7hravqrcD4jlunsAAGqKQu8gFHqgdg7lluiGOauUXViuL+7pr3bhfkZHAlBH8koqtHRXlhbtyNLSnVkqOO66e083swbGNtHFHcM0pH1Thfp6GJgUAID6gULvIBR6oHYqLFbd+tYardp3VJHBXvpm8gAFsYAW0OCVV1q1Zn+2Fm7P0KIdWTqUW2J/zmSSekQFaVjHqtH7Nk18DUwKAIDzotA7CIUeqL2conJdkbBcqdkl6tc6RO+O7y03F1a+BxoLm82m7en5WnhsUb2th6pfd9861EcXHyv33aKC5MJ19wAASKLQOwyFHjg3uzIKdNVrK1RUbtGt/Vrq6dGdjY4EwCBpuSVavCNTC7ZnavW+o6qw/PnrR4iPu4a0b6phHcM0MDZU3u6uBiYFAMBYdV7oU1NTZTKZ1KJFC0nSmjVr9MEHH6hjx46aOHFi7VI7IQo9cO4Wbs/UxPfWyWaT/jWmi27sw5Z2QGNXUFqhZUmHtXB7ppbszFJ+6Z/X3Xu4mjUgJlQXdwzT0A5hauLHdfcAgMalzgv9wIEDNXHiRN1yyy3KyMhQu3bt1KlTJ+3evVtTpkzRE088UevwzoRCDzhGwpI9euHnXWrTxEc/3neh3F2Zeg+gSoXFqrX7s7VwR9WWeAdzql93Hx8ZqIs7humSY9fdm0xMzQcANGx1XuiDgoK0evVqtWvXTrNnz9bHH3+sFStWaMGCBZo0aZL27dtX6/DOhEIPOIbNZtPry/bphl6RLI4H4JRsNpt2ZhRo0fZMLdyRqc0H86o9Hx3irYs7hmlYhzB1bxnEuhwAgAapzgu9r6+vtm7dqujoaF1xxRXq37+/Hn74YaWkpKhdu3YqKSk580nqAQo9UHdsNhsjbQBOKyOvVIt2VC2qt3LPUZVbrPbnvN1d1C0qUL2ig9UrOljdogK59h4A0CDUeaHv06ePLrroIl1++eW65JJLtHr1asXFxWn16tW65pprdPDgwVqHdyYUeqBufPB7ilbtO6qXr4+XmZWtAZyFwrJK/Zp0WIu2Z+qXXVnKLa6o9ryL2aTOEf7qFR2sntHB6hUdpBD2vQcA1EN1XuiXLl2qMWPGKD8/X7fddpvefvttSdI//vEP7dy5U1988UXtkjsZCj3geClHizXspWUqt1g1bVispg1ra3QkAPWM1WrT7qxCrUnO1rrkbK3dn620vNITjmvdxEe9jxX83tHBigz2YmYQAMDpnZdt6ywWi/Lz8xUUFGR/LDk5Wd7e3mratGltTul0KPRA3fhkbaoe+nyzJOm/N3XXpV2aGZwIQH13KLdEa/dna21y1S0ps/CEY5r6eRyboh+kntHB6tDMXy7MEgIAOJk6L/QlJSWy2Wzy9vaWJB04cEBffvmlOnTooOHDh9cutROi0AN15+lvt+vtFfvl5eaiz+++QB0j+DcGwHFyi8u1/kDOsVH8HG0+mKsKS/Vfefw8XNW9ZZB6RQepV3Sw4iID5enmYlBiAACq1Hmhv+SSS3TVVVdp0qRJys3NVfv27eXm5qYjR47opZde0t13313r8M4gISFBCQkJslgsSkpKotADdaDSYtW4+Wv12+4jah7opW/u7c/1rgDqTGmFRZtSc4+N4Odo/YEcFZZVVjvGzcWkri0C1TM6SL1aBqtndJACvdmZAwBwftV5oQ8NDdWyZcvUqVMnvfnmm3rllVe0ceNGff7553riiSe0Y8eOWod3JozQA3Urr7hCoxOWK/losXq3Ctb7E/qwDRWA88JitWlnRn7VNP0DOVq7P1tZBWUnHNc2zNe+kn6vVsFqHuhlQFoAQGNSkx5aq/1diouL5efnJ0lasGCBrrrqKpnNZvXt21cHDhyozSkBNEIB3m5687aeuuq1lbqoXVO5ci0rgPPExWxSp4gAdYoI0O39W8lmsyk1u8S+0N6a5GztO1ykpMxCJWUW6v3fUyRJEQGe6tXqz4X2Ypv6slsHAMAwtRqh79q1qyZMmKAxY8aoc+fO+umnn9SvXz+tX79el19+uTIyMuoi63nHCD1wfuQUlSvIh2mtAJzL0cIyrU3OqVpJPzlbW9PyZbFW/7UpwMtNPVtWLbLXu1WQOjcPkIcr1+EDAGqvzqfcf/bZZ7rxxhtlsVg0ZMgQLVy4UJL03HPP6ddff9WPP/5Yu+ROhkIPnH/5pRXacjBP/WNCjY4CANUUl1dqY0rVdfjrknO0ISVHxeWWasd4uJoVFxloX2ive8sg+Xu6GZQYAFAfnZdt6zIyMpSenq64uDiZzVXXvK5Zs0b+/v5q3759bU7pdCj0wPmVW1yusXN/196sQs0f10sXUOoBOLEKi1U70vO1Zn9VwV+bnK2jReXVjjGbpPbh/uoZHaS4FoGKiwxQ61Cm6QMATu28FPo/HDx4UJLUokWLczmNU6LQA+eXxWrTvR9s0I9bM+Tj7qKP7+qnzs0DjI4FAGfFZrNp/5Ei+0r6a5OzdeBo8QnH+Xq4qkvzAHWNDFBci0B1bRGg5oFeMpko+QCA81DorVarZsyYoZkzZ6qwsFCS5OfnpwceeECPPfaYfcS+vqPQA+dfaYVFt89bo9X7shXq667PJl2g6FAfo2MBQK1k5Zdq7bHp+ZsP5mrroXyVVFhOOC7Ex11xkVXl/o+Sz1aeANA41Xmhf/TRR/XWW2/pqaeeUv/+/SVJy5cv1/Tp03XnnXfq2WefrV1yJ0OhB4xRUFqhG+as1ra0fEUGe+nzSReoqb+n0bEA4JxVWqzac7hQm1PzlHgwV5sP5mpneoEqrSf+OtY80Evxx0p+1xaB6tIiQL4etdqgCABQj9R5oY+IiNDrr7+uK664otrjX3/9te655x4dOnSopqd0ShR6wDiHC8p0zesrdeBosTo089fHd/VlYSkADVJphUU70vO1+WCeNqXmatPBXO07UqS//oZmMkltmvjar8Xv2iJQHZr5sao+ADQwdb4PfXZ29kkXvmvfvr2ys7Nrc0oAqKaJn4feu6OPrvrvSuUUletIQRmFHkCD5Onmom5RQeoWFWR/rKC0QlsO5dlL/uaDeTqUW6I9WYXak1WozzdUrWHk5mJS+3D/qqn6kYGKaxGomKa+cmHRPQBoFGo1Qt+nTx/16dNHs2fPrvb4lClTtGbNGv3+++8OC2gkRugB4+1Iz5e/l5uaB3oZHQUADHWksEybD+ZqU2qeNh2sKvnZf1lVX5K83V3UOSKgWsmPDGbRPQCoL+p8yv2yZct0+eWXKyoqSv369ZMkrVq1Sqmpqfrhhx80cODA2iV3MhR6wPnsySpUmyY+/GIKoNGz2Ww6mFOizQfzqor+wVxtOZinovITF90L8nZTlxaBivtj0b3IADX1Y20SAHBG52XburS0NCUkJGjnzp2SpA4dOmjixImaMWOG5syZU5tTOh0KPeBcftqarqkfJWriwNZ6cHg7o+MAgNOxWG3ad7hQm+wlP0870vJVbrGecGyzAE/7gnvxkVWL7nFpEwAY77zuQ3+8TZs2qXv37rJYTvxkuD6i0APO5cM1KXr0iy2SpOmjOur2/q0MTgQAzq+80qqdGflVJf/Y9fi7swp0koX11TrUx17yu7cMUqcIf7m5NIztiAGgvqjzRfEAwAhje0fpSEGZZi5M0lPfbVewr4euiIswOhYAODV3V7O6tghU1xaBUt+WkqSiskpt/WPRvWPX46dkF2vfkSLtO1KkrxLTJElebi6KjwxUz+gg9YwOVveoQPkxig8AToNCD6BeuXdIjI4UlumdVQf0wCeJCvJ208DYJkbHAoB6xcfDVX1ah6hP6xD7Y9lF5dp8rNxvSs3V+pQc5RZXaNW+o1q176gkyWyS2oX7q1d0kHq0DFKv6GBFsGgpABiGKfenwZR7wDlZrTZN/WijvtucLm93F314Z1/FRQYaHQsAGhSr1aZ9Rwq1NjlH65JztO5Atg4cLT7huOaBXsfKfdUoftswP7bNA4BzUGdT7q+66qrTPp+bm1uT0wFArZjNJr10XbzySir02+4j+mz9QQo9ADiY2WxSTFM/xTT109jeUZKkrIJSrU/OqSr5B7K1LS1fh3JLdCi3RN9sqpqm7+fhqu7HCn6PlsGKjwyUl7uLkd8KADRYNRqhHzdu3FkdN2/evFoHciaM0APOrbCsUh/+nqLxA1rJzGgQAJx3RWWV2pSaay/4Gw7knLBtnqvZpE7NA9SrZdUIfs/oIIX6ehiUGACcn2Gr3Dc0FHqgfrFabSqusMjXg+VBAMAIlRardmYUaP2BHK1Nzta65Bxl5JeecFyrUJ9q0/Rbh/rIZOKDWQCQKPQOQ6EH6o/ySqse/HST0nJL9N74PkzvBAAnYLPZdCi3xH4N/rrkHO3KLNBff/sM9nG3F/weLYPVpXmA3F3ZLg9A40ShdxAKPVB/7D9SpNGvLld+aaWGtm+q12/pwd7JAOCE8koqtCElR+uSs7U2OUebUnNVVmmtdoyHq1lxLaq2y+sVHazuUUEK8Ga7PACNA4XeQSj0QP2yNjlbN7/5u8oqrbqmRwu9cE1XpnACgJMrr7Rqa1rescX2srXuQI6yi8pPOK5dmJ96RgdV3VoGq0WQF/8fD6BBotA7CIUeqH8Wbs/UpP+tl8Vq012DWuvRSzsYHQkAUAM2m037jxRp3bGCv/5AjvYdKTrhuHB/T/WIDrIvttc+3E+uzMwC0ABQ6B2EQg/UT5+sTdVDn2+WJP3z8g6aMLC1wYkAAOfiSGGZ1iXnaP2Bqmn6Ww/lqdJa/VdYTzezYpr6Krapn2Ka+qptmJ9im/oqMthbLuyEAqAeodA7CIUeqL9eW7pH//5plzxczVr294sUHuBpdCQAgIOUlFu06WCu1h2bor/+QI4KSitPeqyHq1ltmvgqNsxXsU19FXus6EcFezOiD8Ap1aSHsrfTSSQkJCghIUEWi+XMBwNwSncPaqPiMov6x4RS5gGggfFyd1Hf1iHq2zpEkmSx2pSSXazdmQXanVVo/3NPVqHKKq3anp6v7en51c7h7mpW61Afe8FvG+armKZ+ahnizaKqAOoNRuhPgxF6oGGxWm0yM+0SABoNi9WmgznF2p1ZqKSsAu3JLLQX/ZKKkw/cuLmY1Oq4oh/b1E9tw3zVMsSHrfQAnBdMuXcQCj3QcCRlFmjqhxv1ythuig3zMzoOAMBAVqtNh3JLtDurQEmZhdqdWag9WVWj+sXlJy/6rmaTokN97CP5sceu048O9ZaHq8t5/g4ANGQUegeh0AMNx7h5a7Rk12E1C/DU53dfoIhAL6MjAQCcjNVqU1peyZ/T9o8b0S8sO/k1+i5mk1qGeKttUz/FhvnaF+RrFeojTzeKPoCao9A7CIUeaDhyisp17RurtCerUG2a+OizSRcoyMfd6FgAgHrAZrMpPa/0L0W/6s+CUxR9s0lqGeJzbCG+qqn7sWG+atPEl6IP4LQo9A5CoQcalrTcEl3935VKzytVfGSgPrizj7zdWRsUAFA7NptNmfll9qn7e46V/KTMAuWfYtV9k0mKCvZWbFNfdW4eoG5RQYpvEagAb7fznB6As6LQOwiFHmh49mQV6JrXVym3uEKD2jbRm7f1ZDVjAIBD2Ww2HS4o0+6sqnK/O6tQe44tzJdbXHHSr4lp6qtukYHqFhWkblGBahvmJxcWcgUaJQq9g1DogYZpQ0qObpr7u0oqLJo6JEb3X9LO6EgAgEbAZrPpSGG5dmcVaFdGgTYfzNOGlBwdOFp8wrHe7i6KaxGoblFVJT8+MlBN/DwMSA3gfKPQOwiFHmi4luzK0tvL9yvhpu7y92SaIwDAOEcLy5SYmquNKbnamJqjTal5J12ELzLYS90ig+wlv2Mzf7bSAxogCr2DUOiBhs1ms8lkYjojAMC5WKw27ckq1MaUHHvJ351VqL/+1u7ualbnCH91iwpS92NT9ZsFePLfNqCeo9A7CIUeaDzm/LpXgd7uuq5npNFRAAA4QX5phTan5lWV/NRcbUzJUc5JrscP8/eoNorfpXmAvNxZVR+oTyj0DkKhBxqHJTuzNG7+WrmYTXrj5h4a1jHM6EgAAJyWzWbTgaPF2piaow0Hqkbxd6QXyGKt/qu9i9mkDs38qpX86BBvRvEBJ0ahdxAKPdA42Gw2/f2zzfps/UF5uJr1vwl91Cs62OhYAADUSEm5RVsO5dmn6m9IyVFWQdkJxwV5uyn+uBX14yIDWU8GcCIUegeh0AONR6XFqrveW6/FO7Pk7+mqTyb1U/tw/t0DAOovm82m9LzSquvwj03V33IoT+WV1mrHmUxSTBNf+3X43aKCFNPUl23zAINQ6B2EQg80LiXlFt3y1u9adyBHYf4e+mzSBYoM9jY6FgAADlNeadWO9PzjrsXPVUr2idvm+Xq4Ki4ywD5VPz4yUCG+bJsHnA8Uegeh0AONT15xha57Y5V2ZRaodaiPvp86kMWEAAAN2pHCMiUem6K/MSVXmw7mqrjccsJxkcFe6tjMXx2a+dv/bBHkxfX4gINR6B2EQg80Thl5pbr2jZUa37+Vbu/fyug4AACcVxarTUmZBdWm6u/JKjzpsX6ervaC/0fJjw3zlacbH4YDtUWhdxAKPdB4lZRbGJkHAOCYvOIKbUvL0/b0fG1Pz9eO9ALtySpQheXEKuFiNqlNEx91OFbw/yj8TfyYsg+cDQq9g1DoAUhVv8S8uypZky+KkZkFggAAkFR1Pf6erELtSM/XDnvRz1dOccVJjw/19VCHZn7qGPHnaH7rUB+5upjPc3LAudWkh7qep0wAUC9ZrDbd9NZqbT2UryOFZZp+RSeuFQQAQJK7q7mqnEf8WThsNpsy88u0PT1PO9ILqkp+Wr72Hy3SkcIy/ba7TL/tPlLtHO3C/KqK/rGS376ZvwK82EYPOBuM0J8GI/QAJOmbTWm676ONstmkBy5uqylDY42OBABAvVJcXqldGQX2Ufwd6QXakZ5/0sX3JKlFkFe16fodm/krMpgF+NA4MOXeQSj0AP4wf8V+Tf92uyTpX2O66MY+UQYnAgCgfrNabUrJLq42XX9HeoEO5Zac9HhfD1d1aOZXrei3C/djAT40OBR6B6HQAzjeiz/v0qtL9shskl67qbtGdG5mdCQAABqc3OJy+wj+H0V/d2ahyi3WE441m6RWoT7qGBFgL/udji3Ax2g+6isKvYNQ6AEcz2az6R9fbtGHa1Ll7mLWO3f0Vr82IUbHAgCgwauwWLX3cKF9FH97WlXRP1pUftLjQ3zcqxbda+KjqGBvRQZ72//09WAZMTg3FsUDgDpgMpn0zOjOOlpYrm1p+Wrqz/Y7AACcD24uZrUP91f7cH+N6Vb1mM1m0+GCsmpb6e1Iz9e+w4U6WlSu5XuOaPmeIyecK8TH3V7wo44r+lEh3gr395QLO9qgHmGE/jQYoQdwMqUVFuWVVCjM39PoKAAA4C9Kyi1KyizQzox8HTharJTsYqVmV/15qi31/uDmYlKLoD9G9L3shb9FUFXh9/dk9X3UPabcOwiFHsDZWLIzS1Eh3mrTxNfoKAAA4DTySyuUelzBr7qVKDW7WAdzilVhOX01CvR2qzaF//hbswBPubqYz9N3goaMQu8gFHoAZ7JizxHdPm+NAr3d9eGdfRXTlFIPAEB9ZLHalJFfqpSj1Qt/ak7V/SOFJ79e/w8uZpOaB3qdsvAHeDO6j7NDoXcQCj2AMzlaWKab3vxdOzMKFOrroY8m9lFMUz+jYwEAAAcrKqtUak6xUv4yjb+q9JeovPLEVfiP5+/pqqiQP6/Zjwz6s+xHBHrJ3ZXRfVSh0DsIhR7A2cguKteNc1fbS/2Hd/ZRbBilHgCAxsJqtSmroOy4afzF1ab2ZxWUnfbrzSapWUDV6H50qLeign0UHVJ13X7LEB9W5m9kKPQOQqEHcLZyisp145u/a0d6vkJ9q6bfU+oBAIBUtVDfwZziEwr/H38vrTj96H6or4dahnhX3YJ9jpV+b0WH+CjQ200mEyvzNyQUegeh0AOoiZyict305u/anp6vEB93fTNlgJoHehkdCwAAODGbzabDhWVKzS7WgaN/3Ip04Nj97KLTX7vv5+mq6BAfRYV4K/rYiH7LYG9Fh/qoqZ8HZb8eotA7CIUeQE3lFleV+g7N/PXvq7vKzF62AADgHOSXVijlaLGSjxb9WfaPFf+M/NLTfq2nm1ktg/8s+1EhVVP5Wwb7KCKQVfmdFYXeQSj0AGqjoLRC3u6ucqHMAwCAOlRaYVFKdrGSjxRV/Xlc2T+UWyKL9dRVz9VsUosgr6oR/Woj+95qEeQtTzeX8/id4Hg16aGsrgAADubn+ee2NJUWq576drtu7BOlDs34YBAAADiOp5uL2ob5qe1J1u2psFh1KKdEyUePlf0jxUrJLlLysVX6yyutSj5arOSjxSd8rckkNfP3rF72/7+9+w5vq7r/OP6R5D0T2/HK3iF7kIQsICGQBMooUPbes1BKS0sH9NdBKW1pCy5t2ausltVCWSEBssiGJGTvZTux472l+/vjyJLlFTuxfSX7/XoePbq690j52opsf+4595zaa/iZpC+o0EPfiKysLGVlZcntdmvLli300AM4Zn/6ZIv+9MlWdY8J1z9vPIlQDwAAbOfxWMouqjBh3xvq9+SXekN/mUoqa5p9fkpchG9SvoGpcd6TCnHq3T2Gyw3bAEPu2whD7gEcr8Lyal359Jf6el+huseE6+UbTtLwTH6eAACA4GRZlvJKq3zX6u/KK9OePH/PfnOT9EWHuzQoNU6D0+I01DtyYEh6vDITo5icrxUI9G2EQA+gLRSWV+uqp7/UV4R6AAAQ4grLzSR9u/NLtetwqbbmlmhLTom255aoyt348ntxkWEalGpC/uA006M/ND2eWfibQKBvIwR6AG2lqKJaVz69XF/tLVC3mHC9fMNkjchMtLssAACANlHj9mh3fpm25hRrc3aJtuQWa2tOsXYcKlVNE5PzJUSF+Xrxh9QO3U+PV0pcZAdXH1wI9G2EQA+gLRVVVOuqp5dr7d4CpSVE6rMfzGQGWQAA0KmZyfdKtSWnWFuyi7Ulp0Rbcoq1K69UTU3CnxQboSHenvzBafHe4ftx6hYT0bHF24RA30YI9ADaWlFFtW56YaVuPXWQThnSw+5yAAAAbFFR7daOQ96gn+MP+nuPlKmphNojPtI3bH+oN+wPSYsLWGGoMyDQtxECPYD2YFlWwPViHo/FjLAAAACSyqvc2pZbos05Zsh+bdjfX1De5HMyE6N84b52Gb/BaXGKiQjN5fUI9G2EQA+gve04VKJbXlql339njEb36mZ3OQAAAEGppLImIODX9uznFFU2+ZzeSdEakuodtp8ep8Gp8RqUGhf0lzwS6NsIgR5Ae7vt5VV6f122EqLC9NINkwn1AAAArVBYVq2tucXeHv0Sbc4u1tbcYh0uaXx5PadDevrqiZo5LLWDK2251uTQ0ByDAACdxO8uHKPcokqt3H1Elz/1pV66frLG9O5md1kAAAAhITEmXCf2S9KJ/ZIC9ueVVGpLTom25np79b0z7xeUVat3UrRN1bY9euibQQ89gI5QUlmja59drhW7jig+KkwvXj9ZYwn1AAAAbcqyLB0qqVRybKRcQTx/UWtyqLODagIANCEuMkzPXTtJk/olqbiiRlc+9aXW7Dlid1kAAACdisPhUGp8VFCH+dYi0ANAEIiNDNOz107UpP5JKq6s0SMfbhYDqAAAANAcAj0ABInYyDA9e81EXTWlr564fELA0nYAAABAfQR6AAgisZFh+r9zRyoxJty3L7e4wsaKAAAAEKwI9AAQxJ5ZtFOzfv+ZVu7Kt7sUAAAABBkCPQAEKbfH0qebclVSWaOrn1muFYR6AAAA1EGgB4Ag5XI69ORVJ2r6oBSVVrl19TPLtXwnoR4AAAAGgR4Aglh0hEtPXW1CfVmVW9c8u1xf7sizuywAAAAEAQI9AAS5qHAT6mcMNqH+2udWEOoBAABAoAeAUBAV7tKTV/lD/dq9BXaXBAAAAJuF2V0AAKBlakP9hxuyde7YnnaXAwAAAJvRQw8AISQq3BUQ5ksqa7RmzxEbKwIAAIBdCPQAEKJKKmt0zTPLdemTy7Rk22G7ywEAAEAHI9ADQIgKczoUHxWmimqPrnt+hRYT6gEAALoUAj0AhKiocJf+duUEzRzaw4T651Zo0VZCPQAAQFdBoAeAEBYZZkL9rGGpqqzx6PrnV+iLrYfsLgsAAAAdgEAPACEuMsylJ64Yr9O8of6G51cS6gEAALoAAj0AdAKRYS799Yrxmn1CqmIjw9QjPtLukgAAANDOWIceADqJyDCX/nr5BB0oKFe/lFi7ywEAAEA7o4ceADqRiDBnQJhftiNPCzfn2lgRAAAA2guBHgA6qW8OFOnaZ1fophdXaQGhHgAAoNMh0ANAJzU4LU4nD0lRVY1HN7+wSgs2EeoBAAA6EwI9AHRS4S6nHr9svOaNTFeV26ObX1ylTzfl2F0WAAAA2giBHgA6sXCXU3+5dJzOHOUP9R9/Q6gHAADoDAj0ANDJhbuc+vMl43TWqAxVuy3d/OJKLduRZ3dZAAAAOE4sWwcAXUC4y6k/XTJWMREu5ZVW6cS+3e0uCQAAAMeJQA8AXUS4y6nfXThaVW6PwlxmgFa12yOPZSkyzGVzdQAAAGgthtwDQBficDh84d2yLP30rfW68unlKiirsrkyAAAAtBaBHgC6qL355Xp/3UEt35mvC55Yor35ZXaXBAAAgFYg0ANAF9UnOUZv3DpF6QlR2n6oVN/+6xJ9va/A7rIAAADQQgR6AOjChqUn6K3bp2pYerwOl1Tq4r8v0/yNLGsHAAAQCgj0ANDFZSRG641bpmjG4BSVV7t14wsr9eryPXaXBQAAgKMg0AMAFB8VrmeumajvTOglh8Oh9MQou0sCAADAUTgsy7LsLiJYFRUVKTExUYWFhUpISLC7HABod5ZlacOBIo3smWh3KQAAAF1Sa3IoPfQAAB+HwxEQ5ncdLtUtL65iWTsAAIAgRKAHADTKsizd+coafbAhm2XtAAAAghCBHgDQKIfDoUe+M1oZiSxrBwAAEIwI9ACAJg1LT9Bbt03TCRkJLGsHAAAQZAj0AIBmpSdG6fWbT9LJQ3r4lrV7adluu8sCAADo8gj0jcjKytLw4cM1ceJEu0sBgKAQHxWup68+URef2FseS3pz9T7VuD12lwUAANClsWxdM1i2DgACWZal55fs0tljMpUcF2l3OQAAAJ0Oy9YBANqFw+HQNdP6B4T511fuZVk7AAAAGxDoAQDH7PUVe/XDf33NsnYAAAA2INADAI7Z6N6JLGsHAABgEwI9AOCYsawdAACAfQj0AIDjUrus3YzBKSxrBwAA0IEI9ACA4xYfFa5nrpmoi07sJY8l/fTt9Vq/v9DusgAAADq1MLsLAAB0DuEupx6+YLR6dY+R0yGN7Jlod0kAAACdGoEeANBmHA6Hvnva4IB9ucUVinA51S0mwqaqAAAAOieG3AMA2k1pZY2ue24Fy9oBAAC0AwI9AKDd5BZXKq+kimXtAAAA2gGBHgDQbvqnxOqt26ZpWHo8y9oBAAC0MQI9AKBdpSdG6Y1bprCsHQAAQBsj0AMA2l1jy9o9vWin3WUBAACENGa5BwB0iLrL2r24bLfOGJ5md0kAAAAhzWFZlmV3EcGqqKhIiYmJKiwsVEJCgt3lAECnUVhWrcSYcN/jGrdHYS4GjQEAALQmh/LXEwCgw9UN8x9tyNa8P3/BsnYAAACtRKAHANimxu3Rbz/YpK25Jfr2XxezrB0AAEArEOgBALYJczn1zxtO0gkZCTpcUsWydgAAAK1AoAcA2Co9MUqv33ySTh7Sw7es3YssawcAAHBUBHoAgO3io8L19NUn6uITe8tjST97e70e+t9GMW8rAABA0wj0AICgEO5y6rcXjNL3Tx8iSaqocttcEQAAQHBjHXoAQNBwOBy687TBGtenu6YMTJbD4bC7JAAAgKBFDz0AIOhMH5wil9OE+aoaj255cZWW7cizuSoAAIDgQqAHAAS1pxft1AcbsnXZk8uUtWCbPB6uqwcAAJAI9ACAIHf11L46f3xPeSzpkQ8365rnViivpNLusgAAAGxHoAcABLWYiDD98aKx+t2FoxUV7tTnWw7pzL98oeU78+0uDQAAwFYEegBASLjoxN565/bpGtgjVjlFlbr0yWX616p9dpcFAABgGwI9ACBkDE2P17t3TNd5YzMVHe7ShL7d7S4JAADANixbBwAIKbGRYXr04rHad6RcvZNifPtziiqUlhBlY2UAAAAdix56AEDIcTgcAWF+ybbDmvHwAv3j8+3Mgg8AALoMAj0AIOR99E2Oqtwe/eb9TbrxhZUqKKuyuyQAAIB2R6AHAIS8B84erl9/e6QiwpyavylXZ/1lkVbvOWJ3WQAAAO2KQA8ACHkOh0OXT+6rN2+dqn7JMdpfUK6L/rZUT32xQ5bFEHwAANA5EegBAJ3GyJ6J+s+d03XWqAzVeCz96r2N+mzLIbvLAgAAaBfMcg8A6FTio8L1+GXjdNKyJK3bX6hThvSwuyQAAIB2QaAHAHQ6DodDV07pF7CvoKxK76/L1qWTesvhcNhTGAAAQBtiyD0AoNOzLEv3vvG17n9rnW55aZUKy6vtLgkAAOC4EegBAF3CjMEpCnc59OGGHH3rsS/09b4Cu0sCAAA4LgR6AECn53A4dPXUfvr3rVPVOylae/PLdeETS/X8kl3Mgg8AAEIWgR4A0GWM7tVN/71zhuaMSFOV26MH3t2g2/+5WkUVDMEHAAChh0APAOhSEqPD9bcrJuhn3xquMKdDX+0tlMdDLz0AAAg9zHIPAOhyHA6Hrp/eX+P7dJPT4VC3mAhJ8g2/ZxZ8AAAQCuihBwB0WeP6dNeY3t18j19bsVfffXWtihmCDwAAQgA99AAASCosq9av3tuoksoard9fqKzLxmt4ZoLdZQEAADSJHnoAACQlxoTr+esmKTMxSjsPl+q8vy7WP7/cwyz4AAAgaBHoAQDwmtC3u9777gzNHNpDVTUe3f/WOt392lqVVtbYXRoAAEADBHoAAOroHhuhp6+eqPvmDpPL6dA7aw/ovKzFqqh2210aAABAAAI9AAD1OJ0O3XrqQL1600lKT4jSt0ZnKircZXdZAAAAAZgUDwCAJkzsl6T/3TVDCdHhvn0HCsrVLSZcMRH8CgUAAPaihx4AgGZ0j42Qy2nWpa+oduuG51fqnMcXa0tOsc2VAQCAro5ADwBAC+07UqbDJZXalluicx5fpH+t2md3SQAAoAsj0AMA0EKDUuP1/l0zNGNwiiqqPbr3ja/0gze+UnkVE+YBAICOR6AHAKAVUuIi9fy1k/T904fI6ZDeWLVP52Yt0rZchuADAICORaAHAKCVnE6H7jxtsF66YbJ6xEdqS06JHnh3g91lAQCALoZADwDAMZo6MEXvfXe65o1M18MXjLa7HAAA0MUQ6AEAOA6p8VF64ooJ6tU9xrfvsflbtWJXvo1VAQCAroBFdAEAaEPLduTpDx9vkT6Wrjypr344d6jio8KP/kQAAIBWooceAIA2dEJGgi6Z2FuS9OKy3Zrz6OdasCnX5qoAAEBnRKAHAKANJUaH67cXjNbLN0xWn6QYHSis0LXPrdDdr65RfmmV3eUBAIBOhEAPAEA7mDYoRR/efbJunNFfTof09toDuujvS+XxWHaXBgAAOgkCPQAA7SQ6wqWfnDVcb902TcPS43XnrEFyOh12lwUAADoJJsUDAKCdjendTf+5c7rC6oT5jzZkK7e4UpdN6kPIBwAAx4RADwBABwh3+QfFFZZV6/631utwSaXe/eqAfnv+KA3oEWdjdQAAIBQx5B4AgA4WFxWm22cOVHS4S8t35mvun7/QEwu3q8btsbs0AAAQQgj0AAB0MJfToWun9ddH3ztZMwanqKrGo4c/2KRzsxZr/f5Cu8sDAAAhgkAPAIBNeifF6IXrJun33xmjxOhwbThQpPOyFmtPXpndpQEAgBDANfQAANjI4XDowgm9dPKQFD347gbFRISpT3KM3WUBAIAQQKAHACAIpMZH6a+XT1B1nevoDxSU68kvduie04coPircxuoAAEAwYsg9AABBpO5s+D99e72eXbxLZzz6uT7dlGNjVQAAIBgR6AEACFLXT++vPkkxOlhYoeueW6m7Xl2jvJJKu8sCAABBgkAPAECQmjYoRR/efbJunNFfTof0ztoDOv3Rz/XO2v2yLMvu8gAAgM0I9AAABLHoCJd+ctZwvXXbNA1Lj1d+aZXuenWt3li5z+7SAACAzQj0AACEgDG9u+ndO6brntOHaEhanM4Zm2l3SQAAwGYEegAAQkREmFPfPW2w3vvuDEWFuyRJbo+lB9/doB2HSmyuDgAAdDQCPQAAIabuTPjPL9ml55bs0tw/f6EnFm5XTZ1l7wAAQOdGoO8stn4slebZXQUAoIOdMSJNJw/poaoajx7+YJPOzVqs9fsL7S4LAAB0AAJ9Z7Dqeenl70ivXSHVsJwRAHQlvbrH6PlrJ+oP3xmjxOhwbThQpHOzFuvhDzapotptd3kAAKAddfpAv3fvXp166qkaPny4Ro8erTfeeMPuktpe78lSZLy0Z4n03+9JLGUEAF2Kw+HQBRN66ZN7TtFZozPk9lh6YuF23fvGV3aXBgAA2pHD6uQL2R48eFA5OTkaO3assrOzNWHCBG3ZskWxsbFHfW5RUZESExNVWFiohISEDqj2OGz7xPTSWx5p9i+k6XfbXREAwCYfbcjWL/7zjZ66+kSdkBHkv78AAECA1uTQTt9Dn5GRobFjx0qS0tPTlZKSovz8fHuLag+DZktzHzbbnzwobfyvreUAAOxzxoh0LfzBqQFh/qkvdujTTTk2VgUAANqa7YH+888/19lnn63MzEw5HA69/fbbDdpkZWWpX79+ioqK0uTJk7V8+fJj+rdWrVolt9ut3r17H2fVQWryTdLEGyRZ0ps3SgcZagkAXVXdmfC/OVCkh/63Sdc9t1J3vbpGeSXMtwIAQGdge6AvLS3VmDFjlJWV1ejx1157Tffcc48eeOABrV69WmPGjNGcOXOUm5vrazN27FiNHDmywe3AgQO+Nvn5+brqqqv0j3/8o92/JlvNfVgaOEuqLpNeuVQqzra7IgCAzfqnxOq6af3kdEjvrD2g0x/9XO+s3a9OftUdAACdXlBdQ+9wOPTWW2/pvPPO8+2bPHmyJk6cqMcff1yS5PF41Lt3b91555360Y9+1KLXrays1Omnn64bb7xRV155ZbPtKiv9vRZFRUXq3bt3aFxDX1d5gfT06dLhLVLmeOna96XwaLurAgDY7Ku9Bbrv319rU3axJGnWsFT96ryRyuzG7wgAAIJFp7mGvqqqSqtWrdLs2bN9+5xOp2bPnq2lS5e26DUsy9I111yjWbNmNRvmJemhhx5SYmKi7xayQ/Oju0mXvSZFJ0kHVktv3yp5PHZXBQCw2Zje3fTuHdN1z+lDFOFy6tNNuTovazHL2wEAEKKCOtAfPnxYbrdbaWlpAfvT0tKUnd2yoeSLFy/Wa6+9prfffltjx47V2LFjtW7dukbb/vjHP1ZhYaHvtnfv3uP+GmyTNEC6+CXJGS5teEv67Ld2VwQACAIRYU5997TBeu+70zW+TzfdeupARYW7JJmT4EE0cA8AABxFmN0FtLfp06fL08Le6cjISEVGRrZzRR2o3zTp7D9J79wuffawlDJEGnWh3VUBAILA4LR4vXHLVDnq7Ju/MVd//3y7fjTvBE3o29222gAAQMsEdQ99SkqKXC6XcnICl9nJyclRenq6TVWFmHFXSFO/a7bfvk3au8LeegAAQcPldMjp9Ef6v3y6VSt2HdEFTyzRzS+u1PZDJTZWBwAAjiaoA31ERIQmTJig+fPn+/Z5PB7Nnz9fU6ZMsbGyEDP7QWnomZK7Unr1Uqlgj90VAQCC0N+vnKCLT+wtp0P6cEOOznj0c93/1jrlFlXYXRoAAGiE7YG+pKREa9eu1dq1ayVJO3fu1Nq1a7Vnjwmd99xzj5588kk9//zz2rhxo2699VaVlpbq2muvtbHqEON0Sec/KaWNkkoPSf+8RKostrsqAECQyUiM1sMXjtYHd5+s2Sekye2x9M8v9+iURxbqpWW77S4PAADUY/uydQsXLtTMmTMb7L/66qv13HPPSZIef/xxPfLII8rOztbYsWP1l7/8RZMnT2732lqzXEBIKNgrPTlLKs2VhsyVLvmnCfsAADRi+c58PfS/jVqzp0BPXXWiZg9PO/qTAADAcWlNDrU90AezThfoJWnfSulZ7/D7KXdIc35td0UAgCBmWZaWbM/T1IHJcjjM9fZvrNyriDCnzh6dGXANPgAAOH6dZh16tINeJ0rffsJsL31cWv2CvfUAAIKaw+HQtEEpvjBfWFatX723UXe9ulbnZi3W4m2Hba4QAICui0DfFY28QDr1x2b7v9+Tdn5hbz0AgJAREebUDdP7Ky4yTOv2F+ryp77UVc8s14YDhXaXBgBAl8OQ+2Z0yiH3tSxL+vf10vp/S9HdpRvmS8kD7a4KABAi8koq9din2/Tyl7tV7bbkcEjnje2pH8wZqsxu0XaXBwBAyGLIPY7O4ZDOzZJ6niiVH5H+ebG5BwCgBZLjIvXgOSP0yT2n6JwxmbIs6Z21+1VaWWN3aQAAdBkE+q4sPNrMdJ/QS8rbKr1+teSutrsqAEAI6Zscq79cOk7/uWO6fnrWcA1Oi/cdW7ApV+VVbhurAwCgcyPQNyIrK0vDhw/XxIkT7S6l/cWnSZe9KoXHSjs/k/73QzMcHwCAVhjVK1HXTe/ve7wtt1jXP79Cp/5+gV5dvkc1bo+N1QEA0DlxDX0zOvU19PVtel969TJJljT3YemkW+yuCAAQwpZuz9O9b3yl/QXlkqTBqXH64dxhmn1Cqm/GfAAA0BDr0LeRLhXoJWnxn6WPfy45nNJlr0uDT7e7IgBACKuscevFpbv1+IJtKigzl3RN7NddP5o3TBP6JtlcHQAAwYlJ8XBspn5XGnuFZHmkN66VcjfaXREAIIRFhrl0w4wB+uwHM3XbqQMVGebUil1HdN1zK1VWxeR5AAAcLwI9/BwO6VuPSn2nSVXFZub70sN2VwUACHGJ0eH64dxh+uwHM3XJxN66c9YgxUSESZIsy1JeSaXNFQIAEJoI9AgUFiFd/JLUvb9UsFt69XKphj+0AADHLz0xSr+9YLRumDHAt2/B5lxNe/hT/f7DzSqqYKUVAABag0CPhmKSzDX0kYnS3mXSf+5i5nsAQLv4YH22Kqo9enzBNp3yuwV6ZtFOVdaw1B0AAC1BoEfjegyRLnpOcrikr16RFv3R7ooAAJ3QwxeM1t+vnKABPWJ1pKxa//ffbzT7j5/pnbX75fFwMhkAgOYwy30zutws941Z/qT0/r1m+6IXpeHn2FsPAKBTqnF79PrKffrTJ1uUW2wu9Tp7TKYeu3SczZUBANCxmOUebWfSjdKkm8z2WzdLB9baWg4AoHMKczl12eQ+WviDU3XvGUMUFxmms0dn+I7T/wAAQEP00DeDHnovd430z4uk7fOl+AzpxgVSQsbRnwcAwDEqKKtSYnS4HA6HJOmpL3Zo/f5Cff+MoeqdFGNzdQAAtB966NG2XGHSd56VUoZKxQelVy6RqsrsrgoA0Il1i4nwhfmKareyFmzT22sPaNYfFuoX/9mgwyx1BwAAgR4tFJUoXfaqFJ0kHVwrvX2L5PHYXRUAoAuICnfpxesna/qgFFW7LT27eJem/vZT/ejfX2trTrHd5QEAYBsCPVouaYB0ycuSM1z65h1p4W/srggA0EWM7Jmol26YrBevn6QxvbupqsajV1fs1emPfq6nF+20uzwAAGxBoG9EVlaWhg8frokTJ9pdSvDpO1U65y9m+/NHpK9es7ceAECXMmNwD71921T965YpmjsiXS6nQzMGp/iO55VUso49AKDLYFK8ZjApXjM+fkBa/CfJFSFd/V+pz2S7KwIAdEG5RRVKTYjyPb771TVatC1PV0/pq8tP6quk2AgbqwMAoPWYFA/t77QHpGHfktxV0quXSUd2210RAKALqhvmK2vcWrXniA6XVOoPH2/RlIfm6/631mlbbomNFQIA0H7ooW8GPfRHUVkiPTtXyl4npQ6XrvtQiuL7BACwT7Xbo/fXHdRTX+zUuv2Fvv2zhqXq1lMHamK/JBurAwDg6OihR8eIjJMufU2KS5Nyv5H+fYPk4bpFAIB9wl1OnTu2p969Y5pev3mKzhieJodD+nRTrlbuOmJ3eQAAtCkCPY5PYk/p0leksChp64fSRz+zuyIAAORwODSpf5L+cdWJWvD9U3XN1H66bFIf3/FPN+Uoa8E2HSmtsrFKAACOD0Pum8GQ+1ZY/6b0r2vN9tl/liZcY2s5AAA059t/Xaw1ewoUFe7UhRN66bpp/TWgR5zdZQEAwJB72GDk+dKp95vt974v7fjM3noAAGiCZVm6akpfDc9IUEW1Ry8t26NZf/hMNzy/Qku354m+DgBAqKCHvhn00LeSZZnr6Nf/S4pKlG74VEoZZHdVAAA0yrIsLd2Rp2cW7dQnG3N9+y8+sbcevnC0jZUBALoyeuhhD4dDOjdL6jVRqiiU/nmRVJZvd1UAADTK4XBo6sAUPXX1RM3//im64qQ+igp36tShPXxtiiuqVVhWbWOVAAA0jR76ZtBDf4xKcqUnZ0mFe6X+J0tXvCm5wu2uCgCAo8ovrVJCVJjCXKbPI2vBNj3+6TZddGIvXTutv/qlxNpcIQCgs6OHHvaKS5UufVWKiJN2fi69f68Zjg8AQJBLio3whXlJWr4zX+XVbj2/dLdm/mGhbnphpZbvzOc6ewBAUKCHvhn00B+nzf+TXrlUkiXNeUiacpvdFQEA0CqWZWnJ9jw99cUOLdh8yLd/dK9E3XzyQJ01OsPG6gAAnRE99AgOQ+dJZ/zSbH/0E2nLR/bWAwBAKzkcDk0blKJnr52kT+45WZdO6qPIMKe+3leo+Rtz7C4PANDF0UPfDHro24BlSe/eKa15UYqIl67/SEobbndVAAAcs7ySSr385R7NPiFNwzPN3webs4v1yvI9um5af/VJjrG5QgBAKGtNDiXQN4NA30ZqqqQXvy3tXiQl9pFu/FSK63H05wEAECJ++K+v9PrKfXI6pDOGp+uGGf01oW93ORwOu0sDAIQYhtwjuIRFSBe/KCUNkAr3SK9eJlVX2F0VAABt5tyxPXXKkB7yWNIHG7J14d+W6ry/LtF/vjqgGrfH7vIAAJ0UPfSNyMrKUlZWltxut7Zs2UIPfVs5tEV6erZZo37URdL5/zBr1wMA0ElsySnWM4t26s01+1VVY4L8mF6Jevv2afTWAwBahCH3bYQh9+1g+wLppQskyy3N+ql08g/srggAgDZ3uKRSLy3brReX7tZVU/rprtmDJUlVNR59/E2OTjshVVHhLpurBAAEIwJ9GyHQt5MVT0vv3WO2R5wvnf4LqVsfe2sCAKAdVFS75fZYio0MkyR9uCFbN7+4SvFRYfrW6ExdOKGnxvfhWnsAgF9rcmhYB9UE+E28XirOlj5/RNrwprT5fWnKHdL070mRcXZXBwBAm6nfC19R7VZmYpQOFFboleV79MryPeqXHKPzx/fSt8f1VO8kZsgHALQcPfTNoIe+nR38Wvrgx2b2e0mKS5dO+7k05lLJyXyNAIDOyeOxtGxHnv61ep8+WJ+tsiq379jnP5jJsncA0MUx5L6NEOg7gGVJm/4rffRT6cgusy9jjDTnIanfNFtLAwCgvZVW1ujDDdn69+p9Kq6o0bt3TPcde3HpLvVJjtX0QSlyORmSDwBdBYG+jRDoO1BNpfTl380w/Mois2/4udLp/yd172draQAAdITKGrciw8wQ/aKKak381SeqrPEoLSFS543rqQvG99KQtHibqwQAtDcCfRsh0Nug5JC04NfS6uclyyO5IqSTbpNmfF+K4j0AAHQNh4or9dinW/XuVwdUUFbt2z+qZ6LOH99T54zJVHJcpI0VAgDaC4G+jRDobZSzwVxfv/Mz8zi2hzTrZ9K4KyQny/wAALqGqhqPPt2UqzdX79OCzbmqdps/2340b5huOWWgzdUBANoDgb6NEOhtZlnSlg+kD38i5W83+9JGSXN/I/U/2d7aAADoYPmlVfrPVwf05pr9+vsVE5SeGCVJ+mB9thZtO6Tzx/fSuN7dWAIPAEIcgb6NEOiDRE2VtOIp6bPfShWFZt+wb5nr65PpnQAAdG1XPv2lvth6WJI0ICVW54/vqW+P76We3aJtrgwAcCwI9G2EQB9kSvOkhQ9JK5+RLLfkDJdOukU6+QdSVKLd1QEAYItFWw/r36v36X/rD6qi2uPbP2VAsi6Y0EsXjO9Jrz0AhBACfRsh0Aep3I3Sh/dL2z81j2NSpJn3S+Ovllxh9tYGAIBNSipr9L91B/Xm6v1auiNPkjSpf5Jev3mKr41lWYR7AAhyBPo2QqAPYpYlbf1Y+ugn0uEtZl/qcGnOr6WBs+ytDQAAm+07Uqa31+zXoNQ4zR2ZIUnKK6nUeX9drG+NztQF43tpUGqczVUCABpDoG8jBPoQ4K42Q/AX/EaqKDD7hsyTzviVlDLI1tIAAAgmzy/ZpQfe3eB7PKZXoi6Y0Etnj85U99gIGysDANRFoG8jBPoQUpYvffY7acWTkqdGcoZJk26STvmhFN3d7uoAALBdZY1bn27M1b9X79PCzYdU4zF/Aoa7HJo1LFU/OXO4+iTH2FwlAIBA30YI9CHo0Bbpo59KWz80j6O7SzN/Ik24luvrAQDwOlxSqXfXHtCba/Zp/f4ihbscWn7/bF9PfWFZtRKiw7jeHgBsQKBvIwT6ELZtvlm//tBG87jHMOmMX0uDZ9tbFwAAQWZzdrHW7S/UhRN6+fZd8MQS5RRVaNawVM0clqopA5IVFe6ysUoA6DoI9McpKytLWVlZcrvd2rJlC4E+VLlrpNXPSZ/+WirPN/sGnW4mzusx1NbSAAAIVnkllZr28KcBS+BFhTs1fVCKZg5L1axhqcpIZI17AGgvBPo2Qg99J1FeIH3+iPTl3yVPteRwSRNvkE79kRSTZHd1AAAEnbKqGi3Zlqf5m3K1YFOusosqfMe+NTpDj182XpJZBs9jSS4nQ/MBoK0Q6NsIgb6TydsuffQzafN75nFUNxPqJ94gucJtLQ0AgGBlWZY2HizWgs25mr8xR1dN6afzxvWUJG3NKdZFf1+qU4eaofmnDO6hxBh+pwLA8SDQtxECfSe1Y6H0wf1SrnfpnuTBZpm7IXMkJv8BAKDFnvpih3713kbfY5fToQl9u2uWd2j+4NQ4JtYDgFYi0LcRAn0n5nFLq1+QPv2VVHbY7BswU5rzGyltuL21AQAQImrcHq3afUSfbjZD87fklAQcf+n6yZo+OMWm6gAgNBHo2wiBvguoKJS++IO07AnJXSU5nGaJu5n3S7H8AQIAQGvszS/Tgs25+nRTrtbuLdCyH5/mmx3/T59s0fr9hUysBwBHQaBvIwT6LiR/h/Txz6WN/zGPIxOlU34gTbpZCouwtzYAAEJQtdujcJfT9/j0P36mrbn+Hvxh6fE67QQT7sf27s7EegDgRaBvIwT6LmjXIumDH0vZX5vHSQPM9fVDz+T6egAAjsM3B4p8vfer9xxR3b9Ah6TF6aPvnWJfcQAQRAj0bYRA30V53NLaf0rz/08qzTX7+s2Q5j4kpY9q5nkeyVNT7+Zu5eNjbdPE8fAYqc9JUp8pUnS3Dvn2AQBwNPmlVfpsS64+3XRIn23O1Rkj0vX774yRJHk8lm5+aZXG9+mu005gYj0AXQ+Bvo0Q6Lu4ymJp0aPSkscld6W5vj42telQrWD+KDnMyYh+M6R+06S+U6Xo7nYXBQCAatwelVTWqFuMucTt630FOufxxb7jPbtFm1nzT0jVlAHJvmvyAaCzItC3EQI9JElHdkufPCBteOvYnu9wSc4w/81VZ9tZ71iDx821CT/6a5TkSLsXS3nb6hclpY80Ab+vN+DHJB33twoAgOOVV1Kp99Yd1KebcrVke56qajy+Y1HhTv3fOSN10cTeNlYIAO2LQN9GCPQIcGSXVFHU+vAdDMMEi7PN/AC7FpmAf3hLvQYOKW2k6b3vN92EfAI+AMBmZVU1WrItz7cs3sHCCr1600k6aUCyJGnlrnx9uilXs4alalwfJtYD0DkQ6NsIgR6dVnGOtHuRtGuxCfmHNzdskzrChPt+00zAZxk/AICNLMvSpuxiDUqN882e//N31uuFpbslSYnR4Zo6MFlTB6Vo2sBk9U+J5dp7ACGJQN9GCPToMkpyTc99bS/+oU0N26QO9/fe95tOwAcA2O6Tb3L0zlcH9NnmXBVV1AQcy0iM0vvfnaHusSw/CyC0EOjbCIEeXVbJoXoBf2PDNj1OqDNEf7oU16Pj6wQAQGZiva/3F2rx1sNavP2wVu8uUI/4SC26b6avl/6X//1G1W6Ppg5M0ZQByUqMCbe5agBoHIG+jRDoAa/Sw4EBP/ebhm1ShnqH6HtvcakdXycAAJLKq9zad6RMg9PiJUluj6Vx//eRrxff6ZBG9kzU1IEpmjYoWSf2TVJ0BLPnAwgOBPo2QqAHmlCa5w/4uxdLOesbtkkZ4g/3fadL8WkdXycAADI9+J9szNWS7Ye1eNthbT9UGnB8Uv8kvX7zFN9jt8digj0AtiHQtxECPdBCZfnegO8N+TnrGrZJHlwn4E+TEjI6vk4AACRlF1Z4w32elmw/rO9M6KV7zhgqSSosq9aM332qif2SzAR7g5I1NC2eCfYAdBgCfRsJqUBvWdKB1VLm+OBYJg1dW1m+tHuJN+R/IWWvl1TvR03yIO8EezPMtfgJmbaUCgDo2izLUrXbUkSYmTn/ow3ZuunFVQFtUuIiNGWgmT1/5rBUpSVE2VEqgC6CQN9GQirQ71okPXeW1GuiNO1uaeiZktNpd1WAUX5E2r3Uew3+F1L2OjUI+EkDTMCPSzUnqCR/m0Yf1z+mZtp6H7fodVvZNrq7NPICKXNs4187ACCkuD2WvjlQpMXe4fkrduWrotrjO/7Q+aN06aQ+kqTC8mpVuz1KiYu0q1wAnRCBvo2EVKBf8ZT0wf2Su9I8ThkiTbtLGnWRFMZyLQgy5UekPcv8k+xlfy1ZnqM/L5ilj5LGXSmN+o4Uk2R3NQCANlJZ49aaPQVasu2wFm/P06MXjVWf5BhJ0vNLdumBdzdoWHq8b4K9yQOSFRcZZnPVAEIZgb6NhFSgl8xa4suekFY8LVUWmn3xmdKU26RJN0lhnD1GkCovMAF/75dSdZl3p/fSEd8lJPUfN3eskcdt2lb+xznrpU3vSe4qs8sVIQ37ljTuCmnAqZKTWZMBoLP6xX826NnFuwL2uZwOjemVqOmDUnTd9P7qFkPHCoDWIdC3kZAL9LUqiqRVz0nL/ioVH5SSBkp3rCBYAO2lLF9a9y9pzQveywm8EnpJYy8zt6T+9tUHAGg3h0sqtXR7nm+SvT355sR0mNOhrx44Q7He3vplO/IUGxGm4ZkJzKAPoFkE+uOUlZWlrKwsud1ubdmyJfQCfa2aSunr16SoRGn4uWZfdYW08DfSiddJ3fvZWh7QKR38SlrzkvT161JFgX9//5PNkPwTzpbCo20rDwDQvvbml2nJ9sM6WFihu2cP8e0/+7FFWre/UInR4ZoyIFnTBiVrfN/uGpIWr3AX8x4B8CPQt5GQ7aFvzqrnpP/cJTlc0ohvS9PvNtf+Amhb1RXSpv+acL9joXyT6UUmSqMuMOE+cxyrUgBAF+D2WLr1pVVasj1PJZU1AcciwpyafUKq/nr5BN8+j8eSk158oMsi0LeRThno9y6XFv5W2j7fv2/QbDMzfr/phAugPRTskda+Iq19yWzXSh1hrrUffbEUm2xffQCADlHj9ujr/YVasu2wlu7I09f7ClVcUaOzRmco67LxkkyYn/SbT5TZLVojeyZqlPc2JC3et7QegM6NQN9GOmWgr3XwK2nxn6UNb/lnF+95onT1f6SIGHtrAzorj0fa9bnptf/mXf+qFM5wadiZptd+4CzmuwCALsKyLO3JL1ONx9LAHnGSpJ2HSzXz9wsbtI1wOTU0PV4XjO+pa6YxLwvQmRHo20inDvS18ndISx6X1r5seuiv+Lf/mMfDWvZAeyk/4p1I7yXp4Fr//vhMaeyl0tjLpeSBtpUHALBHbchft79Q6/YXav3+Qq3fX6TC8mpJ0h0zB+neOUMlmQn5rn5muUb1TPT15g9Nj1dUOCeGgVBGoG8jXSLQ1yrJlSqL/QGi6ID07Dxp4g3ShGukyHhbywM6tex10pqXpa9fNUG/Vt/pZkj+8HOkiFj76gMA2MqyLO07Uq51+ws1sEechqabv8sWbM7Vtc+uCGgb5nRocFq8RvVM0IUTemtS/yQ7SgZwHAj0baRLBfr6FvxG+uxhsx2VKE28UZp8ixTXw966gM6splLa/L7ptd82X76J9CLi/RPp9ZzAXBcAAEnSkdIqfbkzz9ubX6T1+wuVX1rlO/67C0frohN7S5K+OVCkZxfv1Khepjd/eEYCPflAkCLQt5EuHehrKs2yW4v/LOVtNfvCokxv4ZQ7WFMbaG+F+/wT6R3Z5d/fY5h3Ir1LOMEGAAhgWZYOFFZo3T4zVP87J/ZS32Qzwuv5Jbv0wLsbfG1dTocGp8b5hurPGZGu9MQou0oHUAeBvo106UBfy+ORNr8nLXpU2r/K7ItMkL6/mcnzgI7g8Ui7F3sn0ntHqik3+51h0pC5ptd+0GzJFWZvnQCAoLZuX6E++ibbd13+4ZKqgOOv3zzFNzx/5a58rdtfqFE9EzU8M0ExEfyOAToSgb6NEOjrsCxp1yJp8Z+k5MHSvN/6j+1fJWWOZxgw0N4qCqX1/zbhvvYEmyTFpUljLjXhPmWQffUBAEKCZVnKLvL35K/bX6jHLhuvuEgT3B98d4OeW7JLkuR0SAN7xPnCfd/kWE0flKLoCIbrA+2FQN9GCPRN8Lj9y2rt+VJ65gwT6Kd/Txr2LWbGBzpCzjcm2H/9qlSW59/fZ4p3Ir3zpMg428oDAISuN1bu1QfrTW9+bnFlg+Nrfna6usdGSDJD+b/eV6g+STHqmxyjPskx6psUo6TYCDno7AGOCYG+jRDoW2DVc9L/7pNqKszj5MHStO9Koy+WwiJtLQ3oEmqqpC0feCfS+1iyPGZ/RJw04tum1773JEbQAACOSW5RhdYfKNS6fUXaklOsQyWVeu2mk3xh/frnVmj+ptwGz4uLDFPvpBj965YpivX2/O88XCqXw6HMblEKc9EBBDSFQN9GCPQtVHJI+vJv0oonzZBgSYrPkE66TZp0kxTOBCtAhyg6IH31ign3+Tv8+5MHm177MZdK8Wn21dcUyzInIjxuyVMjWW6zbXnM49r9dbetRva1+HFj+zwtaNPCx5bbTCIalWjmHImMl6ISzHaU93FkYp1t7/6IOE68AAg5CzfnasOBIu3OK9XuvDLtyS/TwULT0RMXGaZ1D57hC/83PL9Sn2zMkcvpUM9u0aZHPynG17t/+vB0uZz8HAQI9G2EQN9KlcWmx35pllR8UOrWR7pzDZN1AR3NsqQ9S6XVL0rfvC1Vl5n9Dpc0+AwpeaA/QPuCsfvoobq5fQHHa1/T0/y+2n+ndlRBl+eoE/qbOhGQ4D1REN/0fle43V8IgC6uotqtfUfKdLikSicNSPbtv/GFlfpsyyFV1TT8uR8T4dKGX8zxhf+HP9ikgwXl6pMcq75J/qH8PeIjGcqPTo9A30YI9Meodsm7iBhp5AXefVXSp7+UTrxWShpgb31AV1JRJG14y/Ta71tudzXHxuEyIdXhMrP7O2vvw9r4ses4XqPOPodTqi6XKovM9993Xxy4r+62p6btvl9h0c2cFGjsBEGCFNtDShnCpVIA2p3HYym3uNL06OeXaU9emXbnl8npkP58yThfu7l/+lybsosbPD863KWh6fF6+/Zpvn3r9xcqNjJMPbtFKyKMofwIfQT6NkKgb0NrXpbeuc38oTv8PGn63VLGGLurArqWQ5ulDW9LVSUmfDpcgSG02X1hdbad3uOues+p/3xv2yb3hTX9fN/xLvCHmWWZeUgaC/pHOxFQWezfrh2JcaycYVKPYVL6aCl9lPc2Uoru3jZfJwC0wsff5Ghbbon25PuH8h8oKJfHkoamxevD753sazvvz19o48EiOR1SRqIZym+G88dqQI9YzRmRbuNXArQegb6NEOjb0L6V0sKHpG2f+Pf1m2HWz+47Tcocx9B8ADge7hp/4K8b9BucCKh/gqBYKtwnVRQ0/rqJfaSMuiF/lJTYm+v9AXS4qhqP9heUq7SyRiN7Jvr2n//Xxdp4sFjl1e4GzxmSFqePvneK7/EtL65SZY1bvZNi1Kt7tHp1N/e9u8eoW0w4w/kRFAj0bYRA3w6y10mL/yytf9NcOyuZXsD7dpthn5JUsEeKTWUyPQDoKJZlQn32Ou/ta3Mr2NN4+6hEb09+naDfYyjX73vcUtF+6cguKX+ndGSn936XVFUqde8rde9vLj1L8t5368vvO6ANWJalQyWVZgi/dxj/nrxSpSVG6cfzTvC1G/XghyquaPwyp9G9EvXuHdN9j/+9ap9iI12+0J8YTeBHxyDQtxECfTs6slva9F9p9xIzTPTKt/zHnjpdOrhW6nmi1G+a1Heq1HuyFBFrW7kA0CWVF0g56/1B/+DX0qGNjV/z74rwD9mv7dFPG2HCf2dSVWYC+pGdgcH9yC5zAsRd1coXdEgJPb0Bv39g4O/e33+yG8BxsyxLi7Yd1r4j5dp3pEx78839viPlyi2u1KxhqXrmmom+9vXDf3xkmHp6e/XH9+2m204d5DtWVlWjmAhGm6JtEOjbCIHeBu4a6S9jpcK9gfudYVLGWGnYmdKM79tRGQBAMhOfHtpcrzd/nRm+35ju/by9+GP8vfkJmcE7ZN+ypNLDgb3rvu2dUklO8893hvt74rv38wfziNg6r7XDvF7+Tqmq4aRfAWJSAgN+3d79mOTg/T4CIaai2q3Syholx5nJQatqPPr+G1/5gv/hksqA9qcO7aHnrp3kezz6wQ8lyT+Ev86Q/gE9YjWwR1zHfTEIeQT6NkKgt4llmT92di0yPfi7l0iF3mGfJ5wjXfyiv938/zPX3/edKsWm2FczAHRlliUV7K4T8r29+UX7Gm8fneQP9xneoJ88uOPmUnFXm970gLC+y3+rKmn++VGJ3nDtDe3d6/SuJ2SaiR1bwrKksjxvuN9RL+zvkMoON//8iHh/z37SgMDAH5/ZNSaVBDpIeZVb+wvKtdfbo98jLlJzR5rJ9oorqjXqwY+afG798H/Pa2uVHBfhC/+197GR9PDDINC3EQJ9ECnYY4J9fLo04FSzL2+79Nh4f5sew0yw7zvN3BIybCkVAOBVlh8Y8rO/Nr37VsOJq+SKlNKGe4O+9/r8tBFS5DH2alUUNT4sPn+nmS+gsRp8HFJiL29Y79cwuHfUzP+1X0NA4PfeivZLauZPOFekv/b6Yb9bH+Y7sEt1ubRvhbRrsbR/ZZ2TWyOltFFSXA+7K8QxKquq8Q3lN/f+7akDU/SjecMkSUUV1RrdSPh3yqNTY3bqW33dOv+0GVLyQCm6u95Zu1+p8VHKSIxSemKUosJbeMIQIY1A30YI9EEuf6e09HET9HO/aXh85k+lU35gtmv/mzM0EQDsVV1hrsOv25Ofs76JXnGHCaH1e/Pj0szP9ZLsRobFe7fL8pqvIyy6XmCvM0S+Wx8pLLKtv/K2VV1hRkXU9ujX7d0v2N34PAe1HC5zwqLu8P26owwiYjru6+jsqsqkfctNgN+1yIT45uZZiEs34T59lJTmvU8e1PJRHwh6JZU1+tfKvdp3pFw5eQVKObREo0oW62RrpVIcgZcueWJStLIkWTs9GdphZWinla7DkX1UndhPPbrFa+bQHrpySj9f+225xUpPjFYcPf0hj0DfRgj0IaQ0T9qz1DtEf5H5I/Hil80195K0fYH0zh2mB7+ftwc/eRABHwCCgcdjAmn93vzig423j04yE6rWVDT/ujEpjQyL927Hp3fe3wHuGnO5Q93h+0d2+R/XlDf//Lh0E/LTRkiZY82lbSlDWV62JapKpb1f1gnwqyRPdWCb+Ayp33Qz4W9Fgff/+3rz/jQ26iIsWko9wX9iK22kd8JJ/jYNSWX50taPzOTQ2+abn2Ve7shEVScNVVTJPqn4QJMv4bYc2mulqjJxgIaOGC8lD1RZQn/NfHa/ctRd8ZHhSvf26KcnmN79Cf2SdMoQMwKkNv4xY3/wItC3EQJ9CKsoNMMNa5cC+vTX0ue/C2wTm+ofoj/iPCkutcPLBAA0o+SQlFPvuvy8rZLlMccdLqlb78avZ+/Wl8DTGMuSirMbGcq/w9wqCht/Xli0Wb0gY6wJ+JnjpJTB9BxXlngD/CJzO7C64eiIhJ4mwPedZu6TBjR+MqmyxIw4rP3/nrNeytkQEPgC1E44mTbK36uf2LvznqgKZQV7pc3vmxC/a3HgJT8JvaRhZ5lb36n+y2EqS6S8bb6blbdN7kNb5MzbJmd1aZP/VIkVpZ1WunZapld/h7d3f+KESXrgwsmSzLD/Sb/+RBmJ0b7An55Yex+twalx6pfC6lJ2ItC3EQJ9J1JVaq5Z273E/CDdt0Jy15mt9JZF5hehZH551lSa6zfpjQCA4FJVZkJ9ZIIZNs614G2rLN8E/Lzt0sGvpANrzX1js/GHx5qQXxvwM8dJSQM792R8lcXSni+lXV9IuxdLB9Y0DPCJvQMDfPd+xx6yPW5z4sV3Ymu9CfpF+xtvH5XoD/i1Q/Z7DPN3cKBjWJb5e3LTeybEZ38deDxtpDT0TBPiM8a07v+HZZnVNg5vNT8L87b7t4/sbn5+kLh0KWWwCmL66rGvLO2wMrXTStdeK1Vu+U/OXXFSH/3qPPN3cVFFta5/boXSE6NN4A84ARCtHvGRcjk5idTWCPRthEDfidVUSvtX+38ZX/SCv5fhrVukr16RIuLMcLi+U80v5MxxwX9NJQAAbc3jkfK3m9+XtbeDX0uN9RJGxJuAUjtUP3Nc0z3SoaCiSNqzrE6AX9swMHXrI/Wdbv5W6DfdLFvY3upOOJmz3gT9Q5saDu+XzEiWHkO9AX+kv1efCfjalrvGjNaoDfEFu/3HHE6pzxQT4IeeaUYRtYeaKnN5Td42E/APewN/3lap9FDTpTvClB/RU/tdPbXDylBa/5GaNvkkKXmwtpRE6ow/fdHkc+uG/0PFlbrjn6sVHxWuhKgwJUSHKz4qzHsL1/CMBI3p3c38mx5LeSWVio8KV1S4k+H/9RDoj1NWVpaysrLkdru1ZcsWAn1X85+7pQ1vNhx2GBZl/jC55n1/74O7mt4hAEDX43GbsOAL+GtNyG/s+vzIRClzjD/gZ4w9vl7r9lReEBjgD37lv8SjVvd+dQL8NBPog0FNlXR4c52efG/gLz/SePvaCfhqe/KZgK/1qsqkHQtMiN/8P6k8338sLEoaOMuE+CFz7V9eubzAH+4Pb60znH97s/NqWJGJKojpq8ORvbXf2VPbrAx9U5mqtaXJ2lNs6XunD9HtMwdJkrbkFOuMRz9v8rWum9ZfPz97uCQpp6hCk38zX5IU5nT4Qn/tCYC5I9J1zTRz4qOi2q0Xlu6qc9ycMKh74qCzzf5PoG8j9NB3YR63uY5t9xLzC33XYrMecOpw6bal/nZPzjJnPNNGmQlqan8xdu/fuYccAgBQn7vGBMoDa0xP9oE1JlDWvcStVlS3OkP1x5p7O67/Lj8i7V7q/V3/ham3QYDv7+997zvNzNsQKixLKjrg7cn3Bv3sdS2YgG+kd+lIJuBroCxf2vKBCfHb5geG4eju0pB5JsQPnClFhMB16B6PuYSj/vD9vG3m2v9mlse0EnvJkzRQrqhEyRWhKrl0sLhGlZZLlR6Xyt1OVXicKnO7VO52aGBad43u20NyhSu3zKNHPt6uSitMNXKpRi5VKUw1ClO1FaYzRvfS9ScPkVzhOlTm0YVPrlSNVdvGpWqF+W7nT+ij339njCSpvMqtC/+2pE7wNycBak8AnJCRoOmDbT650gIE+jZCoIePZZkfbOUFUu+JZp/HI/0ms/GzmuGx0uDTpYue9++rLpfCozukXAAAgoK7Wsrd6O/FP7DGhMrGhobHJPt78GvDfkJm24b8svw6J+u/MLXUDyxJAwMDfGLPtvv3g8WxTMBXG+6TBvgnoIxLDc6RFm3tyC5p0/tmYrvdiwNP+iT28U9q12dK55p/qbrcnPzJ2xY4fP/wVrNCQ5Cw5JDDFS65IuRxhCmvwlK1XKq26oZ/l2oUpoS4GA1M6ybNecicuApSBPo2QqDHUZXlm1+AOev917DlbjS9EcO+JV3ysmlnWdLvBkiR8f5r2NJG0JsPAOh6aipNmKztxT+wxjyuP7mcZFakqXs9fuY4s+RgS5Xle8O7dxb6nA1qEOCTBwcG+ISM4/jiQlhrJ+CTpPAYb7jv13C1iW69Q3fuIcsy34NN75lbzrrA42mj/CE+fVTXOKlRl2WZz1beVhP4q8vMyTt3teSuMp9ld5V5XHfbXW1O5rmrzIged5X3cd3tFrxOcxP/tdR1H0l9Jh//67QTAn0bIdDjmLhrzORBHreUZq4TUtFB6Y/DGm8fHiuNv1Ka97B/X2WxCf8AAHQF1RVS7oY6E++tNSfIG/vDPS49MOBnjvUvPVt6uE6AX2xes76Uoeba99oA35oTBF1R7QR8Od6J9/J3mtnUi/Y1vDwhgMOsRFEb+OuG/e79pJikDim/xdw10p4lpid+03tS4R7/MYfT/F+pndSuIyY+RNM8njonBuqeKGjupEG9dgNmSrHJdn8lTSLQtxECPdpUWb53eJt3opqcDeaPlZoKaeqd0hm/8rf7XX8zyU7ayDo9+vTmAwC6kOpy8zuz7uz6hzc3HiITeprVaQ5vbnisxwmBAb42/OP41FRJhXu9AX+nGZae770/srPp4fu1ohL94b5u2E/qb97Pjpicr6pU2v6pCfBbPgicQDAsWhp0mgnxg+cEdfhD50OgbyMEerQ7d40ZqhQW6T/bu3up9OzcxtuHx0in/FCa/j3v86vNHzxMVgMA6AqqSk1vcUDI36qAYfSpwwOH0Ns9u3hXZFlm0uCmwn5JTvPPd4abjo3Gwn63vlJk3LHXVnrYzEi/+X0T5msq/Meik7zrw59penAjYo793wGOA4G+jRDoYRvftfkbGvbmf+tR6cTrTLt9q6SnZnl785lpHwDQBVUWm+XlKkukXhPpSQ0FVaVm2H5A2K8N/LsbnzSxrtjUOmG/Xi9/XFrDa9rzd/qvh9+7LHCUR7c+0rCzTU9878mda1I7hCwCfRsh0COo1F6bH5Pi/2Plq9ekt25qvH14jHRuljTyfPO45JD5RZk80Cyr0tUmcAEAAMHP4zZL7TUW9vN3Hn129bBof8CPT5f2fNlwLoX00Wby4mFnmc4Q/iZCkGlNDuUUFBAqXGFSj6GB+8ZcbJbHq3ttfrZ30prqMnN9Wq3t86W3bjbbUYlm2Zmkgd77AdKAU7vuzL4AACA4OF1mhvxuvaX+Jzc8Xn6k4RD+I7uk/F1mor6acunQRnOr5XCZeRSGfUsaOs/0ygOdBIEeCHUxSf5rBWvV9uYnZNbZV20mmSnaL1UU+q89rHX5v/2Bftsn0pqX/WE/2Rv8Y3twFhsAANgnuru5ZY5reKz+RH2F+6Qew6Qhc4JvVn2gjRDogc6osd788VeaW1WZ90z2Du9tu7lPGexvu2+ltOHNhq8bEW+GsJ3zmFkmSDKTy7irzbA2wj4AALBLWITphEgeaHclQIch0ANdTUSMlDbc3JoyZI4UEesP/Xk7zBnvqmIp+2tzrNbKZ6UFvzLX7CcNMIG/7nD+nhOYJRYAAABoBwR6AA1ljms4lK2m0sw8m7/dLBlTq6JAcjjNNfs5682trtuX+0cLfPOO6f2vO5Q/PpPZ+AEAAIBjQKAH0DJhkVKPIeZW15xfS6c9YHrw87bXG8q/MzD8b/lIWvtS4PNdkd5e/YHS2X+W4nqY/RWFZqbasIj2/boAAACAEEWgB3D8WnrN2tC5UmScP/Qf2SW5K82s/Ic2SRc+7W/74U+ktS9Lib3M8jPd+/nXmu3ezyw5w1qxAAAA6ML4axhAxznhbHOr5a7xzka7w6w5Gx7tP1Z0QLI8UsEec9v5eeBr3X/QH+hXPmMuB0jq7w/+CT0J/AAAAOjU+GsXgH1cYd7h9v0bHrvi31JJbp31ZWvXm90lVZUGTrS34a2Ggd8ZZtaZ7d5PuuwNf7gvOiBFxElRCe3zNQEAAAAdhEAPIDg5HFJ8mrn1Oan5tqMvNuvM1gb/gt2Su8r0/FeWBPbUv32rtGOhFJPccBh/Un+p7zSW3wMAAEBIINADCH3jrjC3Wh6PVHzQ9O5XFge2rSg092V55rZ/lf9YTIr0w+3+x/N/adrXHcrfvW/gsn0AAACATQj0ADofp1NK7Glu9d20UKooMr34dYfxH9klRcYHtl3/b3NSoL7YVKnneOmy1/z7di+RXBFSXJq5MTs/AAAA2hmBHkDXE5UgpY8yt+acfK+Ut61O8N9peuxLc6XSQ4Ft374tMPxHJ0nx6Sbcp40wy/vVyl4nhceY4/T2AwAA4BgR6AGgKXWH8dcqP+Jdbq8mcH9iL8ldLZXkSJ5qqTzf3HK/8Q/zr/XaFeY1JCki3swTEJdu7nucIJ3yA3/bI7vNCYioblzbDwAAgAAEegBojeju5lbfNf819x6PCf0l2VJxtgn44TGBbcNjzb7qMqmqWMorNiMBJClzfGCgf/5sc3lAWJTp7a/t9Y9Pl1KGSJNu9LctL5AiE8wlBwAAAOj0CPQA0JacTik22dzSRjTe5rYlkmWZCftKcvzBvzhbiu4W2Lam0ntfYYJ9wW7/sZ4nBgb6v88wy/LFpgb2+selSymDpVEX+tuWF5jh/q7wtviqAQAAYAMCPQDYweHwDqVPMGG7KfdulqrLA0N/7X18ur+dZUklhyRPjVR8wNzq6jUxMND/9SSzEkBYtKkhMsFMChiVIKWOkOb+xt927SuS5TZtattGJfofh0W2zfcEAAAArUKgB4BgFx5tls5L6t90G4dD+vFeqSTXO9w/J/C+W5/A9rXL+dWUSyXl5iRBraqywLbz/6/hCYJaPYZJt3/pf/zGNWY5wMiEeicAEqT4jMCTCgV7zQiByATzNTJHAAAAQKsQ6AGgs3CFN71cX3337ZYqi8ytot59ZEJg20GzzImBum0qi71t6y31t3e5VLS/8X8zZWhgoH/5O9KhjWbbGRY4SiBpgHTRC/626980kw7Gp0sJmea+/r8NAADQxRDoAaArcoVJMUnmdjTnZjW+3+Mx1/bXdc5jUlm+VFkYGP4riqS41Iav4XBKlsdcKlC7MoDknzug1uePmBUD6oqI804OOFS69J/+/bsWSQ6XORafbnr/AQAAOiECPQDg2DidUkS9GfwHndby59++zFz7X1XScJSA0xXYtu80KbaHmTug+KBpU1ViVgdwRQS2fe9ef8+/ZFYliM/wrwww72H/scNbzYoDcalMEAgAAEIOgR4AYB+Hwwydj4yX1MylAmf9PvBxZYl3csCDpoe/ru59zciB4oPmvvyIueV+Yy4dqOu1K6RDmyQ5zAmD+HQT/hMypORB0tQ7/W0rCqWIeJYFBAAAQYNADwAIPZFx5pY8sOGxy14z95YlVRT4e/WLsxv25jtc5vp9T41Ummtu2V+bY2kjAwP902eYEQFx6f7h/LXhv3t/aeT5/raWxSR/AACg3RHoAQCdk8NhhttHd5dST2i8zW1LzFwAZXne0H/QH/6jEgPbFmeb4F+0z9zqShsZGOj/MtYsIxgR6715T0BExEpJA6Uzf+dvu+p5qbrMtKltW/u8qMTmVzcAAABdGoEeANC1OZ1SXA9zyxjddLsfbDPLAvp6/A/6t+MzAttWlkjVpeZWWu910nMDHy/+k5S/o/F/s3t/6a61/sdPn2Ha+oJ/nfCfkBk4P8CGt81cA/VPEkTEm5ML8enNf18AAEDQI9ADANASrVkW8LZlZtK+qlLvrc52RGxg26FnSkUH6rX1to9LC2xbelgqPWRu9XXvFxjoFz0qHVzbeH3RSdJ9O/2PX7lUOrDGXJIQFmnua7cjE6TLX/e3XfwX6fCWhu1c4VJYVOBlCruXmssYXLXH6z0nZah/ToKqMnPvijCrMAAAgKPiN2YjsrKylJWVJbfbbXcpAIBQFNdDUo+WtZ3z65a/7lXveJcCLGl4wiAsKrBtv+mmF772eGWd9lEJgW1Lcs1Ig8ZEdQt8vPUjadcXjbd1hgcG+sV/lrb8r+mv52eHJXkD/bt3Suv/ZbYdTu9JgAgpzHsC4LZl/roXPSpt/1QKrx11EBO4PfFGf9tDW8wEirUjFMJj/NuuCOY6AACENAJ9I26//XbdfvvtKioqUmJi4tGfAABAR+jWu+VtmztRYFmBjy94UqoslmqqJHel5K7yb6te4J1wjTTgVG8bb9va7frhuMdQs8KAu1JyVwe2d1eZCQlruavq1OeRasrNrdK7r27bQ5ulnZ83/fWNu8q/vfzv0oqnGm/nDJPuWOmfp+DLf0gb3jInBSJivScJ6myfeJ33ZI2kw9ukwj3mcoa6JwkiYqWwaFZDAAB0CAI9AABdTf3gnTSg5c8ddWHL257+i5a3vfAZs8xg/eBfu113BMKkm6RBs72jFMrMXAVVpWa7qtS7DKJXbA8pZYj3WImZgLD25IGnxoTxWnlbpT1Lmq5x5AX+QP/VP6Uv/tB025s+kzLHmu01L0krnzWXHTjDvJcVhHsfh0szfyKlDDJtd34hbfnAf6z2EgRXhHk87Cz/ZR/5O6WcDf429dt36+sfqVBdbr4HdV/L6WKEAgCEOAI9AACwX23AbYme482tJU79kbnV5a42wb+6TIpN8e+fcI3Ud5r/WO0JgyrvBIcxSf62MclS6ojAkwnVdWZArHui4Mguaf/Kpmuceod/e/8qaenjTbdNPcEf6Ld9Ir1/b9NtL31NGjrXbG94S3r71noNHN7ve4R03hPS8HPM7q2fSB/8SAqP8o9SCPfeImKkMZdJfSabtgV7pe3zTbvw6MC24TFSfFrgCRYAQJsi0AMAgK7FFS5FdzO3utJGmFtLTLnd3OryeC8VqCo1Ew/WGn2xlDle8lSbkwnuau92leSukRL7+Nv2nCBNu8vfzl1lRhLUjliIS/W3jU2Rek0KfN267SPqnFRwVzfyRVj+163bU19RYEYrNKXPFH+gz/5a+s9dTbc96w/SxBvM9q5FZgLG8Bhv+I8N3B5/lTR0nmlbuF9a+8/GTxJExJhJIBMyvd93t38UByMOAHQxBHoAAIC24HT6r6OvK2WwubVE/xnm1hIjvm1uLTHhamnclXVOJNQ9sVBtLk3w1XCKdM175hKIqjIzWqG6zLtdLqXXWd4xJkUaMs/fptp7QqO63DyOiPO3rSr1TupY1HiNA2b6t4/skhb8qumv57SfSzO+b7az10n/OMVsO1zmsgbfzWVOkEy/2xzP2y798yJzzOEyx+u2HXWh/wREySHpv3ebSRrrv6bTJfWb4b8EparUXILh+/fr3fcYJg30fn3uGmnDm95j4XUuwfBOABmbIiUP9H+thfsatnGGcfICgCQCPQAAQNfgdErOSLNkYHPievjnCjiaPpOly15tWdt+M6Q7V9cJ/KX+a/ury6Tek/xtY1Ok8Vc3cpLAex9bZ6RCdbl/23JLbrd3QkevuhMuVpdLeduarrH3ZP92VYm06b9Nt3VF+AN9ZUnzcyqMvcIf6GvKpTdvbLrt8POki54325YlPdrEqBFXhDRkjnTxS/59f51qvgd1w3/tdvpoafYD/rYf/sQ7OWV4w/YJPaXR3/G33fw/c+LHGSbJ8k6saZkJLCMT/F+bJG3+wHzv6rap3Y6Ml044299243+ksvx6r+l9TkSsNPYyf9v1b0rF2Q3byjJ1n1TnkpKv3zAnhSRz4sPpqnMCJ1yafJO/7c7PzdKhzjDvyRtX4AmfgbPMvWROCJXlm89S3ZM3tW279/O3rSjyfs+cDU8gOZyckEGbIdADAACg/UXEBPY8N6fHUOmcv7Ssbe/J0o/2mhEFHrcJtJ4as+2pCbz8Iam/dO0H3uM13rZu/+PkQf62MUnSt/7kfy2rTjuP21xGUSs8Spp8a53jNSaU1m4HzPngMKMRao/5Vn7wjp6Iz/A3dVebsFr3pITvWFXDFSsObzav2Zj6+1e/KFUWNt6216TAQP/f7zW9tGXaKOnWRf7HH/xIOrKz8bZJAwID/cLfSjnrG28bnxEY6Jc9Ie1b3njbyMTAQL/2JWnHwsbbOsMCA/2yv0mb32u8rST99JA/pC98SFr3RtNt79slRXc32x/9RFr9QtNtv/eNfz6MTx6UVj7jD/2+e6e5XfVO4Goca16od4KgTvtvPer/nG1429QbcEKjtr1Tmna3v+2uxdLm9+v9+3Ved9SF5oSFJOVuNJfQ+E6A1Btp0nOCmT9DMiudFO5vOMqkdjss0v/9xTEh0AMAACB0OZ3e2fwTjt42IlbqO6VlrxuVKJ14bcvbzvtty9pGxklXvd2ytmER0s8OmeDeWPh3RQS2v+Y9/zHffVXDyyokacb3zOiIxtrWBrdavU6USg+bYw6HJIe/l7n+Khm9J0mJvRq2k0NKyAhs22+6lNi7TluHf7s2GNcaOEvq1iewbe3rh0cHth18hlnlQfKOEPD4T9w46i0pmT7KjASx3GYejLonhCx3YNiMSTGvW/t6AW09gctrWh41q+7rVpVKFU2cXDEv5t8s2m8uM2lK3RErh7c0P8pk3JX+QH9gTfMTcvac4P9/sXtJ8xNyXvJPsyKHJG39uPkRKec/KY2+yGxv+dBM3umKDAz/Yd776d/zz7ORvU5a9GidEwT1ThQMmWP+30pScY609UP/8QEzAyc5DXEEegAAACCYOWpXJAiXFNt0uz4ntfw1p3+v5W3rDus/mvP/0fK28x5ueduZP2552/oTVrbV6877bctP3JybJZ39WOAoEN+2OzBQnnKfNOnmwDZ1TzDEZ/rbjrvSXL4S0K5O+8Re/raDTzf/ju9ERb32ddtmjpOmfrfeyYo69wl1aujeTxp+rv9ERsBJpMrAUTHOMCkuzbsMqfekkafOJJ11VzepKpXK8pr+npbl+7eLDkjr/91029ge/kCft1V6907/sZs+61SB3mFZ9cfqoFZRUZESExNVWFiohIQWnPUFAAAAADTNsvzh3xVpeuAlM+9A0X7/yYGaysARJBmjzSgNycyRsPl/gSNLair9bUd8W+o3zbTNXid9+mt/23Me81/CEKRak0MJ9M0g0AMAAAAAOlJrcqiz2aMAAAAAACAoEegBAAAAAAhBBHoAAAAAAEIQgR4AAAAAgBBEoAcAAAAAIAQR6AEAAAAACEEEegAAAAAAQhCBHgAAAACAEESgBwAAAAAgBBHoAQAAAAAIQQR6AAAAAABCEIEeAAAAAIAQRKAHAAAAACAEEegBAAAAAAhBBHoAAAAAAEIQgR4AAAAAgBBEoAcAAAAAIAQR6AEAAAAACEEEegAAAAAAQhCBHgAAAACAEESgBwAAAAAgBBHoAQAAAAAIQQR6AAAAAABCEIEeAAAAAIAQRKAHAAAAACAEEegBAAAAAAhBBHoAAAAAAEIQgR4AAAAAgBBEoAcAAAAAIAQR6AEAAAAACEEEegAAAAAAQhCBHgAAAACAEBRmdwHBzLIsSVJRUZHNlQAAAAAAuoLa/FmbR5tDoG9GcXGxJKl37942VwIAAAAA6EqKi4uVmJjYbBuH1ZLY30V5PB4dOHBA8fHxcjgcdpfTrKKiIvXu3Vt79+5VQkKC3eWgFXjvQhfvXejivQttvH+hi/cudPHehS7eu9BjWZaKi4uVmZkpp7P5q+TpoW+G0+lUr1697C6jVRISEvighijeu9DFexe6eO9CG+9f6OK9C128d6GL9y60HK1nvhaT4gEAAAAAEIII9AAAAAAAhCACfScRGRmpBx54QJGRkXaXglbivQtdvHehi/cutPH+hS7eu9DFexe6eO86NybFAwAAAAAgBNFDDwAAAABACCLQAwAAAAAQggj0AAAAAACEIAI9AAAAAAAhiEAfQrKystSvXz9FRUVp8uTJWr58ebPt33jjDQ0bNkxRUVEaNWqU3n///Q6qFLUeeughTZw4UfHx8UpNTdV5552nzZs3N/uc5557Tg6HI+AWFRXVQRWj1oMPPtjgfRg2bFizz+EzFzz69evX4P1zOBy6/fbbG23P584+n3/+uc4++2xlZmbK4XDo7bffDjhuWZZ+/vOfKyMjQ9HR0Zo9e7a2bt161Ndt7e9MtF5z7111dbXuu+8+jRo1SrGxscrMzNRVV12lAwcONPuax/KzF613tM/dNddc0+B9mDt37lFfl89dxzja+9fY7z+Hw6FHHnmkydfksxe6CPQh4rXXXtM999yjBx54QKtXr9aYMWM0Z84c5ebmNtp+yZIluvTSS3X99ddrzZo1Ou+883Teeedp/fr1HVx51/bZZ5/p9ttv17Jly/Txxx+rurpaZ5xxhkpLS5t9XkJCgg4ePOi77d69u4MqRl0jRowIeB8WLVrUZFs+c8FlxYoVAe/dxx9/LEn6zne+0+Rz+NzZo7S0VGPGjFFWVlajx3/3u9/pL3/5i/72t7/pyy+/VGxsrObMmaOKioomX7O1vzNxbJp778rKyrR69Wr97Gc/0+rVq/Xmm29q8+bNOuecc476uq352Ytjc7TPnSTNnTs34H145ZVXmn1NPncd52jvX9337eDBg3rmmWfkcDh0wQUXNPu6fPZClIWQMGnSJOv222/3PXa73VZmZqb10EMPNdr+oosuss4666yAfZMnT7Zuvvnmdq0TzcvNzbUkWZ999lmTbZ599lkrMTGx44pCox544AFrzJgxLW7PZy643XXXXdbAgQMtj8fT6HE+d8FBkvXWW2/5Hns8His9Pd165JFHfPsKCgqsyMhI65VXXmnydVr7OxPHr/5715jly5dbkqzdu3c32aa1P3tx/Bp7766++mrr3HPPbdXr8LmzR0s+e+eee641a9asZtvw2Qtd9NCHgKqqKq1atUqzZ8/27XM6nZo9e7aWLl3a6HOWLl0a0F6S5syZ02R7dIzCwkJJUlJSUrPtSkpK1LdvX/Xu3VvnnnuuNmzY0BHloZ6tW7cqMzNTAwYM0OWXX649e/Y02ZbPXPCqqqrSSy+9pOuuu04Oh6PJdnzugs/OnTuVnZ0d8NlKTEzU5MmTm/xsHcvvTHSMwsJCORwOdevWrdl2rfnZi/azcOFCpaamaujQobr11luVl5fXZFs+d8ErJydH7733nq6//vqjtuWzF5oI9CHg8OHDcrvdSktLC9iflpam7OzsRp+TnZ3dqvZofx6PR3fffbemTZumkSNHNtlu6NCheuaZZ/TOO+/opZdeksfj0dSpU7Vv374OrBaTJ0/Wc889pw8++EBPPPGEdu7cqRkzZqi4uLjR9nzmgtfbb7+tgoICXXPNNU224XMXnGo/P635bB3L70y0v4qKCt1333269NJLlZCQ0GS71v7sRfuYO3euXnjhBc2fP18PP/ywPvvsM82bN09ut7vR9nzugtfzzz+v+Ph4nX/++c2247MXusLsLgDoKm6//XatX7/+qNcjTZkyRVOmTPE9njp1qk444QT9/e9/1y9/+cv2LhNe8+bN822PHj1akydPVt++ffX666+36Cw3gsfTTz+tefPmKTMzs8k2fO6A9lNdXa2LLrpIlmXpiSeeaLYtP3uDwyWXXOLbHjVqlEaPHq2BAwdq4cKFOu2002ysDK31zDPP6PLLLz/qRK989kIXPfQhICUlRS6XSzk5OQH7c3JylJ6e3uhz0tPTW9Ue7euOO+7Qf//7Xy1YsEC9evVq1XPDw8M1btw4bdu2rZ2qQ0t069ZNQ4YMafJ94DMXnHbv3q1PPvlEN9xwQ6uex+cuONR+flrz2TqW35loP7Vhfvfu3fr444+b7Z1vzNF+9qJjDBgwQCkpKU2+D3zugtMXX3yhzZs3t/p3oMRnL5QQ6ENARESEJkyYoPnz5/v2eTwezZ8/P6BHqa4pU6YEtJekjz/+uMn2aB+WZemOO+7QW2+9pU8//VT9+/dv9Wu43W6tW7dOGRkZ7VAhWqqkpETbt29v8n3gMxecnn32WaWmpuqss85q1fP43AWH/v37Kz09PeCzVVRUpC+//LLJz9ax/M5E+6gN81u3btUnn3yi5OTkVr/G0X72omPs27dPeXl5Tb4PfO6C09NPP60JEyZozJgxrX4un70QYvesfGiZV1991YqMjLSee+4565tvvrFuuukmq1u3blZ2drZlWZZ15ZVXWj/60Y987RcvXmyFhYVZv//9762NGzdaDzzwgBUeHm6tW7fOri+hS7r11lutxMREa+HChdbBgwd9t7KyMl+b+u/dL37xC+vDDz+0tm/fbq1atcq65JJLrKioKGvDhg12fAld1ve//31r4cKF1s6dO63Fixdbs2fPtlJSUqzc3FzLsvjMhQK322316dPHuu+++xoc43MXPIqLi601a9ZYa9assSRZf/zjH601a9b4ZkL/7W9/a3Xr1s165513rK+//to699xzrf79+1vl5eW+15g1a5b12GOP+R4f7Xcm2kZz711VVZV1zjnnWL169bLWrl0b8DuwsrLS9xr137uj/exF22juvSsuLrbuvfdea+nSpdbOnTutTz75xBo/frw1ePBgq6KiwvcafO7sc7Sfm5ZlWYWFhVZMTIz1xBNPNPoafPY6DwJ9CHnsscesPn36WBEREdakSZOsZcuW+Y6dcsop1tVXXx3Q/vXXX7eGDBliRUREWCNGjLDee++9Dq4Ykhq9Pfvss7429d+7u+++2/c+p6WlWWeeeaa1evXqji++i7v44outjIwMKyIiwurZs6d18cUXW9u2bfMd5zMX/D788ENLkrV58+YGx/jcBY8FCxY0+nOy9v3xeDzWz372MystLc2KjIy0TjvttAbvad++fa0HHnggYF9zvzPRNpp773bu3Nnk78AFCxb4XqP+e3e0n71oG829d2VlZdYZZ5xh9ejRwwoPD7f69u1r3XjjjQ2COZ87+xzt56ZlWdbf//53Kzo62iooKGj0NfjsdR4Oy7Ksdh0CAAAAAAAA2hzX0AMAAAAAEIII9AAAAAAAhCACPQAAAAAAIYhADwAAAABACCLQAwAAAAAQggj0AAAAAACEIAI9AAAAAAAhiEAPAACCisPh0Ntvv213GQAABD0CPQAA8LnmmmvkcDga3ObOnWt3aQAAoJ4wuwsAAADBZe7cuXr22WcD9kVGRtpUDQAAaAo99AAAIEBkZKTS09MDbt27d5dkhsM/8cQTmjdvnqKjozVgwAD961//Cnj+unXrNGvWLEVHRys5OVk33XSTSkpKAto888wzGjFihCIjI5WRkaE77rgj4Pjhw4f17W9/WzExMRo8eLDefffd9v2iAQAIQQR6AADQKj/72c90wQUX6KuvvtLll1+uSy65RBs3bpQklZaWas6cOerevbtWrFihN954Q5988klAYH/iiSd0++2366abbtK6dev07rvvatCgQQH/xi9+8QtddNFF+vrrr3XmmWfq8ssvV35+fod+nQAABDuHZVmW3UUAAIDgcM011+ill15SVFRUwP77779f999/vxwOh2655RY98cQTvmMnnXSSxo8fr7/+9a968skndd9992nv3r2KjY2VJL3//vs6++yzdeDAAaWlpalnz5669tpr9atf/arRGhwOh37605/ql7/8pSRzkiAuLk7/+9//uJYfAIA6uIYeAAAEmDlzZkBgl6SkpCTf9pQpUwKOTZkyRWvXrpUkbdy4UWPGjPGFeUmaNm2aPB6PNm/eLIfDoQMHDui0005rtobRo0f7tmNjY5WQkKDc3Nxj/ZIAAOiUCPQAACBAbGxsgyHwbSU6OrpF7cLDwwMeOxwOeTye9igJAICQxTX0AACgVZYtW9bg8QknnCBJOuGEE/TVV1+ptLTUd3zx4sVyOp0aOnSo4uPj1a9fP82fP79DawYAoDOihx4AAASorKxUdnZ2wL6wsDClpKRIkt544w2deOKJmj59ul5++WUtX75cTz/9tCTp8ssv1wMPPKCrr75aDz74oA4dOqQ777xTV155pdLS0iRJDz74oG655RalpqZq3rx5Ki4u1uLFi3XnnXd27BcKAECII9ADAIAAH3zwgTIyMgL2DR06VJs2bZJkZqB/9dVXddtttykjI0OvvPKKhg8fLkmKiYnRhx9+qLvuuksTJ05UTEyMLrjgAv3xj3/0vdbVV1+tiooKPfroo7r33nuVkpKiCy+8sOO+QAAAOglmuQcAAC3mcDj01ltv6bzzzrO7FAAAujyuoQcAAAAAIAQR6AEAAAAACEFcQw8AAFqMK/UAAAge9NADAAAAABCCCPQAAAAAAIQgAj0AAAAAACGIQA8AAAAAQAgi0AMAAAAAEIII9AAAAAAAhCACPQAAAAAAIYhADwAAAABACCLQAwAAAAAQgv4fQU+c9vRJyuEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n",
    "plot_loss(careful_bias_history, \"Careful Bias\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fKMioV0ddG3R"
   },
   "source": [
    "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RsA_7SEntRaV"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:06.480430Z",
     "iopub.status.busy": "2024-01-17T02:21:06.479928Z",
     "iopub.status.idle": "2024-01-17T02:21:28.642764Z",
     "shell.execute_reply": "2024-01-17T02:21:28.642100Z"
    },
    "id": "yZKAc8NCDnoR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 1:57 - loss: 0.0167 - cross entropy: 0.0034 - Brier score: 5.3563e-04 - tp: 69.0000 - fp: 9.0000 - tn: 47523.0000 - fn: 16.0000 - accuracy: 0.9995 - precision: 0.8846 - recall: 0.8118 - auc: 0.9339 - prc: 0.8024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0149 - cross entropy: 0.0077 - Brier score: 0.0012 - tp: 90.0000 - fp: 52.0000 - tn: 76103.0000 - fn: 44.0000 - accuracy: 0.9987 - precision: 0.6338 - recall: 0.6716 - auc: 0.9110 - prc: 0.6003     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0139 - cross entropy: 0.0091 - Brier score: 0.0014 - tp: 107.0000 - fp: 80.0000 - tn: 104699.0000 - fn: 75.0000 - accuracy: 0.9985 - precision: 0.5722 - recall: 0.5879 - auc: 0.8913 - prc: 0.5043"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0129 - cross entropy: 0.0095 - Brier score: 0.0014 - tp: 115.0000 - fp: 99.0000 - tn: 133310.0000 - fn: 109.0000 - accuracy: 0.9984 - precision: 0.5374 - recall: 0.5134 - auc: 0.8813 - prc: 0.4443"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58/90 [==================>...........] - ETA: 0s - loss: 0.0120 - cross entropy: 0.0094 - Brier score: 0.0014 - tp: 129.0000 - fp: 111.0000 - tn: 163976.0000 - fn: 137.0000 - accuracy: 0.9985 - precision: 0.5375 - recall: 0.4850 - auc: 0.8724 - prc: 0.4212"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73/90 [=======================>......] - ETA: 0s - loss: 0.0113 - cross entropy: 0.0093 - Brier score: 0.0013 - tp: 155.0000 - fp: 120.0000 - tn: 194633.0000 - fn: 165.0000 - accuracy: 0.9985 - precision: 0.5636 - recall: 0.4844 - auc: 0.8741 - prc: 0.4292"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "88/90 [============================>.] - ETA: 0s - loss: 0.0109 - cross entropy: 0.0093 - Brier score: 0.0013 - tp: 178.0000 - fp: 128.0000 - tn: 225286.0000 - fn: 201.0000 - accuracy: 0.9985 - precision: 0.5817 - recall: 0.4697 - auc: 0.8752 - prc: 0.4232"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 2s 11ms/step - loss: 0.0109 - cross entropy: 0.0092 - Brier score: 0.0013 - tp: 179.0000 - fp: 128.0000 - tn: 227336.0000 - fn: 202.0000 - accuracy: 0.9986 - precision: 0.5831 - recall: 0.4698 - auc: 0.8759 - prc: 0.4240 - val_loss: 0.0053 - val_cross entropy: 0.0053 - val_Brier score: 7.6563e-04 - val_tp: 44.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 38.0000 - val_accuracy: 0.9991 - val_precision: 0.8980 - val_recall: 0.5366 - val_auc: 0.9188 - val_prc: 0.7535\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0053 - cross entropy: 0.0053 - Brier score: 5.7561e-04 - tp: 4.0000 - fp: 0.0000e+00 - tn: 2043.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.8000 - auc: 0.9946 - prc: 0.8154"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16/90 [====>.........................] - ETA: 0s - loss: 0.0084 - cross entropy: 0.0084 - Brier score: 0.0012 - tp: 19.0000 - fp: 7.0000 - tn: 32707.0000 - fn: 35.0000 - accuracy: 0.9987 - precision: 0.7308 - recall: 0.3519 - auc: 0.8688 - prc: 0.4091     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "31/90 [=========>....................] - ETA: 0s - loss: 0.0077 - cross entropy: 0.0077 - Brier score: 0.0011 - tp: 49.0000 - fp: 13.0000 - tn: 63367.0000 - fn: 59.0000 - accuracy: 0.9989 - precision: 0.7903 - recall: 0.4537 - auc: 0.8675 - prc: 0.5190"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "46/90 [==============>...............] - ETA: 0s - loss: 0.0073 - cross entropy: 0.0073 - Brier score: 0.0010 - tp: 77.0000 - fp: 18.0000 - tn: 94027.0000 - fn: 86.0000 - accuracy: 0.9989 - precision: 0.8105 - recall: 0.4724 - auc: 0.8848 - prc: 0.5372"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "61/90 [===================>..........] - ETA: 0s - loss: 0.0071 - cross entropy: 0.0071 - Brier score: 9.9375e-04 - tp: 94.0000 - fp: 20.0000 - tn: 124700.0000 - fn: 114.0000 - accuracy: 0.9989 - precision: 0.8246 - recall: 0.4519 - auc: 0.8783 - prc: 0.5393"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "75/90 [========================>.....] - ETA: 0s - loss: 0.0070 - cross entropy: 0.0070 - Brier score: 9.8045e-04 - tp: 120.0000 - fp: 29.0000 - tn: 153317.0000 - fn: 134.0000 - accuracy: 0.9989 - precision: 0.8054 - recall: 0.4724 - auc: 0.8824 - prc: 0.5376"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - ETA: 0s - loss: 0.0070 - cross entropy: 0.0070 - Brier score: 9.7767e-04 - tp: 137.0000 - fp: 31.0000 - tn: 181946.0000 - fn: 162.0000 - accuracy: 0.9989 - precision: 0.8155 - recall: 0.4582 - auc: 0.8800 - prc: 0.5341"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0070 - cross entropy: 0.0070 - Brier score: 9.7767e-04 - tp: 137.0000 - fp: 31.0000 - tn: 181946.0000 - fn: 162.0000 - accuracy: 0.9989 - precision: 0.8155 - recall: 0.4582 - auc: 0.8800 - prc: 0.5341 - val_loss: 0.0044 - val_cross entropy: 0.0044 - val_Brier score: 6.3545e-04 - val_tp: 54.0000 - val_fp: 7.0000 - val_tn: 45480.0000 - val_fn: 28.0000 - val_accuracy: 0.9992 - val_precision: 0.8852 - val_recall: 0.6585 - val_auc: 0.9263 - val_prc: 0.7737\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0068 - cross entropy: 0.0068 - Brier score: 9.8711e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.8658 - prc: 0.5494"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0051 - cross entropy: 0.0051 - Brier score: 7.2899e-04 - tp: 27.0000 - fp: 5.0000 - tn: 30667.0000 - fn: 21.0000 - accuracy: 0.9992 - precision: 0.8438 - recall: 0.5625 - auc: 0.9322 - prc: 0.6912 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0052 - cross entropy: 0.0052 - Brier score: 7.3622e-04 - tp: 55.0000 - fp: 9.0000 - tn: 59286.0000 - fn: 42.0000 - accuracy: 0.9991 - precision: 0.8594 - recall: 0.5670 - auc: 0.9386 - prc: 0.6833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0057 - cross entropy: 0.0057 - Brier score: 8.2313e-04 - tp: 83.0000 - fp: 14.0000 - tn: 89943.0000 - fn: 72.0000 - accuracy: 0.9990 - precision: 0.8557 - recall: 0.5355 - auc: 0.9234 - prc: 0.6561"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58/90 [==================>...........] - ETA: 0s - loss: 0.0056 - cross entropy: 0.0056 - Brier score: 8.1488e-04 - tp: 100.0000 - fp: 19.0000 - tn: 118573.0000 - fn: 92.0000 - accuracy: 0.9991 - precision: 0.8403 - recall: 0.5208 - auc: 0.9160 - prc: 0.6298"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72/90 [=======================>......] - ETA: 0s - loss: 0.0059 - cross entropy: 0.0059 - Brier score: 8.5071e-04 - tp: 116.0000 - fp: 26.0000 - tn: 147196.0000 - fn: 118.0000 - accuracy: 0.9990 - precision: 0.8169 - recall: 0.4957 - auc: 0.9064 - prc: 0.5996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0061 - cross entropy: 0.0061 - Brier score: 8.9999e-04 - tp: 137.0000 - fp: 33.0000 - tn: 175810.0000 - fn: 148.0000 - accuracy: 0.9990 - precision: 0.8059 - recall: 0.4807 - auc: 0.9040 - prc: 0.5701"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0061 - cross entropy: 0.0061 - Brier score: 8.9642e-04 - tp: 146.0000 - fp: 33.0000 - tn: 181944.0000 - fn: 153.0000 - accuracy: 0.9990 - precision: 0.8156 - recall: 0.4883 - auc: 0.9033 - prc: 0.5771 - val_loss: 0.0040 - val_cross entropy: 0.0040 - val_Brier score: 6.0828e-04 - val_tp: 55.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 27.0000 - val_accuracy: 0.9993 - val_precision: 0.9016 - val_recall: 0.6707 - val_auc: 0.9266 - val_prc: 0.7869\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 5.3611e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2045.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.6667 - auc: 0.8283 - prc: 0.6680"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0061 - cross entropy: 0.0061 - Brier score: 9.1208e-04 - tp: 20.0000 - fp: 6.0000 - tn: 30668.0000 - fn: 26.0000 - accuracy: 0.9990 - precision: 0.7692 - recall: 0.4348 - auc: 0.8758 - prc: 0.5327 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0054 - cross entropy: 0.0054 - Brier score: 8.1668e-04 - tp: 47.0000 - fp: 12.0000 - tn: 59291.0000 - fn: 42.0000 - accuracy: 0.9991 - precision: 0.7966 - recall: 0.5281 - auc: 0.9121 - prc: 0.5968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0057 - cross entropy: 0.0057 - Brier score: 8.7736e-04 - tp: 68.0000 - fp: 17.0000 - tn: 87909.0000 - fn: 70.0000 - accuracy: 0.9990 - precision: 0.8000 - recall: 0.4928 - auc: 0.9091 - prc: 0.5844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0060 - cross entropy: 0.0060 - Brier score: 9.3898e-04 - tp: 94.0000 - fp: 21.0000 - tn: 116519.0000 - fn: 102.0000 - accuracy: 0.9989 - precision: 0.8174 - recall: 0.4796 - auc: 0.9016 - prc: 0.5914"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0058 - cross entropy: 0.0058 - Brier score: 9.2113e-04 - tp: 118.0000 - fp: 24.0000 - tn: 145139.0000 - fn: 127.0000 - accuracy: 0.9990 - precision: 0.8310 - recall: 0.4816 - auc: 0.9045 - prc: 0.6166"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0058 - cross entropy: 0.0058 - Brier score: 9.0614e-04 - tp: 143.0000 - fp: 29.0000 - tn: 173760.0000 - fn: 148.0000 - accuracy: 0.9990 - precision: 0.8314 - recall: 0.4914 - auc: 0.9054 - prc: 0.6140"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0057 - cross entropy: 0.0057 - Brier score: 8.9241e-04 - tp: 147.0000 - fp: 30.0000 - tn: 181947.0000 - fn: 152.0000 - accuracy: 0.9990 - precision: 0.8305 - recall: 0.4916 - auc: 0.9045 - prc: 0.6121 - val_loss: 0.0037 - val_cross entropy: 0.0037 - val_Brier score: 5.6512e-04 - val_tp: 58.0000 - val_fp: 7.0000 - val_tn: 45480.0000 - val_fn: 24.0000 - val_accuracy: 0.9993 - val_precision: 0.8923 - val_recall: 0.7073 - val_auc: 0.9327 - val_prc: 0.7996\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0046 - cross entropy: 0.0046 - Brier score: 7.1316e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.8725 - prc: 0.7516"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0054 - cross entropy: 0.0054 - Brier score: 8.6995e-04 - tp: 17.0000 - fp: 5.0000 - tn: 30671.0000 - fn: 27.0000 - accuracy: 0.9990 - precision: 0.7727 - recall: 0.3864 - auc: 0.8945 - prc: 0.5607 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0048 - cross entropy: 0.0048 - Brier score: 8.0269e-04 - tp: 48.0000 - fp: 10.0000 - tn: 59288.0000 - fn: 46.0000 - accuracy: 0.9991 - precision: 0.8276 - recall: 0.5106 - auc: 0.9125 - prc: 0.6649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0050 - cross entropy: 0.0050 - Brier score: 8.2230e-04 - tp: 70.0000 - fp: 19.0000 - tn: 87911.0000 - fn: 64.0000 - accuracy: 0.9991 - precision: 0.7865 - recall: 0.5224 - auc: 0.9117 - prc: 0.6178"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0050 - cross entropy: 0.0050 - Brier score: 8.1646e-04 - tp: 102.0000 - fp: 24.0000 - tn: 116527.0000 - fn: 83.0000 - accuracy: 0.9991 - precision: 0.8095 - recall: 0.5514 - auc: 0.9166 - prc: 0.6377"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0052 - cross entropy: 0.0052 - Brier score: 8.3901e-04 - tp: 127.0000 - fp: 29.0000 - tn: 145143.0000 - fn: 109.0000 - accuracy: 0.9991 - precision: 0.8141 - recall: 0.5381 - auc: 0.9086 - prc: 0.6386"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0051 - cross entropy: 0.0051 - Brier score: 8.1558e-04 - tp: 158.0000 - fp: 30.0000 - tn: 173761.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8404 - recall: 0.5467 - auc: 0.9112 - prc: 0.6597"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0050 - cross entropy: 0.0050 - Brier score: 8.0944e-04 - tp: 163.0000 - fp: 31.0000 - tn: 181946.0000 - fn: 136.0000 - accuracy: 0.9991 - precision: 0.8402 - recall: 0.5452 - auc: 0.9091 - prc: 0.6557 - val_loss: 0.0035 - val_cross entropy: 0.0035 - val_Brier score: 5.4862e-04 - val_tp: 58.0000 - val_fp: 7.0000 - val_tn: 45480.0000 - val_fn: 24.0000 - val_accuracy: 0.9993 - val_precision: 0.8923 - val_recall: 0.7073 - val_auc: 0.9327 - val_prc: 0.8041\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0010 - cross entropy: 0.0010 - Brier score: 3.1781e-06 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 6.6701e-04 - tp: 30.0000 - fp: 7.0000 - tn: 30666.0000 - fn: 17.0000 - accuracy: 0.9992 - precision: 0.8108 - recall: 0.6383 - auc: 0.9456 - prc: 0.7403     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0046 - cross entropy: 0.0046 - Brier score: 7.6023e-04 - tp: 51.0000 - fp: 12.0000 - tn: 59290.0000 - fn: 39.0000 - accuracy: 0.9991 - precision: 0.8095 - recall: 0.5667 - auc: 0.9204 - prc: 0.6766"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 7.7119e-04 - tp: 77.0000 - fp: 17.0000 - tn: 87911.0000 - fn: 59.0000 - accuracy: 0.9991 - precision: 0.8191 - recall: 0.5662 - auc: 0.9246 - prc: 0.6776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0048 - cross entropy: 0.0048 - Brier score: 7.9680e-04 - tp: 98.0000 - fp: 19.0000 - tn: 116533.0000 - fn: 86.0000 - accuracy: 0.9991 - precision: 0.8376 - recall: 0.5326 - auc: 0.9194 - prc: 0.6757"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0048 - cross entropy: 0.0048 - Brier score: 7.8546e-04 - tp: 134.0000 - fp: 23.0000 - tn: 145145.0000 - fn: 106.0000 - accuracy: 0.9991 - precision: 0.8535 - recall: 0.5583 - auc: 0.9191 - prc: 0.6928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 7.6085e-04 - tp: 158.0000 - fp: 27.0000 - tn: 175817.0000 - fn: 126.0000 - accuracy: 0.9991 - precision: 0.8541 - recall: 0.5563 - auc: 0.9208 - prc: 0.6915"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0046 - cross entropy: 0.0046 - Brier score: 7.5796e-04 - tp: 168.0000 - fp: 27.0000 - tn: 181950.0000 - fn: 131.0000 - accuracy: 0.9991 - precision: 0.8615 - recall: 0.5619 - auc: 0.9214 - prc: 0.6995 - val_loss: 0.0034 - val_cross entropy: 0.0034 - val_Brier score: 5.3008e-04 - val_tp: 60.0000 - val_fp: 7.0000 - val_tn: 45480.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_precision: 0.8955 - val_recall: 0.7317 - val_auc: 0.9388 - val_prc: 0.8149\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 9.0016e-04 - cross entropy: 9.0016e-04 - Brier score: 2.7718e-06 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 7.0024e-04 - tp: 34.0000 - fp: 7.0000 - tn: 30660.0000 - fn: 19.0000 - accuracy: 0.9992 - precision: 0.8293 - recall: 0.6415 - auc: 0.9328 - prc: 0.7086             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0045 - cross entropy: 0.0045 - Brier score: 6.8385e-04 - tp: 75.0000 - fp: 12.0000 - tn: 59269.0000 - fn: 36.0000 - accuracy: 0.9992 - precision: 0.8621 - recall: 0.6757 - auc: 0.9403 - prc: 0.7392"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0044 - cross entropy: 0.0044 - Brier score: 7.0019e-04 - tp: 106.0000 - fp: 14.0000 - tn: 87885.0000 - fn: 59.0000 - accuracy: 0.9992 - precision: 0.8833 - recall: 0.6424 - auc: 0.9382 - prc: 0.7498"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 7.4059e-04 - tp: 137.0000 - fp: 21.0000 - tn: 116499.0000 - fn: 79.0000 - accuracy: 0.9991 - precision: 0.8671 - recall: 0.6343 - auc: 0.9292 - prc: 0.7194"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0048 - cross entropy: 0.0048 - Brier score: 7.5284e-04 - tp: 160.0000 - fp: 29.0000 - tn: 145123.0000 - fn: 96.0000 - accuracy: 0.9991 - precision: 0.8466 - recall: 0.6250 - auc: 0.9145 - prc: 0.6919"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0045 - cross entropy: 0.0045 - Brier score: 7.0737e-04 - tp: 178.0000 - fp: 31.0000 - tn: 173761.0000 - fn: 110.0000 - accuracy: 0.9992 - precision: 0.8517 - recall: 0.6181 - auc: 0.9135 - prc: 0.6948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0045 - cross entropy: 0.0045 - Brier score: 7.0728e-04 - tp: 183.0000 - fp: 33.0000 - tn: 181944.0000 - fn: 116.0000 - accuracy: 0.9992 - precision: 0.8472 - recall: 0.6120 - auc: 0.9133 - prc: 0.6901 - val_loss: 0.0033 - val_cross entropy: 0.0033 - val_Brier score: 5.3596e-04 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 24.0000 - val_accuracy: 0.9993 - val_precision: 0.9062 - val_recall: 0.7073 - val_auc: 0.9388 - val_prc: 0.8227\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0011 - cross entropy: 0.0011 - Brier score: 7.6400e-05 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0053 - cross entropy: 0.0053 - Brier score: 9.7915e-04 - tp: 26.0000 - fp: 4.0000 - tn: 30659.0000 - fn: 31.0000 - accuracy: 0.9989 - precision: 0.8667 - recall: 0.4561 - auc: 0.9286 - prc: 0.7026     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0052 - cross entropy: 0.0052 - Brier score: 9.2169e-04 - tp: 57.0000 - fp: 6.0000 - tn: 59273.0000 - fn: 56.0000 - accuracy: 0.9990 - precision: 0.9048 - recall: 0.5044 - auc: 0.9234 - prc: 0.7091"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0049 - cross entropy: 0.0049 - Brier score: 8.4603e-04 - tp: 83.0000 - fp: 12.0000 - tn: 87899.0000 - fn: 70.0000 - accuracy: 0.9991 - precision: 0.8737 - recall: 0.5425 - auc: 0.9169 - prc: 0.6683"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 7.7790e-04 - tp: 104.0000 - fp: 17.0000 - tn: 116530.0000 - fn: 85.0000 - accuracy: 0.9991 - precision: 0.8595 - recall: 0.5503 - auc: 0.9219 - prc: 0.6712"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0045 - cross entropy: 0.0045 - Brier score: 7.5564e-04 - tp: 139.0000 - fp: 27.0000 - tn: 145146.0000 - fn: 96.0000 - accuracy: 0.9992 - precision: 0.8373 - recall: 0.5915 - auc: 0.9306 - prc: 0.6828"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 7.9561e-04 - tp: 165.0000 - fp: 35.0000 - tn: 173760.0000 - fn: 120.0000 - accuracy: 0.9991 - precision: 0.8250 - recall: 0.5789 - auc: 0.9214 - prc: 0.6639"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0048 - cross entropy: 0.0048 - Brier score: 8.0575e-04 - tp: 169.0000 - fp: 35.0000 - tn: 181942.0000 - fn: 130.0000 - accuracy: 0.9991 - precision: 0.8284 - recall: 0.5652 - auc: 0.9183 - prc: 0.6610 - val_loss: 0.0032 - val_cross entropy: 0.0032 - val_Brier score: 5.4781e-04 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 24.0000 - val_accuracy: 0.9993 - val_precision: 0.9062 - val_recall: 0.7073 - val_auc: 0.9389 - val_prc: 0.8321\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0082 - cross entropy: 0.0082 - Brier score: 0.0012 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.7471 - prc: 0.5026"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0058 - cross entropy: 0.0058 - Brier score: 9.8162e-04 - tp: 23.0000 - fp: 3.0000 - tn: 30665.0000 - fn: 29.0000 - accuracy: 0.9990 - precision: 0.8846 - recall: 0.4423 - auc: 0.8923 - prc: 0.5626"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 8.1071e-04 - tp: 53.0000 - fp: 6.0000 - tn: 59284.0000 - fn: 49.0000 - accuracy: 0.9991 - precision: 0.8983 - recall: 0.5196 - auc: 0.9153 - prc: 0.6860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 6.8037e-04 - tp: 75.0000 - fp: 7.0000 - tn: 87919.0000 - fn: 63.0000 - accuracy: 0.9992 - precision: 0.9146 - recall: 0.5435 - auc: 0.9228 - prc: 0.7244"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 7.3167e-04 - tp: 99.0000 - fp: 15.0000 - tn: 116536.0000 - fn: 86.0000 - accuracy: 0.9991 - precision: 0.8684 - recall: 0.5351 - auc: 0.9150 - prc: 0.7053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.4750e-04 - tp: 135.0000 - fp: 21.0000 - tn: 145148.0000 - fn: 104.0000 - accuracy: 0.9991 - precision: 0.8654 - recall: 0.5649 - auc: 0.9151 - prc: 0.7088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.4467e-04 - tp: 162.0000 - fp: 25.0000 - tn: 173769.0000 - fn: 124.0000 - accuracy: 0.9991 - precision: 0.8663 - recall: 0.5664 - auc: 0.9166 - prc: 0.7090"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.4602e-04 - tp: 170.0000 - fp: 27.0000 - tn: 181950.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8629 - recall: 0.5686 - auc: 0.9186 - prc: 0.7075 - val_loss: 0.0031 - val_cross entropy: 0.0031 - val_Brier score: 5.1218e-04 - val_tp: 58.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 24.0000 - val_accuracy: 0.9993 - val_precision: 0.9062 - val_recall: 0.7073 - val_auc: 0.9388 - val_prc: 0.8314\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0141 - cross entropy: 0.0141 - Brier score: 0.0019 - tp: 1.0000 - fp: 0.0000e+00 - tn: 2043.0000 - fn: 4.0000 - accuracy: 0.9980 - precision: 1.0000 - recall: 0.2000 - auc: 0.6971 - prc: 0.3225"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0049 - cross entropy: 0.0049 - Brier score: 8.6339e-04 - tp: 27.0000 - fp: 9.0000 - tn: 30662.0000 - fn: 22.0000 - accuracy: 0.9990 - precision: 0.7500 - recall: 0.5510 - auc: 0.9172 - prc: 0.5983"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0048 - cross entropy: 0.0048 - Brier score: 8.0296e-04 - tp: 67.0000 - fp: 10.0000 - tn: 59268.0000 - fn: 47.0000 - accuracy: 0.9990 - precision: 0.8701 - recall: 0.5877 - auc: 0.9156 - prc: 0.7084"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 7.1956e-04 - tp: 99.0000 - fp: 16.0000 - tn: 87889.0000 - fn: 60.0000 - accuracy: 0.9991 - precision: 0.8609 - recall: 0.6226 - auc: 0.9203 - prc: 0.7307"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 6.7088e-04 - tp: 116.0000 - fp: 21.0000 - tn: 116525.0000 - fn: 74.0000 - accuracy: 0.9992 - precision: 0.8467 - recall: 0.6105 - auc: 0.9200 - prc: 0.7285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.0766e-04 - tp: 141.0000 - fp: 21.0000 - tn: 145160.0000 - fn: 86.0000 - accuracy: 0.9993 - precision: 0.8704 - recall: 0.6211 - auc: 0.9285 - prc: 0.7527"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.6002e-04 - tp: 171.0000 - fp: 24.0000 - tn: 173771.0000 - fn: 114.0000 - accuracy: 0.9992 - precision: 0.8769 - recall: 0.6000 - auc: 0.9235 - prc: 0.7418"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 6.7102e-04 - tp: 178.0000 - fp: 25.0000 - tn: 181952.0000 - fn: 121.0000 - accuracy: 0.9992 - precision: 0.8768 - recall: 0.5953 - auc: 0.9203 - prc: 0.7351 - val_loss: 0.0030 - val_cross entropy: 0.0030 - val_Brier score: 4.8812e-04 - val_tp: 65.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9155 - val_recall: 0.7927 - val_auc: 0.9388 - val_prc: 0.8293\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 7.5753e-04 - cross entropy: 7.5753e-04 - Brier score: 7.6389e-06 - tp: 3.0000 - fp: 0.0000e+00 - tn: 2045.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.0024 - cross entropy: 0.0024 - Brier score: 3.4542e-04 - tp: 36.0000 - fp: 3.0000 - tn: 28625.0000 - fn: 8.0000 - accuracy: 0.9996 - precision: 0.9231 - recall: 0.8182 - auc: 0.9655 - prc: 0.8512              "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.0025 - cross entropy: 0.0025 - Brier score: 3.9026e-04 - tp: 66.0000 - fp: 5.0000 - tn: 55206.0000 - fn: 19.0000 - accuracy: 0.9996 - precision: 0.9296 - recall: 0.7765 - auc: 0.9642 - prc: 0.8400"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 4.8022e-04 - tp: 85.0000 - fp: 7.0000 - tn: 81791.0000 - fn: 37.0000 - accuracy: 0.9995 - precision: 0.9239 - recall: 0.6967 - auc: 0.9336 - prc: 0.7796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54/90 [=================>............] - ETA: 0s - loss: 0.0032 - cross entropy: 0.0032 - Brier score: 5.0804e-04 - tp: 114.0000 - fp: 13.0000 - tn: 110414.0000 - fn: 51.0000 - accuracy: 0.9994 - precision: 0.8976 - recall: 0.6909 - auc: 0.9325 - prc: 0.7608"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67/90 [=====================>........] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 5.6222e-04 - tp: 142.0000 - fp: 18.0000 - tn: 136984.0000 - fn: 72.0000 - accuracy: 0.9993 - precision: 0.8875 - recall: 0.6636 - auc: 0.9266 - prc: 0.7487"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "81/90 [==========================>...] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.0617e-04 - tp: 179.0000 - fp: 19.0000 - tn: 165594.0000 - fn: 96.0000 - accuracy: 0.9993 - precision: 0.9040 - recall: 0.6509 - auc: 0.9208 - prc: 0.7467"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.3325e-04 - tp: 191.0000 - fp: 25.0000 - tn: 181952.0000 - fn: 108.0000 - accuracy: 0.9993 - precision: 0.8843 - recall: 0.6388 - auc: 0.9170 - prc: 0.7323 - val_loss: 0.0030 - val_cross entropy: 0.0030 - val_Brier score: 4.8228e-04 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9167 - val_recall: 0.8049 - val_auc: 0.9388 - val_prc: 0.8301\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0080 - cross entropy: 0.0080 - Brier score: 0.0014 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 2045.0000 - fn: 3.0000 - accuracy: 0.9985 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.6632 - prc: 0.1044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 5.8304e-04 - tp: 23.0000 - fp: 4.0000 - tn: 30677.0000 - fn: 16.0000 - accuracy: 0.9993 - precision: 0.8519 - recall: 0.5897 - auc: 0.8963 - prc: 0.6521         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.7984e-04 - tp: 61.0000 - fp: 7.0000 - tn: 59280.0000 - fn: 44.0000 - accuracy: 0.9991 - precision: 0.8971 - recall: 0.5810 - auc: 0.9180 - prc: 0.7257"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0045 - cross entropy: 0.0045 - Brier score: 8.1202e-04 - tp: 95.0000 - fp: 17.0000 - tn: 87889.0000 - fn: 63.0000 - accuracy: 0.9991 - precision: 0.8482 - recall: 0.6013 - auc: 0.9262 - prc: 0.7155"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0046 - cross entropy: 0.0046 - Brier score: 8.2841e-04 - tp: 123.0000 - fp: 21.0000 - tn: 116502.0000 - fn: 90.0000 - accuracy: 0.9990 - precision: 0.8542 - recall: 0.5775 - auc: 0.9216 - prc: 0.7117"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.7832e-04 - tp: 150.0000 - fp: 27.0000 - tn: 145127.0000 - fn: 104.0000 - accuracy: 0.9991 - precision: 0.8475 - recall: 0.5906 - auc: 0.9262 - prc: 0.7241"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 7.6383e-04 - tp: 168.0000 - fp: 33.0000 - tn: 173757.0000 - fn: 122.0000 - accuracy: 0.9991 - precision: 0.8358 - recall: 0.5793 - auc: 0.9232 - prc: 0.7150"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 7.6081e-04 - tp: 173.0000 - fp: 35.0000 - tn: 181942.0000 - fn: 126.0000 - accuracy: 0.9991 - precision: 0.8317 - recall: 0.5786 - auc: 0.9254 - prc: 0.7097 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 4.7943e-04 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9167 - val_recall: 0.8049 - val_auc: 0.9388 - val_prc: 0.8330\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.8039e-04 - tp: 1.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.5000 - auc: 0.9974 - prc: 0.5633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.4525e-04 - tp: 16.0000 - fp: 3.0000 - tn: 30681.0000 - fn: 20.0000 - accuracy: 0.9993 - precision: 0.8421 - recall: 0.4444 - auc: 0.9154 - prc: 0.5950 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.3626e-04 - tp: 43.0000 - fp: 7.0000 - tn: 59305.0000 - fn: 37.0000 - accuracy: 0.9993 - precision: 0.8600 - recall: 0.5375 - auc: 0.9178 - prc: 0.6767"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.6116e-04 - tp: 77.0000 - fp: 10.0000 - tn: 87921.0000 - fn: 56.0000 - accuracy: 0.9993 - precision: 0.8851 - recall: 0.5789 - auc: 0.9238 - prc: 0.7067"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 7.1711e-04 - tp: 108.0000 - fp: 19.0000 - tn: 116533.0000 - fn: 76.0000 - accuracy: 0.9992 - precision: 0.8504 - recall: 0.5870 - auc: 0.9229 - prc: 0.6806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.0610e-04 - tp: 140.0000 - fp: 21.0000 - tn: 145152.0000 - fn: 95.0000 - accuracy: 0.9992 - precision: 0.8696 - recall: 0.5957 - auc: 0.9289 - prc: 0.7035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.4633e-04 - tp: 166.0000 - fp: 28.0000 - tn: 173768.0000 - fn: 118.0000 - accuracy: 0.9992 - precision: 0.8557 - recall: 0.5845 - auc: 0.9233 - prc: 0.6860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.4700e-04 - tp: 175.0000 - fp: 29.0000 - tn: 181948.0000 - fn: 124.0000 - accuracy: 0.9992 - precision: 0.8578 - recall: 0.5853 - auc: 0.9238 - prc: 0.6897 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 4.7884e-04 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9167 - val_recall: 0.8049 - val_auc: 0.9388 - val_prc: 0.8350\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0024 - cross entropy: 0.0024 - Brier score: 5.1770e-04 - tp: 4.0000 - fp: 1.0000 - tn: 2043.0000 - fn: 0.0000e+00 - accuracy: 0.9995 - precision: 0.8000 - recall: 1.0000 - auc: 0.9998 - prc: 0.8723"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0030 - cross entropy: 0.0030 - Brier score: 4.5984e-04 - tp: 34.0000 - fp: 5.0000 - tn: 30671.0000 - fn: 10.0000 - accuracy: 0.9995 - precision: 0.8718 - recall: 0.7727 - auc: 0.9310 - prc: 0.7510 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30/90 [=========>....................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.3856e-04 - tp: 59.0000 - fp: 7.0000 - tn: 61335.0000 - fn: 39.0000 - accuracy: 0.9993 - precision: 0.8939 - recall: 0.6020 - auc: 0.9329 - prc: 0.7572"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "45/90 [==============>...............] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.5127e-04 - tp: 80.0000 - fp: 11.0000 - tn: 92010.0000 - fn: 59.0000 - accuracy: 0.9992 - precision: 0.8791 - recall: 0.5755 - auc: 0.9235 - prc: 0.7330"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "60/90 [===================>..........] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.3839e-04 - tp: 112.0000 - fp: 16.0000 - tn: 122665.0000 - fn: 87.0000 - accuracy: 0.9992 - precision: 0.8750 - recall: 0.5628 - auc: 0.9136 - prc: 0.7067"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "74/90 [=======================>......] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.1160e-04 - tp: 141.0000 - fp: 22.0000 - tn: 151287.0000 - fn: 102.0000 - accuracy: 0.9992 - precision: 0.8650 - recall: 0.5802 - auc: 0.9167 - prc: 0.7074"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "89/90 [============================>.] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.1933e-04 - tp: 177.0000 - fp: 30.0000 - tn: 181943.0000 - fn: 122.0000 - accuracy: 0.9992 - precision: 0.8551 - recall: 0.5920 - auc: 0.9171 - prc: 0.7144"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.1931e-04 - tp: 177.0000 - fp: 30.0000 - tn: 181947.0000 - fn: 122.0000 - accuracy: 0.9992 - precision: 0.8551 - recall: 0.5920 - auc: 0.9171 - prc: 0.7144 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 4.8724e-04 - val_tp: 64.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 18.0000 - val_accuracy: 0.9995 - val_precision: 0.9275 - val_recall: 0.7805 - val_auc: 0.9388 - val_prc: 0.8409\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 8.6128e-04 - tp: 3.0000 - fp: 0.0000e+00 - tn: 2043.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.6000 - auc: 0.9997 - prc: 0.9207"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0049 - cross entropy: 0.0049 - Brier score: 9.1922e-04 - tp: 31.0000 - fp: 7.0000 - tn: 30657.0000 - fn: 25.0000 - accuracy: 0.9990 - precision: 0.8158 - recall: 0.5536 - auc: 0.9276 - prc: 0.6194 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0044 - cross entropy: 0.0044 - Brier score: 8.0396e-04 - tp: 61.0000 - fp: 13.0000 - tn: 59275.0000 - fn: 43.0000 - accuracy: 0.9991 - precision: 0.8243 - recall: 0.5865 - auc: 0.9222 - prc: 0.6891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.4837e-04 - tp: 82.0000 - fp: 17.0000 - tn: 87906.0000 - fn: 59.0000 - accuracy: 0.9991 - precision: 0.8283 - recall: 0.5816 - auc: 0.9282 - prc: 0.6836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.5617e-04 - tp: 107.0000 - fp: 20.0000 - tn: 116524.0000 - fn: 85.0000 - accuracy: 0.9991 - precision: 0.8425 - recall: 0.5573 - auc: 0.9288 - prc: 0.7011"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.4583e-04 - tp: 134.0000 - fp: 24.0000 - tn: 145146.0000 - fn: 104.0000 - accuracy: 0.9991 - precision: 0.8481 - recall: 0.5630 - auc: 0.9277 - prc: 0.7032"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.4135e-04 - tp: 162.0000 - fp: 26.0000 - tn: 173768.0000 - fn: 124.0000 - accuracy: 0.9991 - precision: 0.8617 - recall: 0.5664 - auc: 0.9257 - prc: 0.7072"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0042 - cross entropy: 0.0042 - Brier score: 7.5652e-04 - tp: 167.0000 - fp: 27.0000 - tn: 181950.0000 - fn: 132.0000 - accuracy: 0.9991 - precision: 0.8608 - recall: 0.5585 - auc: 0.9238 - prc: 0.6964 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7200e-04 - val_tp: 66.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9167 - val_recall: 0.8049 - val_auc: 0.9388 - val_prc: 0.8410\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 8.1860e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.9998 - prc: 0.9088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.0050 - cross entropy: 0.0050 - Brier score: 8.6951e-04 - tp: 23.0000 - fp: 5.0000 - tn: 28620.0000 - fn: 24.0000 - accuracy: 0.9990 - precision: 0.8214 - recall: 0.4894 - auc: 0.8817 - prc: 0.6457 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/90 [========>.....................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.6402e-04 - tp: 59.0000 - fp: 6.0000 - tn: 57241.0000 - fn: 38.0000 - accuracy: 0.9992 - precision: 0.9077 - recall: 0.6082 - auc: 0.9166 - prc: 0.7614"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42/90 [=============>................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.9154e-04 - tp: 79.0000 - fp: 15.0000 - tn: 85865.0000 - fn: 57.0000 - accuracy: 0.9992 - precision: 0.8404 - recall: 0.5809 - auc: 0.9256 - prc: 0.7345"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56/90 [=================>............] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.2358e-04 - tp: 106.0000 - fp: 21.0000 - tn: 114482.0000 - fn: 79.0000 - accuracy: 0.9991 - precision: 0.8346 - recall: 0.5730 - auc: 0.9207 - prc: 0.7127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70/90 [======================>.......] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.9853e-04 - tp: 136.0000 - fp: 24.0000 - tn: 143104.0000 - fn: 96.0000 - accuracy: 0.9992 - precision: 0.8500 - recall: 0.5862 - auc: 0.9172 - prc: 0.7213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84/90 [===========================>..] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.1012e-04 - tp: 167.0000 - fp: 31.0000 - tn: 171719.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8434 - recall: 0.5922 - auc: 0.9229 - prc: 0.7247"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.1767e-04 - tp: 177.0000 - fp: 34.0000 - tn: 181943.0000 - fn: 122.0000 - accuracy: 0.9991 - precision: 0.8389 - recall: 0.5920 - auc: 0.9239 - prc: 0.7223 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.6891e-04 - val_tp: 65.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9155 - val_recall: 0.7927 - val_auc: 0.9388 - val_prc: 0.8418\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0052 - cross entropy: 0.0052 - Brier score: 4.9231e-04 - tp: 3.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.7500 - auc: 0.8739 - prc: 0.7518"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0044 - cross entropy: 0.0044 - Brier score: 7.9049e-04 - tp: 32.0000 - fp: 5.0000 - tn: 30660.0000 - fn: 23.0000 - accuracy: 0.9991 - precision: 0.8649 - recall: 0.5818 - auc: 0.9083 - prc: 0.7451 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.4874e-04 - tp: 55.0000 - fp: 8.0000 - tn: 59287.0000 - fn: 42.0000 - accuracy: 0.9992 - precision: 0.8730 - recall: 0.5670 - auc: 0.9165 - prc: 0.6927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.1012e-04 - tp: 80.0000 - fp: 11.0000 - tn: 87912.0000 - fn: 61.0000 - accuracy: 0.9992 - precision: 0.8791 - recall: 0.5674 - auc: 0.9175 - prc: 0.7124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.1968e-04 - tp: 106.0000 - fp: 17.0000 - tn: 116532.0000 - fn: 81.0000 - accuracy: 0.9992 - precision: 0.8618 - recall: 0.5668 - auc: 0.9216 - prc: 0.7104"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.1198e-04 - tp: 131.0000 - fp: 21.0000 - tn: 145157.0000 - fn: 99.0000 - accuracy: 0.9992 - precision: 0.8618 - recall: 0.5696 - auc: 0.9252 - prc: 0.7076"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.5275e-04 - tp: 160.0000 - fp: 27.0000 - tn: 173768.0000 - fn: 125.0000 - accuracy: 0.9991 - precision: 0.8556 - recall: 0.5614 - auc: 0.9272 - prc: 0.7044"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.5757e-04 - tp: 166.0000 - fp: 27.0000 - tn: 181950.0000 - fn: 133.0000 - accuracy: 0.9991 - precision: 0.8601 - recall: 0.5552 - auc: 0.9255 - prc: 0.7017 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.6881e-04 - val_tp: 64.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 18.0000 - val_accuracy: 0.9995 - val_precision: 0.9143 - val_recall: 0.7805 - val_auc: 0.9388 - val_prc: 0.8419\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0099 - cross entropy: 0.0099 - Brier score: 0.0018 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 4.0000 - accuracy: 0.9980 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.7478 - prc: 0.5028"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.5408e-04 - tp: 35.0000 - fp: 4.0000 - tn: 30662.0000 - fn: 19.0000 - accuracy: 0.9993 - precision: 0.8974 - recall: 0.6481 - auc: 0.9622 - prc: 0.7581         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.2572e-04 - tp: 64.0000 - fp: 7.0000 - tn: 59284.0000 - fn: 37.0000 - accuracy: 0.9993 - precision: 0.9014 - recall: 0.6337 - auc: 0.9548 - prc: 0.7853"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.7587e-04 - tp: 85.0000 - fp: 15.0000 - tn: 87909.0000 - fn: 55.0000 - accuracy: 0.9992 - precision: 0.8500 - recall: 0.6071 - auc: 0.9348 - prc: 0.7219"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.6206e-04 - tp: 116.0000 - fp: 18.0000 - tn: 116530.0000 - fn: 72.0000 - accuracy: 0.9992 - precision: 0.8657 - recall: 0.6170 - auc: 0.9327 - prc: 0.7349"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.8583e-04 - tp: 143.0000 - fp: 25.0000 - tn: 145148.0000 - fn: 92.0000 - accuracy: 0.9992 - precision: 0.8512 - recall: 0.6085 - auc: 0.9332 - prc: 0.7305"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.8895e-04 - tp: 176.0000 - fp: 28.0000 - tn: 173766.0000 - fn: 110.0000 - accuracy: 0.9992 - precision: 0.8627 - recall: 0.6154 - auc: 0.9292 - prc: 0.7285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.7869e-04 - tp: 185.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 114.0000 - accuracy: 0.9992 - precision: 0.8685 - recall: 0.6187 - auc: 0.9289 - prc: 0.7328 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.5812e-04 - val_tp: 67.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.9178 - val_recall: 0.8171 - val_auc: 0.9449 - val_prc: 0.8473\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 7.0792e-04 - cross entropy: 7.0792e-04 - Brier score: 7.1331e-05 - tp: 4.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.6722e-04 - tp: 37.0000 - fp: 4.0000 - tn: 30658.0000 - fn: 21.0000 - accuracy: 0.9992 - precision: 0.9024 - recall: 0.6379 - auc: 0.9303 - prc: 0.7884             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0044 - cross entropy: 0.0044 - Brier score: 7.6829e-04 - tp: 65.0000 - fp: 8.0000 - tn: 59274.0000 - fn: 45.0000 - accuracy: 0.9991 - precision: 0.8904 - recall: 0.5909 - auc: 0.9127 - prc: 0.7111"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 7.2674e-04 - tp: 94.0000 - fp: 11.0000 - tn: 87898.0000 - fn: 61.0000 - accuracy: 0.9992 - precision: 0.8952 - recall: 0.6065 - auc: 0.9217 - prc: 0.7272"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.8821e-04 - tp: 112.0000 - fp: 17.0000 - tn: 116533.0000 - fn: 74.0000 - accuracy: 0.9992 - precision: 0.8682 - recall: 0.6022 - auc: 0.9184 - prc: 0.7019"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.6004e-04 - tp: 147.0000 - fp: 20.0000 - tn: 145153.0000 - fn: 88.0000 - accuracy: 0.9993 - precision: 0.8802 - recall: 0.6255 - auc: 0.9246 - prc: 0.7302"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.0201e-04 - tp: 177.0000 - fp: 30.0000 - tn: 173762.0000 - fn: 111.0000 - accuracy: 0.9992 - precision: 0.8551 - recall: 0.6146 - auc: 0.9227 - prc: 0.7156"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.9306e-04 - tp: 184.0000 - fp: 31.0000 - tn: 181946.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8558 - recall: 0.6154 - auc: 0.9222 - prc: 0.7129 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7472e-04 - val_tp: 64.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 18.0000 - val_accuracy: 0.9995 - val_precision: 0.9275 - val_recall: 0.7805 - val_auc: 0.9389 - val_prc: 0.8439\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 3.9996e-04 - cross entropy: 3.9996e-04 - Brier score: 1.6031e-06 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 2048.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.0000e+00 - prc: 0.0000e+00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.3058e-04 - tp: 29.0000 - fp: 4.0000 - tn: 30669.0000 - fn: 18.0000 - accuracy: 0.9993 - precision: 0.8788 - recall: 0.6170 - auc: 0.9248 - prc: 0.7508                                 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.8175e-04 - tp: 45.0000 - fp: 12.0000 - tn: 59300.0000 - fn: 35.0000 - accuracy: 0.9992 - precision: 0.7895 - recall: 0.5625 - auc: 0.9054 - prc: 0.6535"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42/90 [=============>................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.6973e-04 - tp: 86.0000 - fp: 15.0000 - tn: 85864.0000 - fn: 51.0000 - accuracy: 0.9992 - precision: 0.8515 - recall: 0.6277 - auc: 0.9226 - prc: 0.7183"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56/90 [=================>............] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5568e-04 - tp: 115.0000 - fp: 22.0000 - tn: 114487.0000 - fn: 64.0000 - accuracy: 0.9993 - precision: 0.8394 - recall: 0.6425 - auc: 0.9210 - prc: 0.7166"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70/90 [======================>.......] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.9393e-04 - tp: 154.0000 - fp: 26.0000 - tn: 143092.0000 - fn: 88.0000 - accuracy: 0.9992 - precision: 0.8556 - recall: 0.6364 - auc: 0.9207 - prc: 0.7253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84/90 [===========================>..] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.6510e-04 - tp: 177.0000 - fp: 30.0000 - tn: 171723.0000 - fn: 102.0000 - accuracy: 0.9992 - precision: 0.8551 - recall: 0.6344 - auc: 0.9204 - prc: 0.7249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.5706e-04 - tp: 191.0000 - fp: 31.0000 - tn: 181946.0000 - fn: 108.0000 - accuracy: 0.9992 - precision: 0.8604 - recall: 0.6388 - auc: 0.9240 - prc: 0.7368 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.8355e-04 - val_tp: 60.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_precision: 0.9375 - val_recall: 0.7317 - val_auc: 0.9388 - val_prc: 0.8442\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 6.8278e-04 - cross entropy: 6.8278e-04 - Brier score: 9.8541e-05 - tp: 1.0000 - fp: 0.0000e+00 - tn: 2047.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.8909e-04 - tp: 23.0000 - fp: 9.0000 - tn: 30673.0000 - fn: 15.0000 - accuracy: 0.9992 - precision: 0.7188 - recall: 0.6053 - auc: 0.9069 - prc: 0.6454             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.0926e-04 - tp: 51.0000 - fp: 11.0000 - tn: 59301.0000 - fn: 29.0000 - accuracy: 0.9993 - precision: 0.8226 - recall: 0.6375 - auc: 0.9053 - prc: 0.6739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.9874e-04 - tp: 79.0000 - fp: 17.0000 - tn: 87915.0000 - fn: 53.0000 - accuracy: 0.9992 - precision: 0.8229 - recall: 0.5985 - auc: 0.9082 - prc: 0.6860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 7.0393e-04 - tp: 110.0000 - fp: 23.0000 - tn: 116531.0000 - fn: 72.0000 - accuracy: 0.9992 - precision: 0.8271 - recall: 0.6044 - auc: 0.9196 - prc: 0.7125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.8635e-04 - tp: 137.0000 - fp: 28.0000 - tn: 145157.0000 - fn: 86.0000 - accuracy: 0.9992 - precision: 0.8303 - recall: 0.6143 - auc: 0.9207 - prc: 0.7125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.1014e-04 - tp: 169.0000 - fp: 30.0000 - tn: 173770.0000 - fn: 111.0000 - accuracy: 0.9992 - precision: 0.8492 - recall: 0.6036 - auc: 0.9188 - prc: 0.7159"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.2760e-04 - tp: 180.0000 - fp: 33.0000 - tn: 181944.0000 - fn: 119.0000 - accuracy: 0.9992 - precision: 0.8451 - recall: 0.6020 - auc: 0.9223 - prc: 0.7170 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 4.9822e-04 - val_tp: 59.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9365 - val_recall: 0.7195 - val_auc: 0.9388 - val_prc: 0.8441\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0044 - cross entropy: 0.0044 - Brier score: 0.0014 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2043.0000 - fn: 3.0000 - accuracy: 0.9985 - precision: 1.0000 - recall: 0.4000 - auc: 0.9990 - prc: 0.7039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 7.6724e-04 - tp: 30.0000 - fp: 4.0000 - tn: 30662.0000 - fn: 24.0000 - accuracy: 0.9991 - precision: 0.8824 - recall: 0.5556 - auc: 0.9345 - prc: 0.7474"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 6.2442e-04 - tp: 64.0000 - fp: 10.0000 - tn: 59285.0000 - fn: 33.0000 - accuracy: 0.9993 - precision: 0.8649 - recall: 0.6598 - auc: 0.9427 - prc: 0.7761"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.4658e-04 - tp: 90.0000 - fp: 15.0000 - tn: 87906.0000 - fn: 53.0000 - accuracy: 0.9992 - precision: 0.8571 - recall: 0.6294 - auc: 0.9294 - prc: 0.7529"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.3115e-04 - tp: 120.0000 - fp: 18.0000 - tn: 116525.0000 - fn: 73.0000 - accuracy: 0.9992 - precision: 0.8696 - recall: 0.6218 - auc: 0.9267 - prc: 0.7569"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.3601e-04 - tp: 146.0000 - fp: 21.0000 - tn: 145148.0000 - fn: 93.0000 - accuracy: 0.9992 - precision: 0.8743 - recall: 0.6109 - auc: 0.9344 - prc: 0.7575"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5085e-04 - tp: 174.0000 - fp: 27.0000 - tn: 173769.0000 - fn: 110.0000 - accuracy: 0.9992 - precision: 0.8657 - recall: 0.6127 - auc: 0.9270 - prc: 0.7394"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5225e-04 - tp: 181.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 118.0000 - accuracy: 0.9992 - precision: 0.8660 - recall: 0.6054 - auc: 0.9273 - prc: 0.7439 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.8637e-04 - val_tp: 60.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_precision: 0.9375 - val_recall: 0.7317 - val_auc: 0.9388 - val_prc: 0.8438\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0011 - cross entropy: 0.0011 - Brier score: 2.2777e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2045.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.6667 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0030 - cross entropy: 0.0030 - Brier score: 4.9610e-04 - tp: 37.0000 - fp: 5.0000 - tn: 30665.0000 - fn: 13.0000 - accuracy: 0.9994 - precision: 0.8810 - recall: 0.7400 - auc: 0.9493 - prc: 0.7942 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.3315e-04 - tp: 59.0000 - fp: 9.0000 - tn: 59288.0000 - fn: 36.0000 - accuracy: 0.9992 - precision: 0.8676 - recall: 0.6211 - auc: 0.9307 - prc: 0.7523"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.0327e-04 - tp: 84.0000 - fp: 12.0000 - tn: 87918.0000 - fn: 50.0000 - accuracy: 0.9993 - precision: 0.8750 - recall: 0.6269 - auc: 0.9394 - prc: 0.7631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56/90 [=================>............] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.2873e-04 - tp: 113.0000 - fp: 18.0000 - tn: 114488.0000 - fn: 69.0000 - accuracy: 0.9992 - precision: 0.8626 - recall: 0.6209 - auc: 0.9415 - prc: 0.7683"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "69/90 [======================>.......] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.8461e-04 - tp: 141.0000 - fp: 21.0000 - tn: 141057.0000 - fn: 93.0000 - accuracy: 0.9992 - precision: 0.8704 - recall: 0.6026 - auc: 0.9371 - prc: 0.7383"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "83/90 [==========================>...] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.7201e-04 - tp: 170.0000 - fp: 26.0000 - tn: 169680.0000 - fn: 108.0000 - accuracy: 0.9992 - precision: 0.8673 - recall: 0.6115 - auc: 0.9361 - prc: 0.7311"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.5348e-04 - tp: 186.0000 - fp: 26.0000 - tn: 181951.0000 - fn: 113.0000 - accuracy: 0.9992 - precision: 0.8774 - recall: 0.6221 - auc: 0.9355 - prc: 0.7402 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.6483e-04 - val_tp: 65.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9155 - val_recall: 0.7927 - val_auc: 0.9388 - val_prc: 0.8427\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 3.9223e-04 - cross entropy: 3.9223e-04 - Brier score: 5.7929e-06 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 5.4619e-04 - tp: 35.0000 - fp: 5.0000 - tn: 30666.0000 - fn: 14.0000 - accuracy: 0.9994 - precision: 0.8750 - recall: 0.7143 - auc: 0.9381 - prc: 0.7684             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 7.0361e-04 - tp: 57.0000 - fp: 12.0000 - tn: 59287.0000 - fn: 36.0000 - accuracy: 0.9992 - precision: 0.8261 - recall: 0.6129 - auc: 0.9347 - prc: 0.7176"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.3057e-04 - tp: 87.0000 - fp: 17.0000 - tn: 87911.0000 - fn: 49.0000 - accuracy: 0.9993 - precision: 0.8365 - recall: 0.6397 - auc: 0.9479 - prc: 0.7573"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.2824e-04 - tp: 115.0000 - fp: 20.0000 - tn: 116535.0000 - fn: 66.0000 - accuracy: 0.9993 - precision: 0.8519 - recall: 0.6354 - auc: 0.9468 - prc: 0.7524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.2634e-04 - tp: 156.0000 - fp: 27.0000 - tn: 145132.0000 - fn: 93.0000 - accuracy: 0.9992 - precision: 0.8525 - recall: 0.6265 - auc: 0.9309 - prc: 0.7222"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.8379e-04 - tp: 187.0000 - fp: 34.0000 - tn: 173757.0000 - fn: 102.0000 - accuracy: 0.9992 - precision: 0.8462 - recall: 0.6471 - auc: 0.9335 - prc: 0.7292"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.7939e-04 - tp: 193.0000 - fp: 35.0000 - tn: 181942.0000 - fn: 106.0000 - accuracy: 0.9992 - precision: 0.8465 - recall: 0.6455 - auc: 0.9340 - prc: 0.7279 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 5.1275e-04 - val_tp: 58.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9355 - val_recall: 0.7073 - val_auc: 0.9449 - val_prc: 0.8509\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.2705e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.8743 - prc: 0.7519"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.4870e-04 - tp: 29.0000 - fp: 2.0000 - tn: 30668.0000 - fn: 21.0000 - accuracy: 0.9993 - precision: 0.9355 - recall: 0.5800 - auc: 0.9494 - prc: 0.7803 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.6928e-04 - tp: 59.0000 - fp: 9.0000 - tn: 59278.0000 - fn: 46.0000 - accuracy: 0.9991 - precision: 0.8676 - recall: 0.5619 - auc: 0.9374 - prc: 0.7424"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 7.2287e-04 - tp: 85.0000 - fp: 14.0000 - tn: 87899.0000 - fn: 66.0000 - accuracy: 0.9991 - precision: 0.8586 - recall: 0.5629 - auc: 0.9397 - prc: 0.7555"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 7.0246e-04 - tp: 113.0000 - fp: 17.0000 - tn: 116521.0000 - fn: 85.0000 - accuracy: 0.9991 - precision: 0.8692 - recall: 0.5707 - auc: 0.9336 - prc: 0.7563"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.9313e-04 - tp: 140.0000 - fp: 22.0000 - tn: 145145.0000 - fn: 101.0000 - accuracy: 0.9992 - precision: 0.8642 - recall: 0.5809 - auc: 0.9287 - prc: 0.7425"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.8530e-04 - tp: 170.0000 - fp: 28.0000 - tn: 173767.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8586 - recall: 0.5965 - auc: 0.9291 - prc: 0.7329"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.7560e-04 - tp: 180.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 119.0000 - accuracy: 0.9992 - precision: 0.8654 - recall: 0.6020 - auc: 0.9290 - prc: 0.7396 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.8990e-04 - val_tp: 59.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9365 - val_recall: 0.7195 - val_auc: 0.9449 - val_prc: 0.8503\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0101 - cross entropy: 0.0101 - Brier score: 9.7782e-04 - tp: 1.0000 - fp: 2.0000 - tn: 2045.0000 - fn: 0.0000e+00 - accuracy: 0.9990 - precision: 0.3333 - recall: 1.0000 - auc: 0.9995 - prc: 0.3333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.0178e-04 - tp: 40.0000 - fp: 4.0000 - tn: 30660.0000 - fn: 16.0000 - accuracy: 0.9993 - precision: 0.9091 - recall: 0.7143 - auc: 0.9547 - prc: 0.7704 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.2138e-04 - tp: 71.0000 - fp: 6.0000 - tn: 59280.0000 - fn: 35.0000 - accuracy: 0.9993 - precision: 0.9221 - recall: 0.6698 - auc: 0.9380 - prc: 0.7694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 6.6613e-04 - tp: 93.0000 - fp: 9.0000 - tn: 87905.0000 - fn: 57.0000 - accuracy: 0.9993 - precision: 0.9118 - recall: 0.6200 - auc: 0.9292 - prc: 0.7494"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.3110e-04 - tp: 125.0000 - fp: 17.0000 - tn: 116526.0000 - fn: 68.0000 - accuracy: 0.9993 - precision: 0.8803 - recall: 0.6477 - auc: 0.9293 - prc: 0.7598"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.2328e-04 - tp: 155.0000 - fp: 21.0000 - tn: 145149.0000 - fn: 83.0000 - accuracy: 0.9993 - precision: 0.8807 - recall: 0.6513 - auc: 0.9299 - prc: 0.7609"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.5227e-04 - tp: 182.0000 - fp: 28.0000 - tn: 175811.0000 - fn: 107.0000 - accuracy: 0.9992 - precision: 0.8667 - recall: 0.6298 - auc: 0.9335 - prc: 0.7612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.4978e-04 - tp: 188.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 111.0000 - accuracy: 0.9992 - precision: 0.8704 - recall: 0.6288 - auc: 0.9307 - prc: 0.7594 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7567e-04 - val_tp: 63.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 19.0000 - val_accuracy: 0.9995 - val_precision: 0.9265 - val_recall: 0.7683 - val_auc: 0.9388 - val_prc: 0.8439\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0063 - cross entropy: 0.0063 - Brier score: 0.0013 - tp: 2.0000 - fp: 1.0000 - tn: 2043.0000 - fn: 2.0000 - accuracy: 0.9985 - precision: 0.6667 - recall: 0.5000 - auc: 0.8737 - prc: 0.6236"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.9237e-04 - tp: 25.0000 - fp: 11.0000 - tn: 30669.0000 - fn: 15.0000 - accuracy: 0.9992 - precision: 0.6944 - recall: 0.6250 - auc: 0.9619 - prc: 0.6747"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30/90 [=========>....................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 7.0266e-04 - tp: 61.0000 - fp: 16.0000 - tn: 61328.0000 - fn: 35.0000 - accuracy: 0.9992 - precision: 0.7922 - recall: 0.6354 - auc: 0.9473 - prc: 0.7321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 7.4345e-04 - tp: 96.0000 - fp: 21.0000 - tn: 89940.0000 - fn: 55.0000 - accuracy: 0.9992 - precision: 0.8205 - recall: 0.6358 - auc: 0.9364 - prc: 0.7112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58/90 [==================>...........] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.6313e-04 - tp: 126.0000 - fp: 25.0000 - tn: 118555.0000 - fn: 78.0000 - accuracy: 0.9991 - precision: 0.8344 - recall: 0.6176 - auc: 0.9305 - prc: 0.7122"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72/90 [=======================>......] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 7.3739e-04 - tp: 156.0000 - fp: 27.0000 - tn: 147178.0000 - fn: 95.0000 - accuracy: 0.9992 - precision: 0.8525 - recall: 0.6215 - auc: 0.9334 - prc: 0.7238"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.3369e-04 - tp: 179.0000 - fp: 29.0000 - tn: 175806.0000 - fn: 114.0000 - accuracy: 0.9992 - precision: 0.8606 - recall: 0.6109 - auc: 0.9274 - prc: 0.7150"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 7.1788e-04 - tp: 183.0000 - fp: 29.0000 - tn: 181948.0000 - fn: 116.0000 - accuracy: 0.9992 - precision: 0.8632 - recall: 0.6120 - auc: 0.9289 - prc: 0.7194 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.6391e-04 - val_tp: 65.0000 - val_fp: 6.0000 - val_tn: 45481.0000 - val_fn: 17.0000 - val_accuracy: 0.9995 - val_precision: 0.9155 - val_recall: 0.7927 - val_auc: 0.9388 - val_prc: 0.8454\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 5.3786e-04 - cross entropy: 5.3786e-04 - Brier score: 2.3608e-05 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0027 - cross entropy: 0.0027 - Brier score: 4.7010e-04 - tp: 35.0000 - fp: 3.0000 - tn: 30669.0000 - fn: 13.0000 - accuracy: 0.9995 - precision: 0.9211 - recall: 0.7292 - auc: 0.9367 - prc: 0.7977             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30/90 [=========>....................] - ETA: 0s - loss: 0.0025 - cross entropy: 0.0025 - Brier score: 4.5537e-04 - tp: 63.0000 - fp: 4.0000 - tn: 61346.0000 - fn: 27.0000 - accuracy: 0.9995 - precision: 0.9403 - recall: 0.7000 - auc: 0.9382 - prc: 0.8082"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0027 - cross entropy: 0.0027 - Brier score: 4.7792e-04 - tp: 102.0000 - fp: 9.0000 - tn: 89963.0000 - fn: 38.0000 - accuracy: 0.9995 - precision: 0.9189 - recall: 0.7286 - auc: 0.9422 - prc: 0.8024"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58/90 [==================>...........] - ETA: 0s - loss: 0.0029 - cross entropy: 0.0029 - Brier score: 5.1905e-04 - tp: 130.0000 - fp: 14.0000 - tn: 118584.0000 - fn: 56.0000 - accuracy: 0.9994 - precision: 0.9028 - recall: 0.6989 - auc: 0.9482 - prc: 0.7850"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72/90 [=======================>......] - ETA: 0s - loss: 0.0030 - cross entropy: 0.0030 - Brier score: 5.3959e-04 - tp: 162.0000 - fp: 22.0000 - tn: 147204.0000 - fn: 68.0000 - accuracy: 0.9994 - precision: 0.8804 - recall: 0.7043 - auc: 0.9449 - prc: 0.7676"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.0183e-04 - tp: 192.0000 - fp: 28.0000 - tn: 175817.0000 - fn: 91.0000 - accuracy: 0.9993 - precision: 0.8727 - recall: 0.6784 - auc: 0.9374 - prc: 0.7524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.2664e-04 - tp: 200.0000 - fp: 29.0000 - tn: 181948.0000 - fn: 99.0000 - accuracy: 0.9993 - precision: 0.8734 - recall: 0.6689 - auc: 0.9306 - prc: 0.7426 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 5.1824e-04 - val_tp: 58.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9355 - val_recall: 0.7073 - val_auc: 0.9388 - val_prc: 0.8435\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0124 - cross entropy: 0.0124 - Brier score: 0.0023 - tp: 0.0000e+00 - fp: 1.0000 - tn: 2043.0000 - fn: 4.0000 - accuracy: 0.9976 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.7469 - prc: 0.0820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 8.0043e-04 - tp: 29.0000 - fp: 3.0000 - tn: 30660.0000 - fn: 28.0000 - accuracy: 0.9990 - precision: 0.9062 - recall: 0.5088 - auc: 0.9290 - prc: 0.7620     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 8.6103e-04 - tp: 54.0000 - fp: 9.0000 - tn: 59277.0000 - fn: 52.0000 - accuracy: 0.9990 - precision: 0.8571 - recall: 0.5094 - auc: 0.9141 - prc: 0.6803"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.9515e-04 - tp: 86.0000 - fp: 17.0000 - tn: 87895.0000 - fn: 66.0000 - accuracy: 0.9991 - precision: 0.8350 - recall: 0.5658 - auc: 0.9233 - prc: 0.6925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.1668e-04 - tp: 112.0000 - fp: 18.0000 - tn: 116527.0000 - fn: 79.0000 - accuracy: 0.9992 - precision: 0.8615 - recall: 0.5864 - auc: 0.9231 - prc: 0.7127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 7.0616e-04 - tp: 147.0000 - fp: 20.0000 - tn: 145144.0000 - fn: 97.0000 - accuracy: 0.9992 - precision: 0.8802 - recall: 0.6025 - auc: 0.9253 - prc: 0.7279"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.0556e-04 - tp: 175.0000 - fp: 27.0000 - tn: 173766.0000 - fn: 112.0000 - accuracy: 0.9992 - precision: 0.8663 - recall: 0.6098 - auc: 0.9259 - prc: 0.7187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.9012e-04 - tp: 185.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 114.0000 - accuracy: 0.9992 - precision: 0.8685 - recall: 0.6187 - auc: 0.9289 - prc: 0.7251 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7472e-04 - val_tp: 60.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 22.0000 - val_accuracy: 0.9994 - val_precision: 0.9231 - val_recall: 0.7317 - val_auc: 0.9449 - val_prc: 0.8510\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 3.3892e-04 - cross entropy: 3.3892e-04 - Brier score: 1.2111e-06 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2046.0000 - fn: 0.0000e+00 - accuracy: 1.0000 - precision: 1.0000 - recall: 1.0000 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 5.3309e-04 - tp: 28.0000 - fp: 5.0000 - tn: 30672.0000 - fn: 15.0000 - accuracy: 0.9993 - precision: 0.8485 - recall: 0.6512 - auc: 0.9411 - prc: 0.7004             "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0027 - cross entropy: 0.0027 - Brier score: 4.8183e-04 - tp: 54.0000 - fp: 9.0000 - tn: 59303.0000 - fn: 26.0000 - accuracy: 0.9994 - precision: 0.8571 - recall: 0.6750 - auc: 0.9556 - prc: 0.7404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.0824e-04 - tp: 94.0000 - fp: 12.0000 - tn: 87907.0000 - fn: 51.0000 - accuracy: 0.9993 - precision: 0.8868 - recall: 0.6483 - auc: 0.9407 - prc: 0.7475"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0032 - cross entropy: 0.0032 - Brier score: 5.5616e-04 - tp: 121.0000 - fp: 16.0000 - tn: 116538.0000 - fn: 61.0000 - accuracy: 0.9993 - precision: 0.8832 - recall: 0.6648 - auc: 0.9416 - prc: 0.7564"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.0698e-04 - tp: 155.0000 - fp: 20.0000 - tn: 145150.0000 - fn: 83.0000 - accuracy: 0.9993 - precision: 0.8857 - recall: 0.6513 - auc: 0.9299 - prc: 0.7360"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.1201e-04 - tp: 185.0000 - fp: 24.0000 - tn: 173772.0000 - fn: 99.0000 - accuracy: 0.9993 - precision: 0.8852 - recall: 0.6514 - auc: 0.9253 - prc: 0.7254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.0173e-04 - tp: 197.0000 - fp: 25.0000 - tn: 181952.0000 - fn: 102.0000 - accuracy: 0.9993 - precision: 0.8874 - recall: 0.6589 - auc: 0.9290 - prc: 0.7333 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.8578e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9510 - val_prc: 0.8570\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0056 - cross entropy: 0.0056 - Brier score: 7.0299e-04 - tp: 1.0000 - fp: 1.0000 - tn: 2045.0000 - fn: 1.0000 - accuracy: 0.9990 - precision: 0.5000 - recall: 0.5000 - auc: 0.7479 - prc: 0.5014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0043 - cross entropy: 0.0043 - Brier score: 7.5069e-04 - tp: 29.0000 - fp: 3.0000 - tn: 30665.0000 - fn: 23.0000 - accuracy: 0.9992 - precision: 0.9062 - recall: 0.5577 - auc: 0.9029 - prc: 0.6914"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30/90 [=========>....................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.6797e-04 - tp: 63.0000 - fp: 13.0000 - tn: 61324.0000 - fn: 40.0000 - accuracy: 0.9991 - precision: 0.8289 - recall: 0.6117 - auc: 0.9312 - prc: 0.7125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 7.1541e-04 - tp: 92.0000 - fp: 18.0000 - tn: 89945.0000 - fn: 57.0000 - accuracy: 0.9992 - precision: 0.8364 - recall: 0.6174 - auc: 0.9422 - prc: 0.7383"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "59/90 [==================>...........] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.8282e-04 - tp: 118.0000 - fp: 24.0000 - tn: 120617.0000 - fn: 73.0000 - accuracy: 0.9992 - precision: 0.8310 - recall: 0.6178 - auc: 0.9364 - prc: 0.7313"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "74/90 [=======================>......] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 7.0536e-04 - tp: 147.0000 - fp: 29.0000 - tn: 151280.0000 - fn: 96.0000 - accuracy: 0.9992 - precision: 0.8352 - recall: 0.6049 - auc: 0.9354 - prc: 0.7234"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "88/90 [============================>.] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.9422e-04 - tp: 187.0000 - fp: 34.0000 - tn: 179891.0000 - fn: 112.0000 - accuracy: 0.9992 - precision: 0.8462 - recall: 0.6254 - auc: 0.9374 - prc: 0.7344"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.9174e-04 - tp: 187.0000 - fp: 35.0000 - tn: 181942.0000 - fn: 112.0000 - accuracy: 0.9992 - precision: 0.8423 - recall: 0.6254 - auc: 0.9373 - prc: 0.7320 - val_loss: 0.0029 - val_cross entropy: 0.0029 - val_Brier score: 5.2550e-04 - val_tp: 58.0000 - val_fp: 3.0000 - val_tn: 45484.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9508 - val_recall: 0.7073 - val_auc: 0.9510 - val_prc: 0.8546\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0055 - cross entropy: 0.0055 - Brier score: 9.7451e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.8742 - prc: 0.7519"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 5.5895e-04 - tp: 29.0000 - fp: 2.0000 - tn: 30672.0000 - fn: 17.0000 - accuracy: 0.9994 - precision: 0.9355 - recall: 0.6304 - auc: 0.9667 - prc: 0.7535 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 5.9832e-04 - tp: 60.0000 - fp: 5.0000 - tn: 59292.0000 - fn: 35.0000 - accuracy: 0.9993 - precision: 0.9231 - recall: 0.6316 - auc: 0.9308 - prc: 0.7505"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0032 - cross entropy: 0.0032 - Brier score: 5.7001e-04 - tp: 92.0000 - fp: 9.0000 - tn: 89962.0000 - fn: 49.0000 - accuracy: 0.9994 - precision: 0.9109 - recall: 0.6525 - auc: 0.9390 - prc: 0.7660"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58/90 [==================>...........] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2735e-04 - tp: 116.0000 - fp: 13.0000 - tn: 118583.0000 - fn: 72.0000 - accuracy: 0.9993 - precision: 0.8992 - recall: 0.6170 - auc: 0.9354 - prc: 0.7397"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72/90 [=======================>......] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.5478e-04 - tp: 148.0000 - fp: 17.0000 - tn: 147199.0000 - fn: 92.0000 - accuracy: 0.9993 - precision: 0.8970 - recall: 0.6167 - auc: 0.9304 - prc: 0.7355"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5558e-04 - tp: 175.0000 - fp: 21.0000 - tn: 175821.0000 - fn: 111.0000 - accuracy: 0.9993 - precision: 0.8929 - recall: 0.6119 - auc: 0.9345 - prc: 0.7413"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.6040e-04 - tp: 183.0000 - fp: 21.0000 - tn: 181956.0000 - fn: 116.0000 - accuracy: 0.9992 - precision: 0.8971 - recall: 0.6120 - auc: 0.9356 - prc: 0.7430 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.9123e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9510 - val_prc: 0.8581\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 33/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0017 - cross entropy: 0.0017 - Brier score: 4.6523e-04 - tp: 3.0000 - fp: 1.0000 - tn: 2044.0000 - fn: 0.0000e+00 - accuracy: 0.9995 - precision: 0.7500 - recall: 1.0000 - auc: 0.9998 - prc: 0.8290"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.3907e-04 - tp: 31.0000 - fp: 2.0000 - tn: 30669.0000 - fn: 18.0000 - accuracy: 0.9993 - precision: 0.9394 - recall: 0.6327 - auc: 0.9277 - prc: 0.7586 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2001e-04 - tp: 56.0000 - fp: 7.0000 - tn: 59296.0000 - fn: 33.0000 - accuracy: 0.9993 - precision: 0.8889 - recall: 0.6292 - auc: 0.9260 - prc: 0.7370"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 5.8696e-04 - tp: 94.0000 - fp: 11.0000 - tn: 87913.0000 - fn: 46.0000 - accuracy: 0.9994 - precision: 0.8952 - recall: 0.6714 - auc: 0.9349 - prc: 0.7687"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 5.9158e-04 - tp: 128.0000 - fp: 13.0000 - tn: 116532.0000 - fn: 63.0000 - accuracy: 0.9993 - precision: 0.9078 - recall: 0.6702 - auc: 0.9416 - prc: 0.7724"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.1240e-04 - tp: 160.0000 - fp: 22.0000 - tn: 145150.0000 - fn: 76.0000 - accuracy: 0.9993 - precision: 0.8791 - recall: 0.6780 - auc: 0.9419 - prc: 0.7562"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.2799e-04 - tp: 191.0000 - fp: 27.0000 - tn: 173767.0000 - fn: 95.0000 - accuracy: 0.9993 - precision: 0.8761 - recall: 0.6678 - auc: 0.9379 - prc: 0.7534"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.3240e-04 - tp: 198.0000 - fp: 27.0000 - tn: 181950.0000 - fn: 101.0000 - accuracy: 0.9993 - precision: 0.8800 - recall: 0.6622 - auc: 0.9339 - prc: 0.7473 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 5.0220e-04 - val_tp: 58.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9206 - val_recall: 0.7073 - val_auc: 0.9510 - val_prc: 0.8544\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 34/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 8.7839e-04 - cross entropy: 8.7839e-04 - Brier score: 1.4166e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2045.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.6667 - auc: 1.0000 - prc: 1.0000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0041 - cross entropy: 0.0041 - Brier score: 8.4902e-04 - tp: 32.0000 - fp: 5.0000 - tn: 30657.0000 - fn: 26.0000 - accuracy: 0.9990 - precision: 0.8649 - recall: 0.5517 - auc: 0.9472 - prc: 0.7343         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.7723e-04 - tp: 59.0000 - fp: 9.0000 - tn: 59286.0000 - fn: 38.0000 - accuracy: 0.9992 - precision: 0.8676 - recall: 0.6082 - auc: 0.9423 - prc: 0.7322"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.4390e-04 - tp: 86.0000 - fp: 12.0000 - tn: 87913.0000 - fn: 53.0000 - accuracy: 0.9993 - precision: 0.8776 - recall: 0.6187 - auc: 0.9379 - prc: 0.7282"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.1981e-04 - tp: 123.0000 - fp: 18.0000 - tn: 116529.0000 - fn: 66.0000 - accuracy: 0.9993 - precision: 0.8723 - recall: 0.6508 - auc: 0.9436 - prc: 0.7589"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2662e-04 - tp: 154.0000 - fp: 21.0000 - tn: 145149.0000 - fn: 84.0000 - accuracy: 0.9993 - precision: 0.8800 - recall: 0.6471 - auc: 0.9362 - prc: 0.7539"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2642e-04 - tp: 184.0000 - fp: 27.0000 - tn: 173768.0000 - fn: 101.0000 - accuracy: 0.9993 - precision: 0.8720 - recall: 0.6456 - auc: 0.9378 - prc: 0.7560"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.3294e-04 - tp: 193.0000 - fp: 29.0000 - tn: 181948.0000 - fn: 106.0000 - accuracy: 0.9993 - precision: 0.8694 - recall: 0.6455 - auc: 0.9373 - prc: 0.7536 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.8504e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9449 - val_prc: 0.8489\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0017 - cross entropy: 0.0017 - Brier score: 4.3421e-04 - tp: 3.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 1.0000 - accuracy: 0.9995 - precision: 1.0000 - recall: 0.7500 - auc: 0.9999 - prc: 0.9442"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0040 - cross entropy: 0.0040 - Brier score: 7.2440e-04 - tp: 33.0000 - fp: 8.0000 - tn: 30662.0000 - fn: 17.0000 - accuracy: 0.9992 - precision: 0.8049 - recall: 0.6600 - auc: 0.9191 - prc: 0.7064 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.8486e-04 - tp: 61.0000 - fp: 12.0000 - tn: 59284.0000 - fn: 35.0000 - accuracy: 0.9992 - precision: 0.8356 - recall: 0.6354 - auc: 0.9210 - prc: 0.7325"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.3790e-04 - tp: 91.0000 - fp: 17.0000 - tn: 87907.0000 - fn: 49.0000 - accuracy: 0.9993 - precision: 0.8426 - recall: 0.6500 - auc: 0.9350 - prc: 0.7649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.7144e-04 - tp: 115.0000 - fp: 21.0000 - tn: 116531.0000 - fn: 69.0000 - accuracy: 0.9992 - precision: 0.8456 - recall: 0.6250 - auc: 0.9340 - prc: 0.7449"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 7.0437e-04 - tp: 148.0000 - fp: 27.0000 - tn: 145143.0000 - fn: 90.0000 - accuracy: 0.9992 - precision: 0.8457 - recall: 0.6218 - auc: 0.9319 - prc: 0.7394"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.8441e-04 - tp: 179.0000 - fp: 29.0000 - tn: 173765.0000 - fn: 107.0000 - accuracy: 0.9992 - precision: 0.8606 - recall: 0.6259 - auc: 0.9275 - prc: 0.7451"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.8906e-04 - tp: 184.0000 - fp: 29.0000 - tn: 181948.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8638 - recall: 0.6154 - auc: 0.9239 - prc: 0.7403 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.9829e-04 - val_tp: 58.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9206 - val_recall: 0.7073 - val_auc: 0.9449 - val_prc: 0.8482\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 36/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0019 - cross entropy: 0.0019 - Brier score: 6.0284e-04 - tp: 1.0000 - fp: 0.0000e+00 - tn: 2045.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.3333 - auc: 0.9997 - prc: 0.7690"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0024 - cross entropy: 0.0024 - Brier score: 4.8674e-04 - tp: 44.0000 - fp: 2.0000 - tn: 30659.0000 - fn: 15.0000 - accuracy: 0.9994 - precision: 0.9565 - recall: 0.7458 - auc: 0.9657 - prc: 0.8646 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0027 - cross entropy: 0.0027 - Brier score: 4.8568e-04 - tp: 70.0000 - fp: 5.0000 - tn: 59291.0000 - fn: 26.0000 - accuracy: 0.9995 - precision: 0.9333 - recall: 0.7292 - auc: 0.9473 - prc: 0.8046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42/90 [=============>................] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 5.8224e-04 - tp: 96.0000 - fp: 10.0000 - tn: 85864.0000 - fn: 46.0000 - accuracy: 0.9993 - precision: 0.9057 - recall: 0.6761 - auc: 0.9430 - prc: 0.7794"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56/90 [=================>............] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.1970e-04 - tp: 122.0000 - fp: 22.0000 - tn: 114484.0000 - fn: 60.0000 - accuracy: 0.9993 - precision: 0.8472 - recall: 0.6703 - auc: 0.9388 - prc: 0.7334"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "70/90 [======================>.......] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.2430e-04 - tp: 151.0000 - fp: 26.0000 - tn: 143107.0000 - fn: 76.0000 - accuracy: 0.9993 - precision: 0.8531 - recall: 0.6652 - auc: 0.9376 - prc: 0.7301"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "84/90 [===========================>..] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.7023e-04 - tp: 184.0000 - fp: 29.0000 - tn: 171718.0000 - fn: 101.0000 - accuracy: 0.9992 - precision: 0.8638 - recall: 0.6456 - auc: 0.9343 - prc: 0.7263"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.5897e-04 - tp: 193.0000 - fp: 30.0000 - tn: 181947.0000 - fn: 106.0000 - accuracy: 0.9993 - precision: 0.8655 - recall: 0.6455 - auc: 0.9340 - prc: 0.7307 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.9601e-04 - val_tp: 58.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 24.0000 - val_accuracy: 0.9994 - val_precision: 0.9206 - val_recall: 0.7073 - val_auc: 0.9449 - val_prc: 0.8474\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 37/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0066 - cross entropy: 0.0066 - Brier score: 0.0017 - tp: 0.0000e+00 - fp: 1.0000 - tn: 2044.0000 - fn: 3.0000 - accuracy: 0.9980 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9979 - prc: 0.3654"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.8587e-04 - tp: 38.0000 - fp: 8.0000 - tn: 30657.0000 - fn: 17.0000 - accuracy: 0.9992 - precision: 0.8261 - recall: 0.6909 - auc: 0.9812 - prc: 0.7782     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 6.0456e-04 - tp: 75.0000 - fp: 9.0000 - tn: 59277.0000 - fn: 31.0000 - accuracy: 0.9993 - precision: 0.8929 - recall: 0.7075 - auc: 0.9665 - prc: 0.8105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.3891e-04 - tp: 100.0000 - fp: 13.0000 - tn: 87900.0000 - fn: 51.0000 - accuracy: 0.9993 - precision: 0.8850 - recall: 0.6623 - auc: 0.9529 - prc: 0.7640"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.7201e-04 - tp: 126.0000 - fp: 21.0000 - tn: 116521.0000 - fn: 68.0000 - accuracy: 0.9992 - precision: 0.8571 - recall: 0.6495 - auc: 0.9399 - prc: 0.7329"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.3147e-04 - tp: 151.0000 - fp: 24.0000 - tn: 145150.0000 - fn: 83.0000 - accuracy: 0.9993 - precision: 0.8629 - recall: 0.6453 - auc: 0.9437 - prc: 0.7463"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 6.9497e-04 - tp: 172.0000 - fp: 31.0000 - tn: 173770.0000 - fn: 107.0000 - accuracy: 0.9992 - precision: 0.8473 - recall: 0.6165 - auc: 0.9364 - prc: 0.7043"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 7.0205e-04 - tp: 184.0000 - fp: 31.0000 - tn: 181946.0000 - fn: 115.0000 - accuracy: 0.9992 - precision: 0.8558 - recall: 0.6154 - auc: 0.9373 - prc: 0.7124 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.9088e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9449 - val_prc: 0.8476\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 38/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0017 - cross entropy: 0.0017 - Brier score: 4.4232e-04 - tp: 3.0000 - fp: 1.0000 - tn: 2044.0000 - fn: 0.0000e+00 - accuracy: 0.9995 - precision: 0.7500 - recall: 1.0000 - auc: 0.9997 - prc: 0.7690"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0026 - cross entropy: 0.0026 - Brier score: 5.5234e-04 - tp: 31.0000 - fp: 4.0000 - tn: 30670.0000 - fn: 15.0000 - accuracy: 0.9994 - precision: 0.8857 - recall: 0.6739 - auc: 0.9669 - prc: 0.8286 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 6.0179e-04 - tp: 61.0000 - fp: 7.0000 - tn: 59291.0000 - fn: 33.0000 - accuracy: 0.9993 - precision: 0.8971 - recall: 0.6489 - auc: 0.9461 - prc: 0.7908"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.1026e-04 - tp: 96.0000 - fp: 10.0000 - tn: 87909.0000 - fn: 49.0000 - accuracy: 0.9993 - precision: 0.9057 - recall: 0.6621 - auc: 0.9372 - prc: 0.7737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.3601e-04 - tp: 116.0000 - fp: 17.0000 - tn: 116536.0000 - fn: 67.0000 - accuracy: 0.9993 - precision: 0.8722 - recall: 0.6339 - auc: 0.9337 - prc: 0.7410"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.6217e-04 - tp: 154.0000 - fp: 22.0000 - tn: 145146.0000 - fn: 86.0000 - accuracy: 0.9993 - precision: 0.8750 - recall: 0.6417 - auc: 0.9326 - prc: 0.7491"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.4137e-04 - tp: 188.0000 - fp: 26.0000 - tn: 173767.0000 - fn: 99.0000 - accuracy: 0.9993 - precision: 0.8785 - recall: 0.6551 - auc: 0.9418 - prc: 0.7706"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.4066e-04 - tp: 195.0000 - fp: 26.0000 - tn: 181951.0000 - fn: 104.0000 - accuracy: 0.9993 - precision: 0.8824 - recall: 0.6522 - auc: 0.9374 - prc: 0.7656 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.8218e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9449 - val_prc: 0.8483\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 39/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0047 - cross entropy: 0.0047 - Brier score: 8.3242e-04 - tp: 0.0000e+00 - fp: 1.0000 - tn: 2045.0000 - fn: 2.0000 - accuracy: 0.9985 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.7474 - prc: 0.1548"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0044 - cross entropy: 0.0044 - Brier score: 8.2011e-04 - tp: 29.0000 - fp: 8.0000 - tn: 30662.0000 - fn: 21.0000 - accuracy: 0.9991 - precision: 0.7838 - recall: 0.5800 - auc: 0.8989 - prc: 0.6583         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.7158e-04 - tp: 60.0000 - fp: 10.0000 - tn: 59284.0000 - fn: 38.0000 - accuracy: 0.9992 - precision: 0.8571 - recall: 0.6122 - auc: 0.9227 - prc: 0.7478"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0036 - cross entropy: 0.0036 - Brier score: 6.5057e-04 - tp: 93.0000 - fp: 12.0000 - tn: 87905.0000 - fn: 54.0000 - accuracy: 0.9993 - precision: 0.8857 - recall: 0.6327 - auc: 0.9210 - prc: 0.7505"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2552e-04 - tp: 128.0000 - fp: 18.0000 - tn: 116524.0000 - fn: 66.0000 - accuracy: 0.9993 - precision: 0.8767 - recall: 0.6598 - auc: 0.9297 - prc: 0.7700"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2850e-04 - tp: 157.0000 - fp: 21.0000 - tn: 145146.0000 - fn: 84.0000 - accuracy: 0.9993 - precision: 0.8820 - recall: 0.6515 - auc: 0.9308 - prc: 0.7686"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "85/90 [===========================>..] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.1635e-04 - tp: 185.0000 - fp: 25.0000 - tn: 173771.0000 - fn: 99.0000 - accuracy: 0.9993 - precision: 0.8810 - recall: 0.6514 - auc: 0.9306 - prc: 0.7704"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.2081e-04 - tp: 195.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 104.0000 - accuracy: 0.9993 - precision: 0.8744 - recall: 0.6522 - auc: 0.9274 - prc: 0.7673 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7334e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9449 - val_prc: 0.8511\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 40/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0050 - cross entropy: 0.0050 - Brier score: 9.5472e-04 - tp: 2.0000 - fp: 0.0000e+00 - tn: 2044.0000 - fn: 2.0000 - accuracy: 0.9990 - precision: 1.0000 - recall: 0.5000 - auc: 0.8741 - prc: 0.6800"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.2344e-04 - tp: 30.0000 - fp: 2.0000 - tn: 30669.0000 - fn: 19.0000 - accuracy: 0.9993 - precision: 0.9375 - recall: 0.6122 - auc: 0.9175 - prc: 0.7209 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0038 - cross entropy: 0.0038 - Brier score: 7.0402e-04 - tp: 68.0000 - fp: 9.0000 - tn: 59278.0000 - fn: 37.0000 - accuracy: 0.9992 - precision: 0.8831 - recall: 0.6476 - auc: 0.9278 - prc: 0.7425"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0034 - cross entropy: 0.0034 - Brier score: 6.0811e-04 - tp: 91.0000 - fp: 10.0000 - tn: 89961.0000 - fn: 50.0000 - accuracy: 0.9993 - precision: 0.9010 - recall: 0.6454 - auc: 0.9212 - prc: 0.7444"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "58/90 [==================>...........] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 5.8631e-04 - tp: 123.0000 - fp: 16.0000 - tn: 118585.0000 - fn: 60.0000 - accuracy: 0.9994 - precision: 0.8849 - recall: 0.6721 - auc: 0.9310 - prc: 0.7520"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "72/90 [=======================>......] - ETA: 0s - loss: 0.0031 - cross entropy: 0.0031 - Brier score: 5.6709e-04 - tp: 157.0000 - fp: 19.0000 - tn: 147206.0000 - fn: 74.0000 - accuracy: 0.9994 - precision: 0.8920 - recall: 0.6797 - auc: 0.9366 - prc: 0.7731"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.1056e-04 - tp: 193.0000 - fp: 26.0000 - tn: 175813.0000 - fn: 96.0000 - accuracy: 0.9993 - precision: 0.8813 - recall: 0.6678 - auc: 0.9353 - prc: 0.7714"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.0397e-04 - tp: 202.0000 - fp: 28.0000 - tn: 181949.0000 - fn: 97.0000 - accuracy: 0.9993 - precision: 0.8783 - recall: 0.6756 - auc: 0.9358 - prc: 0.7739 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7153e-04 - val_tp: 62.0000 - val_fp: 4.0000 - val_tn: 45483.0000 - val_fn: 20.0000 - val_accuracy: 0.9995 - val_precision: 0.9394 - val_recall: 0.7561 - val_auc: 0.9449 - val_prc: 0.8499\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0067 - cross entropy: 0.0067 - Brier score: 0.0012 - tp: 3.0000 - fp: 0.0000e+00 - tn: 2042.0000 - fn: 3.0000 - accuracy: 0.9985 - precision: 1.0000 - recall: 0.5000 - auc: 0.9161 - prc: 0.8196"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5989e-04 - tp: 33.0000 - fp: 5.0000 - tn: 30662.0000 - fn: 20.0000 - accuracy: 0.9992 - precision: 0.8684 - recall: 0.6226 - auc: 0.9333 - prc: 0.7705"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "30/90 [=========>....................] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5161e-04 - tp: 53.0000 - fp: 10.0000 - tn: 61338.0000 - fn: 39.0000 - accuracy: 0.9992 - precision: 0.8413 - recall: 0.5761 - auc: 0.9287 - prc: 0.7328"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "44/90 [=============>................] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.8708e-04 - tp: 80.0000 - fp: 14.0000 - tn: 89959.0000 - fn: 59.0000 - accuracy: 0.9992 - precision: 0.8511 - recall: 0.5755 - auc: 0.9201 - prc: 0.7168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "59/90 [==================>...........] - ETA: 0s - loss: 0.0037 - cross entropy: 0.0037 - Brier score: 6.7696e-04 - tp: 118.0000 - fp: 16.0000 - tn: 120619.0000 - fn: 79.0000 - accuracy: 0.9992 - precision: 0.8806 - recall: 0.5990 - auc: 0.9231 - prc: 0.7332"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73/90 [=======================>......] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.5535e-04 - tp: 143.0000 - fp: 19.0000 - tn: 149248.0000 - fn: 94.0000 - accuracy: 0.9992 - precision: 0.8827 - recall: 0.6034 - auc: 0.9339 - prc: 0.7467"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "88/90 [============================>.] - ETA: 0s - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.6530e-04 - tp: 186.0000 - fp: 24.0000 - tn: 179902.0000 - fn: 112.0000 - accuracy: 0.9992 - precision: 0.8857 - recall: 0.6242 - auc: 0.9406 - prc: 0.7589"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0035 - cross entropy: 0.0035 - Brier score: 6.6866e-04 - tp: 186.0000 - fp: 25.0000 - tn: 181952.0000 - fn: 113.0000 - accuracy: 0.9992 - precision: 0.8815 - recall: 0.6221 - auc: 0.9407 - prc: 0.7539 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.6169e-04 - val_tp: 66.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.9296 - val_recall: 0.8049 - val_auc: 0.9510 - val_prc: 0.8571\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0023 - cross entropy: 0.0023 - Brier score: 6.8943e-04 - tp: 7.0000 - fp: 1.0000 - tn: 2038.0000 - fn: 2.0000 - accuracy: 0.9985 - precision: 0.8750 - recall: 0.7778 - auc: 0.9999 - prc: 0.9752"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.0030 - cross entropy: 0.0030 - Brier score: 5.2772e-04 - tp: 38.0000 - fp: 3.0000 - tn: 30662.0000 - fn: 17.0000 - accuracy: 0.9993 - precision: 0.9268 - recall: 0.6909 - auc: 0.9631 - prc: 0.8487"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "29/90 [========>.....................] - ETA: 0s - loss: 0.0039 - cross entropy: 0.0039 - Brier score: 7.3727e-04 - tp: 69.0000 - fp: 7.0000 - tn: 59272.0000 - fn: 44.0000 - accuracy: 0.9991 - precision: 0.9079 - recall: 0.6106 - auc: 0.9373 - prc: 0.7744"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "43/90 [=============>................] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.1992e-04 - tp: 102.0000 - fp: 13.0000 - tn: 87898.0000 - fn: 51.0000 - accuracy: 0.9993 - precision: 0.8870 - recall: 0.6667 - auc: 0.9470 - prc: 0.7892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "57/90 [==================>...........] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.2821e-04 - tp: 133.0000 - fp: 20.0000 - tn: 116515.0000 - fn: 68.0000 - accuracy: 0.9992 - precision: 0.8693 - recall: 0.6617 - auc: 0.9545 - prc: 0.7823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "71/90 [======================>.......] - ETA: 0s - loss: 0.0032 - cross entropy: 0.0032 - Brier score: 6.1273e-04 - tp: 160.0000 - fp: 24.0000 - tn: 145143.0000 - fn: 81.0000 - accuracy: 0.9993 - precision: 0.8696 - recall: 0.6639 - auc: 0.9475 - prc: 0.7778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "86/90 [===========================>..] - ETA: 0s - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.4029e-04 - tp: 188.0000 - fp: 30.0000 - tn: 175806.0000 - fn: 104.0000 - accuracy: 0.9992 - precision: 0.8624 - recall: 0.6438 - auc: 0.9445 - prc: 0.7663"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restoring model weights from the end of the best epoch: 32.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.0033 - cross entropy: 0.0033 - Brier score: 6.3642e-04 - tp: 193.0000 - fp: 32.0000 - tn: 181945.0000 - fn: 106.0000 - accuracy: 0.9992 - precision: 0.8578 - recall: 0.6455 - auc: 0.9441 - prc: 0.7655 - val_loss: 0.0028 - val_cross entropy: 0.0028 - val_Brier score: 4.7751e-04 - val_tp: 59.0000 - val_fp: 5.0000 - val_tn: 45482.0000 - val_fn: 23.0000 - val_accuracy: 0.9994 - val_precision: 0.9219 - val_recall: 0.7195 - val_auc: 0.9510 - val_prc: 0.8563\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42: early stopping\n"
     ]
    }
   ],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "baseline_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=(val_features, val_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iSaDBYU9xtP6"
   },
   "source": [
    "### Check training history\n",
    "\n",
    "In this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in the [Overfit and underfit](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit) tutorial.\n",
    "\n",
    "Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:28.646164Z",
     "iopub.status.busy": "2024-01-17T02:21:28.645922Z",
     "iopub.status.idle": "2024-01-17T02:21:28.651712Z",
     "shell.execute_reply": "2024-01-17T02:21:28.651114Z"
    },
    "id": "WTSkhT1jyGu6"
   },
   "outputs": [],
   "source": [
    "def plot_metrics(history):\n",
    "  metrics = ['loss', 'prc', 'precision', 'recall']\n",
    "  for n, metric in enumerate(metrics):\n",
    "    name = metric.replace(\"_\",\" \").capitalize()\n",
    "    plt.subplot(2,2,n+1)\n",
    "    plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')\n",
    "    plt.plot(history.epoch, history.history['val_'+metric],\n",
    "             color=colors[0], linestyle=\"--\", label='Val')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel(name)\n",
    "    if metric == 'loss':\n",
    "      plt.ylim([0, plt.ylim()[1]])\n",
    "    elif metric == 'auc':\n",
    "      plt.ylim([0.8,1])\n",
    "    else:\n",
    "      plt.ylim([0,1])\n",
    "\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:28.654651Z",
     "iopub.status.busy": "2024-01-17T02:21:28.654399Z",
     "iopub.status.idle": "2024-01-17T02:21:29.175488Z",
     "shell.execute_reply": "2024-01-17T02:21:29.174853Z"
    },
    "id": "u6LReDsqlZlk"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UCa4iWo6WDKR"
   },
   "source": [
    "Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aJC1booryouo"
   },
   "source": [
    "### Evaluate metrics\n",
    "\n",
    "You can use a [confusion matrix](https://developers.google.com/machine-learning/glossary/#confusion_matrix) to summarize the actual vs. predicted labels, where the X axis is the predicted label and the Y axis is the actual label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:29.180130Z",
     "iopub.status.busy": "2024-01-17T02:21:29.179755Z",
     "iopub.status.idle": "2024-01-17T02:21:29.678420Z",
     "shell.execute_reply": "2024-01-17T02:21:29.677735Z"
    },
    "id": "aNS796IJKrev"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 6s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "36/90 [===========>..................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "74/90 [=======================>......] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/28 [>.............................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/28 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:29.682279Z",
     "iopub.status.busy": "2024-01-17T02:21:29.681638Z",
     "iopub.status.idle": "2024-01-17T02:21:29.686923Z",
     "shell.execute_reply": "2024-01-17T02:21:29.686353Z"
    },
    "id": "MVWBGfADwbWI"
   },
   "outputs": [],
   "source": [
    "def plot_cm(labels, predictions, threshold=0.5):\n",
    "  cm = confusion_matrix(labels, predictions > threshold)\n",
    "  plt.figure(figsize=(5,5))\n",
    "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "  plt.title('Confusion matrix @{:.2f}'.format(threshold))\n",
    "  plt.ylabel('Actual label')\n",
    "  plt.xlabel('Predicted label')\n",
    "\n",
    "  print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n",
    "  print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n",
    "  print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n",
    "  print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n",
    "  print('Total Fraudulent Transactions: ', np.sum(cm[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nOTjD5Z5Wp1U"
   },
   "source": [
    "Evaluate your model on the test dataset and display the results for the metrics you created above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:29.690490Z",
     "iopub.status.busy": "2024-01-17T02:21:29.689895Z",
     "iopub.status.idle": "2024-01-17T02:21:30.043781Z",
     "shell.execute_reply": "2024-01-17T02:21:30.043132Z"
    },
    "id": "poh_hZngt2_9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.0038855739403516054\n",
      "cross entropy :  0.0038855739403516054\n",
      "Brier score :  0.0006162827485240996\n",
      "tp :  81.0\n",
      "fp :  11.0\n",
      "tn :  56840.0\n",
      "fn :  30.0\n",
      "accuracy :  0.9992802143096924\n",
      "precision :  0.8804348111152649\n",
      "recall :  0.7297297120094299\n",
      "auc :  0.9096326231956482\n",
      "prc :  0.7863917350769043\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  56840\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  11\n",
      "Fraudulent Transactions Missed (False Negatives):  30\n",
      "Fraudulent Transactions Detected (True Positives):  81\n",
      "Total Fraudulent Transactions:  111\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "baseline_results = model.evaluate(test_features, test_labels,\n",
    "                                  batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PyZtSr1v6L4t"
   },
   "source": [
    "If the model had predicted everything perfectly (impossible with true randomness), this would be a [diagonal matrix](https://en.wikipedia.org/wiki/Diagonal_matrix) where values off the main diagonal, indicating incorrect predictions, would be zero. In this case, the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P-QpQsip_F2Q"
   },
   "source": [
    "### Changing the threshold\n",
    "\n",
    "The default threshold of $t=50\\%$ corresponds to equal costs of false negatives and false positives.\n",
    "In the case of fraud detection, however, you would likely associate higher costs to false negatives than to false positives.\n",
    "This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity.\n",
    "\n",
    "By decreasing the threshold, we attribute higher cost to false negatives, thereby increasing missed transactions at the price of more false positives.\n",
    "We test thresholds at 10% and at 1%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.047595Z",
     "iopub.status.busy": "2024-01-17T02:21:30.047315Z",
     "iopub.status.idle": "2024-01-17T02:21:30.358583Z",
     "shell.execute_reply": "2024-01-17T02:21:30.357916Z"
    },
    "id": "52bd793e04bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Legitimate Transactions Detected (True Negatives):  56834\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  17\n",
      "Fraudulent Transactions Missed (False Negatives):  23\n",
      "Fraudulent Transactions Detected (True Positives):  88\n",
      "Total Fraudulent Transactions:  111\n",
      "Legitimate Transactions Detected (True Negatives):  56806\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  45\n",
      "Fraudulent Transactions Missed (False Negatives):  22\n",
      "Fraudulent Transactions Detected (True Positives):  89\n",
      "Total Fraudulent Transactions:  111\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cm(test_labels, test_predictions_baseline, threshold=0.1)\n",
    "plot_cm(test_labels, test_predictions_baseline, threshold=0.01)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "P-QpQsip_F2Q"
   },
   "source": [
    "### Plot the ROC\n",
    "\n",
    "Now plot the [ROC](https://developers.google.com/machine-learning/glossary#ROC). This plot is useful because it shows, at a glance, the range of performance the model can reach by tuning the output threshold over its full range (0 to 1). So each point corresponds to a single value of the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.362681Z",
     "iopub.status.busy": "2024-01-17T02:21:30.361927Z",
     "iopub.status.idle": "2024-01-17T02:21:30.366678Z",
     "shell.execute_reply": "2024-01-17T02:21:30.366099Z"
    },
    "id": "lhaxsLSvANF9"
   },
   "outputs": [],
   "source": [
    "def plot_roc(name, labels, predictions, **kwargs):\n",
    "  fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
    "\n",
    "  plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)\n",
    "  plt.xlabel('False positives [%]')\n",
    "  plt.ylabel('True positives [%]')\n",
    "  plt.xlim([-0.5,20])\n",
    "  plt.ylim([80,100.5])\n",
    "  plt.grid(True)\n",
    "  ax = plt.gca()\n",
    "  ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.370026Z",
     "iopub.status.busy": "2024-01-17T02:21:30.369426Z",
     "iopub.status.idle": "2024-01-17T02:21:30.612980Z",
     "shell.execute_reply": "2024-01-17T02:21:30.612290Z"
    },
    "id": "DfHHspttKJE0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y5twGRLfNwmO"
   },
   "source": [
    "### Plot the PRC\n",
    "\n",
    "Now plot the [AUPRC](https://developers.google.com/machine-learning/glossary?hl=en#PR_AUC). Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.617044Z",
     "iopub.status.busy": "2024-01-17T02:21:30.616465Z",
     "iopub.status.idle": "2024-01-17T02:21:30.620715Z",
     "shell.execute_reply": "2024-01-17T02:21:30.620088Z"
    },
    "id": "XV6JSlFGEqGI"
   },
   "outputs": [],
   "source": [
    "def plot_prc(name, labels, predictions, **kwargs):\n",
    "    precision, recall, _ = sklearn.metrics.precision_recall_curve(labels, predictions)\n",
    "\n",
    "    plt.plot(precision, recall, label=name, linewidth=2, **kwargs)\n",
    "    plt.xlabel('Precision')\n",
    "    plt.ylabel('Recall')\n",
    "    plt.grid(True)\n",
    "    ax = plt.gca()\n",
    "    ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.624078Z",
     "iopub.status.busy": "2024-01-17T02:21:30.623514Z",
     "iopub.status.idle": "2024-01-17T02:21:30.872495Z",
     "shell.execute_reply": "2024-01-17T02:21:30.871838Z"
    },
    "id": "FdQs_PcqEsiL"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gpdsFyp64DhY"
   },
   "source": [
    "It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cveQoiMyGQCo"
   },
   "source": [
    "## Class weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ePGp6GUE1WfH"
   },
   "source": [
    "### Calculate class weights\n",
    "\n",
    "The goal is to identify fraudulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class. Note, however, that this does not increase in any way the amount of information of your dataset. In the end, using class weights is more or less equivalent to changing the output bias or to changing the threshold. Let's see how it works out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.876872Z",
     "iopub.status.busy": "2024-01-17T02:21:30.876282Z",
     "iopub.status.idle": "2024-01-17T02:21:30.880845Z",
     "shell.execute_reply": "2024-01-17T02:21:30.880191Z"
    },
    "id": "qjGWErngGny7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight for class 0: 0.50\n",
      "Weight for class 1: 289.44\n"
     ]
    }
   ],
   "source": [
    "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
    "# The sum of the weights of all examples stays the same.\n",
    "weight_for_0 = (1 / neg) * (total / 2.0)\n",
    "weight_for_1 = (1 / pos) * (total / 2.0)\n",
    "\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
    "\n",
    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
    "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mk1OOE2ZSHzy"
   },
   "source": [
    "### Train a model with class weights\n",
    "\n",
    "Now try re-training and evaluating the model with class weights to see how that affects the predictions.\n",
    "\n",
    "Note: Using `class_weights` changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like `tf.keras.optimizers.SGD`, may fail. The optimizer used here, `tf.keras.optimizers.Adam`, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:30.884100Z",
     "iopub.status.busy": "2024-01-17T02:21:30.883722Z",
     "iopub.status.idle": "2024-01-17T02:21:39.459801Z",
     "shell.execute_reply": "2024-01-17T02:21:39.459150Z"
    },
    "id": "UJ589fn8ST3x"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 1:59 - loss: 1.0252 - cross entropy: 0.0042 - Brier score: 6.6709e-04 - tp: 82.0000 - fp: 14.0000 - tn: 58882.0000 - fn: 32.0000 - accuracy: 0.9992 - precision: 0.8542 - recall: 0.7193 - auc: 0.9107 - prc: 0.7667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.9940 - cross entropy: 0.0084 - Brier score: 0.0013 - tp: 98.0000 - fp: 76.0000 - tn: 85406.0000 - fn: 54.0000 - accuracy: 0.9985 - precision: 0.5632 - recall: 0.6447 - auc: 0.8905 - prc: 0.5830      "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/90 [========>.....................] - ETA: 0s - loss: 1.0722 - cross entropy: 0.0108 - Brier score: 0.0017 - tp: 113.0000 - fp: 133.0000 - tn: 113974.0000 - fn: 86.0000 - accuracy: 0.9981 - precision: 0.4593 - recall: 0.5678 - auc: 0.8806 - prc: 0.4900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42/90 [=============>................] - ETA: 0s - loss: 1.0613 - cross entropy: 0.0127 - Brier score: 0.0020 - tp: 131.0000 - fp: 214.0000 - tn: 142520.0000 - fn: 113.0000 - accuracy: 0.9977 - precision: 0.3797 - recall: 0.5369 - auc: 0.8741 - prc: 0.4288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "56/90 [=================>............] - ETA: 0s - loss: 1.0522 - cross entropy: 0.0139 - Brier score: 0.0023 - tp: 155.0000 - fp: 298.0000 - tn: 171063.0000 - fn: 134.0000 - accuracy: 0.9975 - precision: 0.3422 - recall: 0.5363 - auc: 0.8677 - prc: 0.4198"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "69/90 [======================>.......] - ETA: 0s - loss: 1.0039 - cross entropy: 0.0149 - Brier score: 0.0024 - tp: 188.0000 - fp: 369.0000 - tn: 197561.0000 - fn: 156.0000 - accuracy: 0.9974 - precision: 0.3375 - recall: 0.5465 - auc: 0.8797 - prc: 0.4339"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82/90 [==========================>...] - ETA: 0s - loss: 0.9389 - cross entropy: 0.0160 - Brier score: 0.0026 - tp: 221.0000 - fp: 475.0000 - tn: 224033.0000 - fn: 169.0000 - accuracy: 0.9971 - precision: 0.3175 - recall: 0.5667 - auc: 0.8832 - prc: 0.4355"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 2s 11ms/step - loss: 0.9262 - cross entropy: 0.0166 - Brier score: 0.0027 - tp: 233.0000 - fp: 530.0000 - tn: 238298.0000 - fn: 177.0000 - accuracy: 0.9970 - precision: 0.3054 - recall: 0.5683 - auc: 0.8803 - prc: 0.4222 - val_loss: 0.0116 - val_cross entropy: 0.0116 - val_Brier score: 0.0011 - val_tp: 67.0000 - val_fp: 35.0000 - val_tn: 45452.0000 - val_fn: 15.0000 - val_accuracy: 0.9989 - val_precision: 0.6569 - val_recall: 0.8171 - val_auc: 0.9519 - val_prc: 0.7255\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.5303 - cross entropy: 0.0199 - Brier score: 0.0035 - tp: 3.0000 - fp: 8.0000 - tn: 2036.0000 - fn: 1.0000 - accuracy: 0.9956 - precision: 0.2727 - recall: 0.7500 - auc: 0.9760 - prc: 0.7544"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.8011 - cross entropy: 0.0262 - Brier score: 0.0044 - tp: 39.0000 - fp: 113.0000 - tn: 28500.0000 - fn: 20.0000 - accuracy: 0.9954 - precision: 0.2566 - recall: 0.6610 - auc: 0.9234 - prc: 0.4593"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.6340 - cross entropy: 0.0268 - Brier score: 0.0045 - tp: 63.0000 - fp: 233.0000 - tn: 54969.0000 - fn: 31.0000 - accuracy: 0.9952 - precision: 0.2128 - recall: 0.6702 - auc: 0.9165 - prc: 0.4187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.7264 - cross entropy: 0.0287 - Brier score: 0.0050 - tp: 92.0000 - fp: 387.0000 - tn: 81391.0000 - fn: 50.0000 - accuracy: 0.9947 - precision: 0.1921 - recall: 0.6479 - auc: 0.8954 - prc: 0.4130"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.7214 - cross entropy: 0.0293 - Brier score: 0.0052 - tp: 121.0000 - fp: 556.0000 - tn: 107799.0000 - fn: 68.0000 - accuracy: 0.9943 - precision: 0.1787 - recall: 0.6402 - auc: 0.9000 - prc: 0.4210"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66/90 [=====================>........] - ETA: 0s - loss: 0.6745 - cross entropy: 0.0301 - Brier score: 0.0055 - tp: 159.0000 - fp: 747.0000 - tn: 134183.0000 - fn: 79.0000 - accuracy: 0.9939 - precision: 0.1755 - recall: 0.6681 - auc: 0.9052 - prc: 0.4546"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79/90 [=========================>....] - ETA: 0s - loss: 0.6444 - cross entropy: 0.0312 - Brier score: 0.0058 - tp: 186.0000 - fp: 952.0000 - tn: 160565.0000 - fn: 89.0000 - accuracy: 0.9936 - precision: 0.1634 - recall: 0.6764 - auc: 0.9031 - prc: 0.4457"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.6152 - cross entropy: 0.0319 - Brier score: 0.0059 - tp: 202.0000 - fp: 1117.0000 - tn: 180860.0000 - fn: 97.0000 - accuracy: 0.9933 - precision: 0.1531 - recall: 0.6756 - auc: 0.9051 - prc: 0.4410 - val_loss: 0.0172 - val_cross entropy: 0.0172 - val_Brier score: 0.0019 - val_tp: 69.0000 - val_fp: 72.0000 - val_tn: 45415.0000 - val_fn: 13.0000 - val_accuracy: 0.9981 - val_precision: 0.4894 - val_recall: 0.8415 - val_auc: 0.9577 - val_prc: 0.7220\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0217 - cross entropy: 0.0386 - Brier score: 0.0077 - tp: 4.0000 - fp: 16.0000 - tn: 2028.0000 - fn: 0.0000e+00 - accuracy: 0.9922 - precision: 0.2000 - recall: 1.0000 - auc: 0.9994 - prc: 0.6088"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.2510 - cross entropy: 0.0384 - Brier score: 0.0076 - tp: 38.0000 - fp: 259.0000 - tn: 30414.0000 - fn: 9.0000 - accuracy: 0.9913 - precision: 0.1279 - recall: 0.8085 - auc: 0.9722 - prc: 0.4797 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/90 [========>.....................] - ETA: 0s - loss: 0.2889 - cross entropy: 0.0402 - Brier score: 0.0078 - tp: 66.0000 - fp: 489.0000 - tn: 56770.0000 - fn: 19.0000 - accuracy: 0.9911 - precision: 0.1189 - recall: 0.7765 - auc: 0.9632 - prc: 0.4072"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "42/90 [=============>................] - ETA: 0s - loss: 0.4322 - cross entropy: 0.0421 - Brier score: 0.0083 - tp: 111.0000 - fp: 785.0000 - tn: 85085.0000 - fn: 35.0000 - accuracy: 0.9905 - precision: 0.1239 - recall: 0.7603 - auc: 0.9364 - prc: 0.4071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "55/90 [=================>............] - ETA: 0s - loss: 0.4096 - cross entropy: 0.0428 - Brier score: 0.0086 - tp: 144.0000 - fp: 1083.0000 - tn: 111370.0000 - fn: 43.0000 - accuracy: 0.9900 - precision: 0.1174 - recall: 0.7701 - auc: 0.9398 - prc: 0.4145"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "69/90 [======================>.......] - ETA: 0s - loss: 0.4212 - cross entropy: 0.0444 - Brier score: 0.0090 - tp: 185.0000 - fp: 1428.0000 - tn: 139645.0000 - fn: 54.0000 - accuracy: 0.9895 - precision: 0.1147 - recall: 0.7741 - auc: 0.9384 - prc: 0.4218"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "82/90 [==========================>...] - ETA: 0s - loss: 0.4110 - cross entropy: 0.0453 - Brier score: 0.0093 - tp: 214.0000 - fp: 1756.0000 - tn: 165905.0000 - fn: 61.0000 - accuracy: 0.9892 - precision: 0.1086 - recall: 0.7782 - auc: 0.9360 - prc: 0.4193"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.4397 - cross entropy: 0.0461 - Brier score: 0.0095 - tp: 229.0000 - fp: 1929.0000 - tn: 180048.0000 - fn: 70.0000 - accuracy: 0.9890 - precision: 0.1061 - recall: 0.7659 - auc: 0.9307 - prc: 0.4134 - val_loss: 0.0236 - val_cross entropy: 0.0236 - val_Brier score: 0.0029 - val_tp: 69.0000 - val_fp: 106.0000 - val_tn: 45381.0000 - val_fn: 13.0000 - val_accuracy: 0.9974 - val_precision: 0.3943 - val_recall: 0.8415 - val_auc: 0.9662 - val_prc: 0.7291\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.7905 - cross entropy: 0.0650 - Brier score: 0.0155 - tp: 2.0000 - fp: 39.0000 - tn: 2006.0000 - fn: 1.0000 - accuracy: 0.9805 - precision: 0.0488 - recall: 0.6667 - auc: 0.7368 - prc: 0.3339"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.3572 - cross entropy: 0.0557 - Brier score: 0.0121 - tp: 40.0000 - fp: 397.0000 - tn: 28225.0000 - fn: 10.0000 - accuracy: 0.9858 - precision: 0.0915 - recall: 0.8000 - auc: 0.9503 - prc: 0.3879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.2894 - cross entropy: 0.0547 - Brier score: 0.0116 - tp: 59.0000 - fp: 729.0000 - tn: 54492.0000 - fn: 16.0000 - accuracy: 0.9865 - precision: 0.0749 - recall: 0.7867 - auc: 0.9480 - prc: 0.3778"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.3045 - cross entropy: 0.0555 - Brier score: 0.0120 - tp: 92.0000 - fp: 1123.0000 - tn: 80681.0000 - fn: 24.0000 - accuracy: 0.9860 - precision: 0.0757 - recall: 0.7931 - auc: 0.9458 - prc: 0.3813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.3684 - cross entropy: 0.0579 - Brier score: 0.0124 - tp: 137.0000 - fp: 1554.0000 - tn: 106816.0000 - fn: 37.0000 - accuracy: 0.9853 - precision: 0.0810 - recall: 0.7874 - auc: 0.9363 - prc: 0.3819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67/90 [=====================>........] - ETA: 0s - loss: 0.3743 - cross entropy: 0.0594 - Brier score: 0.0130 - tp: 173.0000 - fp: 2080.0000 - tn: 134915.0000 - fn: 48.0000 - accuracy: 0.9845 - precision: 0.0768 - recall: 0.7828 - auc: 0.9375 - prc: 0.3904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80/90 [=========================>....] - ETA: 0s - loss: 0.3658 - cross entropy: 0.0606 - Brier score: 0.0133 - tp: 211.0000 - fp: 2539.0000 - tn: 161033.0000 - fn: 57.0000 - accuracy: 0.9842 - precision: 0.0767 - recall: 0.7873 - auc: 0.9396 - prc: 0.3997"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.4155 - cross entropy: 0.0619 - Brier score: 0.0136 - tp: 231.0000 - fp: 2898.0000 - tn: 179079.0000 - fn: 68.0000 - accuracy: 0.9837 - precision: 0.0738 - recall: 0.7726 - auc: 0.9272 - prc: 0.3804 - val_loss: 0.0319 - val_cross entropy: 0.0319 - val_Brier score: 0.0046 - val_tp: 70.0000 - val_fp: 188.0000 - val_tn: 45299.0000 - val_fn: 12.0000 - val_accuracy: 0.9956 - val_precision: 0.2713 - val_recall: 0.8537 - val_auc: 0.9697 - val_prc: 0.7095\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0342 - cross entropy: 0.0615 - Brier score: 0.0154 - tp: 1.0000 - fp: 40.0000 - tn: 2007.0000 - fn: 0.0000e+00 - accuracy: 0.9805 - precision: 0.0244 - recall: 1.0000 - auc: 0.9990 - prc: 0.1891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.3195 - cross entropy: 0.0728 - Brier score: 0.0166 - tp: 41.0000 - fp: 558.0000 - tn: 28065.0000 - fn: 8.0000 - accuracy: 0.9803 - precision: 0.0684 - recall: 0.8367 - auc: 0.9453 - prc: 0.4517 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/90 [========>.....................] - ETA: 0s - loss: 0.3319 - cross entropy: 0.0744 - Brier score: 0.0168 - tp: 78.0000 - fp: 1141.0000 - tn: 56109.0000 - fn: 16.0000 - accuracy: 0.9798 - precision: 0.0640 - recall: 0.8298 - auc: 0.9396 - prc: 0.3985"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41/90 [============>.................] - ETA: 0s - loss: 0.3072 - cross entropy: 0.0738 - Brier score: 0.0168 - tp: 111.0000 - fp: 1679.0000 - tn: 82153.0000 - fn: 25.0000 - accuracy: 0.9797 - precision: 0.0620 - recall: 0.8162 - auc: 0.9482 - prc: 0.3973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54/90 [=================>............] - ETA: 0s - loss: 0.2795 - cross entropy: 0.0748 - Brier score: 0.0172 - tp: 150.0000 - fp: 2276.0000 - tn: 108137.0000 - fn: 29.0000 - accuracy: 0.9792 - precision: 0.0618 - recall: 0.8380 - auc: 0.9541 - prc: 0.3929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67/90 [=====================>........] - ETA: 0s - loss: 0.3005 - cross entropy: 0.0760 - Brier score: 0.0175 - tp: 181.0000 - fp: 2873.0000 - tn: 134123.0000 - fn: 39.0000 - accuracy: 0.9788 - precision: 0.0593 - recall: 0.8227 - auc: 0.9501 - prc: 0.3887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80/90 [=========================>....] - ETA: 0s - loss: 0.3032 - cross entropy: 0.0768 - Brier score: 0.0177 - tp: 221.0000 - fp: 3464.0000 - tn: 160106.0000 - fn: 49.0000 - accuracy: 0.9786 - precision: 0.0600 - recall: 0.8185 - auc: 0.9518 - prc: 0.3843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.3247 - cross entropy: 0.0773 - Brier score: 0.0178 - tp: 241.0000 - fp: 3872.0000 - tn: 178105.0000 - fn: 58.0000 - accuracy: 0.9784 - precision: 0.0586 - recall: 0.8060 - auc: 0.9471 - prc: 0.3673 - val_loss: 0.0405 - val_cross entropy: 0.0405 - val_Brier score: 0.0068 - val_tp: 71.0000 - val_fp: 334.0000 - val_tn: 45153.0000 - val_fn: 11.0000 - val_accuracy: 0.9924 - val_precision: 0.1753 - val_recall: 0.8659 - val_auc: 0.9714 - val_prc: 0.6518\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0522 - cross entropy: 0.1042 - Brier score: 0.0213 - tp: 2.0000 - fp: 47.0000 - tn: 1999.0000 - fn: 0.0000e+00 - accuracy: 0.9771 - precision: 0.0408 - recall: 1.0000 - auc: 0.9993 - prc: 0.4000"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.3362 - cross entropy: 0.0899 - Brier score: 0.0206 - tp: 53.0000 - fp: 704.0000 - tn: 27908.0000 - fn: 7.0000 - accuracy: 0.9752 - precision: 0.0700 - recall: 0.8833 - auc: 0.9490 - prc: 0.3941 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.3299 - cross entropy: 0.0916 - Brier score: 0.0212 - tp: 92.0000 - fp: 1401.0000 - tn: 53788.0000 - fn: 15.0000 - accuracy: 0.9744 - precision: 0.0616 - recall: 0.8598 - auc: 0.9513 - prc: 0.3898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.3560 - cross entropy: 0.0935 - Brier score: 0.0215 - tp: 124.0000 - fp: 2089.0000 - tn: 79683.0000 - fn: 24.0000 - accuracy: 0.9742 - precision: 0.0560 - recall: 0.8378 - auc: 0.9387 - prc: 0.3511"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.3469 - cross entropy: 0.0948 - Brier score: 0.0218 - tp: 154.0000 - fp: 2825.0000 - tn: 105534.0000 - fn: 31.0000 - accuracy: 0.9737 - precision: 0.0517 - recall: 0.8324 - auc: 0.9385 - prc: 0.3267"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66/90 [=====================>........] - ETA: 0s - loss: 0.3257 - cross entropy: 0.0964 - Brier score: 0.0221 - tp: 199.0000 - fp: 3543.0000 - tn: 131390.0000 - fn: 36.0000 - accuracy: 0.9735 - precision: 0.0532 - recall: 0.8468 - auc: 0.9449 - prc: 0.3375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79/90 [=========================>....] - ETA: 0s - loss: 0.3322 - cross entropy: 0.0973 - Brier score: 0.0224 - tp: 222.0000 - fp: 4314.0000 - tn: 157212.0000 - fn: 44.0000 - accuracy: 0.9731 - precision: 0.0489 - recall: 0.8346 - auc: 0.9402 - prc: 0.3187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 6ms/step - loss: 0.3481 - cross entropy: 0.0976 - Brier score: 0.0225 - tp: 248.0000 - fp: 4880.0000 - tn: 177097.0000 - fn: 51.0000 - accuracy: 0.9729 - precision: 0.0484 - recall: 0.8294 - auc: 0.9351 - prc: 0.3069 - val_loss: 0.0494 - val_cross entropy: 0.0494 - val_Brier score: 0.0093 - val_tp: 73.0000 - val_fp: 511.0000 - val_tn: 44976.0000 - val_fn: 9.0000 - val_accuracy: 0.9886 - val_precision: 0.1250 - val_recall: 0.8902 - val_auc: 0.9742 - val_prc: 0.6313\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0729 - cross entropy: 0.0888 - Brier score: 0.0209 - tp: 3.0000 - fp: 52.0000 - tn: 1993.0000 - fn: 0.0000e+00 - accuracy: 0.9746 - precision: 0.0545 - recall: 1.0000 - auc: 0.9954 - prc: 0.1723"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.2449 - cross entropy: 0.1034 - Brier score: 0.0235 - tp: 39.0000 - fp: 812.0000 - tn: 27815.0000 - fn: 6.0000 - accuracy: 0.9715 - precision: 0.0458 - recall: 0.8667 - auc: 0.9623 - prc: 0.2725 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.3127 - cross entropy: 0.1060 - Brier score: 0.0245 - tp: 75.0000 - fp: 1647.0000 - tn: 53560.0000 - fn: 14.0000 - accuracy: 0.9700 - precision: 0.0436 - recall: 0.8427 - auc: 0.9460 - prc: 0.2709"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.2494 - cross entropy: 0.1050 - Brier score: 0.0247 - tp: 111.0000 - fp: 2481.0000 - tn: 79310.0000 - fn: 18.0000 - accuracy: 0.9695 - precision: 0.0428 - recall: 0.8605 - auc: 0.9602 - prc: 0.2886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54/90 [=================>............] - ETA: 0s - loss: 0.2536 - cross entropy: 0.1051 - Brier score: 0.0247 - tp: 146.0000 - fp: 3359.0000 - tn: 107062.0000 - fn: 25.0000 - accuracy: 0.9694 - precision: 0.0417 - recall: 0.8538 - auc: 0.9582 - prc: 0.2867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67/90 [=====================>........] - ETA: 0s - loss: 0.2671 - cross entropy: 0.1073 - Brier score: 0.0252 - tp: 183.0000 - fp: 4249.0000 - tn: 132753.0000 - fn: 31.0000 - accuracy: 0.9688 - precision: 0.0413 - recall: 0.8551 - auc: 0.9547 - prc: 0.2818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "80/90 [=========================>....] - ETA: 0s - loss: 0.2768 - cross entropy: 0.1074 - Brier score: 0.0252 - tp: 230.0000 - fp: 5078.0000 - tn: 158495.0000 - fn: 37.0000 - accuracy: 0.9688 - precision: 0.0433 - recall: 0.8614 - auc: 0.9538 - prc: 0.2890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.2719 - cross entropy: 0.1078 - Brier score: 0.0253 - tp: 257.0000 - fp: 5673.0000 - tn: 176304.0000 - fn: 42.0000 - accuracy: 0.9686 - precision: 0.0433 - recall: 0.8595 - auc: 0.9564 - prc: 0.2894 - val_loss: 0.0565 - val_cross entropy: 0.0565 - val_Brier score: 0.0112 - val_tp: 73.0000 - val_fp: 633.0000 - val_tn: 44854.0000 - val_fn: 9.0000 - val_accuracy: 0.9859 - val_precision: 0.1034 - val_recall: 0.8902 - val_auc: 0.9757 - val_prc: 0.6267\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0599 - cross entropy: 0.1195 - Brier score: 0.0272 - tp: 0.0000e+00 - fp: 69.0000 - tn: 1979.0000 - fn: 0.0000e+00 - accuracy: 0.9663 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.0000e+00 - prc: 0.0000e+00"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.2047 - cross entropy: 0.1191 - Brier score: 0.0279 - tp: 37.0000 - fp: 973.0000 - tn: 27658.0000 - fn: 4.0000 - accuracy: 0.9659 - precision: 0.0366 - recall: 0.9024 - auc: 0.9582 - prc: 0.2579                     "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.2056 - cross entropy: 0.1170 - Brier score: 0.0273 - tp: 76.0000 - fp: 1847.0000 - tn: 53365.0000 - fn: 8.0000 - accuracy: 0.9665 - precision: 0.0395 - recall: 0.9048 - auc: 0.9648 - prc: 0.2822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.2763 - cross entropy: 0.1157 - Brier score: 0.0272 - tp: 121.0000 - fp: 2730.0000 - tn: 79053.0000 - fn: 16.0000 - accuracy: 0.9665 - precision: 0.0424 - recall: 0.8832 - auc: 0.9536 - prc: 0.2848"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.3031 - cross entropy: 0.1176 - Brier score: 0.0274 - tp: 167.0000 - fp: 3620.0000 - tn: 104732.0000 - fn: 25.0000 - accuracy: 0.9664 - precision: 0.0441 - recall: 0.8698 - auc: 0.9504 - prc: 0.2817"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66/90 [=====================>........] - ETA: 0s - loss: 0.2716 - cross entropy: 0.1183 - Brier score: 0.0275 - tp: 198.0000 - fp: 4526.0000 - tn: 130416.0000 - fn: 28.0000 - accuracy: 0.9663 - precision: 0.0419 - recall: 0.8761 - auc: 0.9553 - prc: 0.2646"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79/90 [=========================>....] - ETA: 0s - loss: 0.2598 - cross entropy: 0.1183 - Brier score: 0.0276 - tp: 234.0000 - fp: 5455.0000 - tn: 156071.0000 - fn: 32.0000 - accuracy: 0.9661 - precision: 0.0411 - recall: 0.8797 - auc: 0.9557 - prc: 0.2618"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 6ms/step - loss: 0.2623 - cross entropy: 0.1179 - Brier score: 0.0275 - tp: 262.0000 - fp: 6123.0000 - tn: 175854.0000 - fn: 37.0000 - accuracy: 0.9662 - precision: 0.0410 - recall: 0.8763 - auc: 0.9554 - prc: 0.2609 - val_loss: 0.0607 - val_cross entropy: 0.0607 - val_Brier score: 0.0124 - val_tp: 73.0000 - val_fp: 686.0000 - val_tn: 44801.0000 - val_fn: 9.0000 - val_accuracy: 0.9847 - val_precision: 0.0962 - val_recall: 0.8902 - val_auc: 0.9754 - val_prc: 0.6069\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0662 - cross entropy: 0.1258 - Brier score: 0.0290 - tp: 4.0000 - fp: 79.0000 - tn: 1965.0000 - fn: 0.0000e+00 - accuracy: 0.9614 - precision: 0.0482 - recall: 1.0000 - auc: 0.9975 - prc: 0.2897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/90 [====>.........................] - ETA: 0s - loss: 0.2339 - cross entropy: 0.1266 - Brier score: 0.0294 - tp: 43.0000 - fp: 1125.0000 - tn: 29548.0000 - fn: 4.0000 - accuracy: 0.9632 - precision: 0.0368 - recall: 0.9149 - auc: 0.9515 - prc: 0.2391"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/90 [========>.....................] - ETA: 0s - loss: 0.2905 - cross entropy: 0.1221 - Brier score: 0.0286 - tp: 72.0000 - fp: 2042.0000 - tn: 55220.0000 - fn: 10.0000 - accuracy: 0.9642 - precision: 0.0341 - recall: 0.8780 - auc: 0.9350 - prc: 0.2257"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "41/90 [============>.................] - ETA: 0s - loss: 0.2432 - cross entropy: 0.1206 - Brier score: 0.0283 - tp: 117.0000 - fp: 2936.0000 - tn: 80903.0000 - fn: 12.0000 - accuracy: 0.9649 - precision: 0.0383 - recall: 0.9070 - auc: 0.9508 - prc: 0.2659"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "54/90 [=================>............] - ETA: 0s - loss: 0.3018 - cross entropy: 0.1197 - Brier score: 0.0280 - tp: 153.0000 - fp: 3826.0000 - tn: 106589.0000 - fn: 24.0000 - accuracy: 0.9652 - precision: 0.0385 - recall: 0.8644 - auc: 0.9417 - prc: 0.2628"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "67/90 [=====================>........] - ETA: 0s - loss: 0.2870 - cross entropy: 0.1181 - Brier score: 0.0278 - tp: 191.0000 - fp: 4707.0000 - tn: 132289.0000 - fn: 29.0000 - accuracy: 0.9655 - precision: 0.0390 - recall: 0.8682 - auc: 0.9475 - prc: 0.2607"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "81/90 [==========================>...] - ETA: 0s - loss: 0.2732 - cross entropy: 0.1185 - Brier score: 0.0279 - tp: 234.0000 - fp: 5715.0000 - tn: 159903.0000 - fn: 36.0000 - accuracy: 0.9653 - precision: 0.0393 - recall: 0.8667 - auc: 0.9540 - prc: 0.2618"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 5ms/step - loss: 0.2915 - cross entropy: 0.1184 - Brier score: 0.0280 - tp: 257.0000 - fp: 6295.0000 - tn: 175682.0000 - fn: 42.0000 - accuracy: 0.9652 - precision: 0.0392 - recall: 0.8595 - auc: 0.9494 - prc: 0.2652 - val_loss: 0.0653 - val_cross entropy: 0.0653 - val_Brier score: 0.0135 - val_tp: 74.0000 - val_fp: 742.0000 - val_tn: 44745.0000 - val_fn: 8.0000 - val_accuracy: 0.9835 - val_precision: 0.0907 - val_recall: 0.9024 - val_auc: 0.9773 - val_prc: 0.5856\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0564 - cross entropy: 0.1126 - Brier score: 0.0305 - tp: 4.0000 - fp: 84.0000 - tn: 1960.0000 - fn: 0.0000e+00 - accuracy: 0.9590 - precision: 0.0455 - recall: 1.0000 - auc: 0.9995 - prc: 0.6667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.2057 - cross entropy: 0.1256 - Brier score: 0.0300 - tp: 42.0000 - fp: 1068.0000 - tn: 27556.0000 - fn: 6.0000 - accuracy: 0.9625 - precision: 0.0378 - recall: 0.8750 - auc: 0.9742 - prc: 0.3016"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.2650 - cross entropy: 0.1283 - Brier score: 0.0305 - tp: 75.0000 - fp: 2065.0000 - tn: 53141.0000 - fn: 15.0000 - accuracy: 0.9624 - precision: 0.0350 - recall: 0.8333 - auc: 0.9617 - prc: 0.2623"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.2633 - cross entropy: 0.1289 - Brier score: 0.0306 - tp: 116.0000 - fp: 3071.0000 - tn: 78712.0000 - fn: 21.0000 - accuracy: 0.9623 - precision: 0.0364 - recall: 0.8467 - auc: 0.9644 - prc: 0.2490"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.2632 - cross entropy: 0.1311 - Brier score: 0.0310 - tp: 147.0000 - fp: 4110.0000 - tn: 104260.0000 - fn: 27.0000 - accuracy: 0.9619 - precision: 0.0345 - recall: 0.8448 - auc: 0.9589 - prc: 0.2367"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66/90 [=====================>........] - ETA: 0s - loss: 0.2627 - cross entropy: 0.1322 - Brier score: 0.0312 - tp: 184.0000 - fp: 5132.0000 - tn: 129821.0000 - fn: 31.0000 - accuracy: 0.9618 - precision: 0.0346 - recall: 0.8558 - auc: 0.9561 - prc: 0.2383"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79/90 [=========================>....] - ETA: 0s - loss: 0.2724 - cross entropy: 0.1329 - Brier score: 0.0313 - tp: 235.0000 - fp: 6183.0000 - tn: 155337.0000 - fn: 37.0000 - accuracy: 0.9616 - precision: 0.0366 - recall: 0.8640 - auc: 0.9546 - prc: 0.2476"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 6ms/step - loss: 0.2632 - cross entropy: 0.1336 - Brier score: 0.0313 - tp: 259.0000 - fp: 6976.0000 - tn: 175001.0000 - fn: 40.0000 - accuracy: 0.9615 - precision: 0.0358 - recall: 0.8662 - auc: 0.9561 - prc: 0.2365 - val_loss: 0.0700 - val_cross entropy: 0.0700 - val_Brier score: 0.0146 - val_tp: 76.0000 - val_fp: 801.0000 - val_tn: 44686.0000 - val_fn: 6.0000 - val_accuracy: 0.9823 - val_precision: 0.0867 - val_recall: 0.9268 - val_auc: 0.9773 - val_prc: 0.5876\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0667 - cross entropy: 0.1329 - Brier score: 0.0324 - tp: 1.0000 - fp: 77.0000 - tn: 1970.0000 - fn: 0.0000e+00 - accuracy: 0.9624 - precision: 0.0128 - recall: 1.0000 - auc: 0.9988 - prc: 0.1667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.3013 - cross entropy: 0.1255 - Brier score: 0.0303 - tp: 40.0000 - fp: 1066.0000 - tn: 27560.0000 - fn: 6.0000 - accuracy: 0.9626 - precision: 0.0362 - recall: 0.8696 - auc: 0.9384 - prc: 0.2439"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.2414 - cross entropy: 0.1266 - Brier score: 0.0302 - tp: 77.0000 - fp: 2039.0000 - tn: 53170.0000 - fn: 10.0000 - accuracy: 0.9629 - precision: 0.0364 - recall: 0.8851 - auc: 0.9563 - prc: 0.2411"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.2574 - cross entropy: 0.1284 - Brier score: 0.0305 - tp: 110.0000 - fp: 3063.0000 - tn: 78733.0000 - fn: 14.0000 - accuracy: 0.9624 - precision: 0.0347 - recall: 0.8871 - auc: 0.9461 - prc: 0.2214"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.2421 - cross entropy: 0.1287 - Brier score: 0.0304 - tp: 152.0000 - fp: 4041.0000 - tn: 104334.0000 - fn: 17.0000 - accuracy: 0.9626 - precision: 0.0363 - recall: 0.8994 - auc: 0.9515 - prc: 0.2331"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66/90 [=====================>........] - ETA: 0s - loss: 0.2238 - cross entropy: 0.1289 - Brier score: 0.0303 - tp: 191.0000 - fp: 5008.0000 - tn: 129949.0000 - fn: 20.0000 - accuracy: 0.9628 - precision: 0.0367 - recall: 0.9052 - auc: 0.9576 - prc: 0.2392"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79/90 [=========================>....] - ETA: 0s - loss: 0.2255 - cross entropy: 0.1282 - Brier score: 0.0300 - tp: 235.0000 - fp: 5956.0000 - tn: 155576.0000 - fn: 25.0000 - accuracy: 0.9630 - precision: 0.0380 - recall: 0.9038 - auc: 0.9595 - prc: 0.2481"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2336 - cross entropy: 0.1282 - Brier score: 0.0299 - tp: 269.0000 - fp: 6690.0000 - tn: 175287.0000 - fn: 30.0000 - accuracy: 0.9631 - precision: 0.0387 - recall: 0.8997 - auc: 0.9586 - prc: 0.2494 - val_loss: 0.0679 - val_cross entropy: 0.0679 - val_Brier score: 0.0140 - val_tp: 76.0000 - val_fp: 757.0000 - val_tn: 44730.0000 - val_fn: 6.0000 - val_accuracy: 0.9833 - val_precision: 0.0912 - val_recall: 0.9268 - val_auc: 0.9777 - val_prc: 0.5891\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.4784 - cross entropy: 0.1336 - Brier score: 0.0310 - tp: 0.0000e+00 - fp: 74.0000 - tn: 1972.0000 - fn: 2.0000 - accuracy: 0.9629 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.9174 - prc: 0.0061"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.2320 - cross entropy: 0.1245 - Brier score: 0.0296 - tp: 27.0000 - fp: 1041.0000 - tn: 27596.0000 - fn: 8.0000 - accuracy: 0.9634 - precision: 0.0253 - recall: 0.7714 - auc: 0.9605 - prc: 0.1987        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.2441 - cross entropy: 0.1275 - Brier score: 0.0299 - tp: 68.0000 - fp: 2051.0000 - tn: 53164.0000 - fn: 13.0000 - accuracy: 0.9627 - precision: 0.0321 - recall: 0.8395 - auc: 0.9610 - prc: 0.2322"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.2469 - cross entropy: 0.1293 - Brier score: 0.0301 - tp: 105.0000 - fp: 3023.0000 - tn: 78774.0000 - fn: 18.0000 - accuracy: 0.9629 - precision: 0.0336 - recall: 0.8537 - auc: 0.9604 - prc: 0.2284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.2245 - cross entropy: 0.1313 - Brier score: 0.0303 - tp: 136.0000 - fp: 4028.0000 - tn: 104359.0000 - fn: 21.0000 - accuracy: 0.9627 - precision: 0.0327 - recall: 0.8662 - auc: 0.9632 - prc: 0.2235"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "65/90 [====================>.........] - ETA: 0s - loss: 0.2408 - cross entropy: 0.1315 - Brier score: 0.0304 - tp: 166.0000 - fp: 4944.0000 - tn: 127984.0000 - fn: 26.0000 - accuracy: 0.9627 - precision: 0.0325 - recall: 0.8646 - auc: 0.9557 - prc: 0.2181"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "78/90 [=========================>....] - ETA: 0s - loss: 0.2257 - cross entropy: 0.1299 - Brier score: 0.0301 - tp: 208.0000 - fp: 5893.0000 - tn: 153615.0000 - fn: 28.0000 - accuracy: 0.9629 - precision: 0.0341 - recall: 0.8814 - auc: 0.9595 - prc: 0.2348"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2399 - cross entropy: 0.1289 - Brier score: 0.0298 - tp: 265.0000 - fp: 6654.0000 - tn: 175323.0000 - fn: 34.0000 - accuracy: 0.9633 - precision: 0.0383 - recall: 0.8863 - auc: 0.9602 - prc: 0.2601 - val_loss: 0.0684 - val_cross entropy: 0.0684 - val_Brier score: 0.0141 - val_tp: 76.0000 - val_fp: 762.0000 - val_tn: 44725.0000 - val_fn: 6.0000 - val_accuracy: 0.9831 - val_precision: 0.0907 - val_recall: 0.9268 - val_auc: 0.9784 - val_prc: 0.5848\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 0s - loss: 0.0556 - cross entropy: 0.1110 - Brier score: 0.0280 - tp: 2.0000 - fp: 73.0000 - tn: 1973.0000 - fn: 0.0000e+00 - accuracy: 0.9644 - precision: 0.0267 - recall: 1.0000 - auc: 0.9990 - prc: 0.3333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/90 [===>..........................] - ETA: 0s - loss: 0.2314 - cross entropy: 0.1289 - Brier score: 0.0299 - tp: 44.0000 - fp: 1032.0000 - tn: 27592.0000 - fn: 4.0000 - accuracy: 0.9639 - precision: 0.0409 - recall: 0.9167 - auc: 0.9651 - prc: 0.3002"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "27/90 [========>.....................] - ETA: 0s - loss: 0.1991 - cross entropy: 0.1266 - Brier score: 0.0295 - tp: 86.0000 - fp: 1991.0000 - tn: 53212.0000 - fn: 7.0000 - accuracy: 0.9639 - precision: 0.0414 - recall: 0.9247 - auc: 0.9732 - prc: 0.3105"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s - loss: 0.2023 - cross entropy: 0.1258 - Brier score: 0.0294 - tp: 122.0000 - fp: 2947.0000 - tn: 78836.0000 - fn: 15.0000 - accuracy: 0.9638 - precision: 0.0398 - recall: 0.8905 - auc: 0.9760 - prc: 0.2958"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "53/90 [================>.............] - ETA: 0s - loss: 0.2163 - cross entropy: 0.1244 - Brier score: 0.0291 - tp: 165.0000 - fp: 3854.0000 - tn: 104502.0000 - fn: 23.0000 - accuracy: 0.9643 - precision: 0.0411 - recall: 0.8777 - auc: 0.9731 - prc: 0.2994"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "66/90 [=====================>........] - ETA: 0s - loss: 0.2148 - cross entropy: 0.1253 - Brier score: 0.0293 - tp: 196.0000 - fp: 4846.0000 - tn: 130100.0000 - fn: 26.0000 - accuracy: 0.9640 - precision: 0.0389 - recall: 0.8829 - auc: 0.9712 - prc: 0.2749"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "79/90 [=========================>....] - ETA: 0s - loss: 0.2286 - cross entropy: 0.1265 - Brier score: 0.0295 - tp: 237.0000 - fp: 5838.0000 - tn: 155684.0000 - fn: 33.0000 - accuracy: 0.9637 - precision: 0.0390 - recall: 0.8778 - auc: 0.9696 - prc: 0.2645"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restoring model weights from the end of the best epoch: 3.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 1s 6ms/step - loss: 0.2341 - cross entropy: 0.1275 - Brier score: 0.0297 - tp: 262.0000 - fp: 6631.0000 - tn: 175346.0000 - fn: 37.0000 - accuracy: 0.9634 - precision: 0.0380 - recall: 0.8763 - auc: 0.9665 - prc: 0.2538 - val_loss: 0.0757 - val_cross entropy: 0.0757 - val_Brier score: 0.0159 - val_tp: 76.0000 - val_fp: 834.0000 - val_tn: 44653.0000 - val_fn: 6.0000 - val_accuracy: 0.9816 - val_precision: 0.0835 - val_recall: 0.9268 - val_auc: 0.9789 - val_prc: 0.5709\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13: early stopping\n"
     ]
    }
   ],
   "source": [
    "weighted_model = make_model()\n",
    "weighted_model.load_weights(initial_weights)\n",
    "\n",
    "weighted_history = weighted_model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=(val_features, val_labels),\n",
    "    # The class weights go here\n",
    "    class_weight=class_weight) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R0ynYRO0G3Lx"
   },
   "source": [
    "### Check training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:39.463567Z",
     "iopub.status.busy": "2024-01-17T02:21:39.462997Z",
     "iopub.status.idle": "2024-01-17T02:21:40.000583Z",
     "shell.execute_reply": "2024-01-17T02:21:39.999835Z"
    },
    "id": "BBe9FMO5ucTC"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(weighted_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "REy6WClTZIwQ"
   },
   "source": [
    "### Evaluate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:40.004799Z",
     "iopub.status.busy": "2024-01-17T02:21:40.004167Z",
     "iopub.status.idle": "2024-01-17T02:21:40.476395Z",
     "shell.execute_reply": "2024-01-17T02:21:40.475576Z"
    },
    "id": "nifqscPGw-5w"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 6s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "36/90 [===========>..................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "73/90 [=======================>......] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/28 [>.............................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/28 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:40.479906Z",
     "iopub.status.busy": "2024-01-17T02:21:40.479644Z",
     "iopub.status.idle": "2024-01-17T02:21:40.834910Z",
     "shell.execute_reply": "2024-01-17T02:21:40.834288Z"
    },
    "id": "owKL2vdMBJr6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.024716919288039207\n",
      "cross entropy :  0.024716919288039207\n",
      "Brier score :  0.0029473488684743643\n",
      "tp :  88.0\n",
      "fp :  134.0\n",
      "tn :  56717.0\n",
      "fn :  23.0\n",
      "accuracy :  0.9972437620162964\n",
      "precision :  0.3963963985443115\n",
      "recall :  0.792792797088623\n",
      "auc :  0.9477326273918152\n",
      "prc :  0.6732124090194702\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  56717\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  134\n",
      "Fraudulent Transactions Missed (False Negatives):  23\n",
      "Fraudulent Transactions Detected (True Positives):  88\n",
      "Total Fraudulent Transactions:  111\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_results = weighted_model.evaluate(test_features, test_labels,\n",
    "                                           batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(weighted_model.metrics_names, weighted_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_weighted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PTh1rtDn8r4-"
   },
   "source": [
    "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions than the baseline model at threshold 50%). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade-offs between these different types of errors for your application.\n",
    "\n",
    "Compared to the baseline model with changed threshold, the class weighted model is clearly inferior. The superiority of the baseline model is further confirmed by the lower test loss value (cross entropy and mean squared error) and additionally can be seen by plotting the ROC curves of both models together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hXDAwyr0HYdX"
   },
   "source": [
    "### Plot the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:40.838456Z",
     "iopub.status.busy": "2024-01-17T02:21:40.838199Z",
     "iopub.status.idle": "2024-01-17T02:21:41.374086Z",
     "shell.execute_reply": "2024-01-17T02:21:41.373352Z"
    },
    "id": "3hzScIVZS1Xm"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0krS8g1OTbD"
   },
   "source": [
    "### Plot the PRC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:41.377809Z",
     "iopub.status.busy": "2024-01-17T02:21:41.377546Z",
     "iopub.status.idle": "2024-01-17T02:21:41.721595Z",
     "shell.execute_reply": "2024-01-17T02:21:41.720701Z"
    },
    "id": "7jHnmVebOWOC"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ysRtr6xHnXP"
   },
   "source": [
    "## Oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "18VUHNc-UF5w"
   },
   "source": [
    "### Oversample the minority class\n",
    "\n",
    "A related approach would be to resample the dataset by oversampling the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:41.725544Z",
     "iopub.status.busy": "2024-01-17T02:21:41.725269Z",
     "iopub.status.idle": "2024-01-17T02:21:41.749868Z",
     "shell.execute_reply": "2024-01-17T02:21:41.749140Z"
    },
    "id": "sHirNp6u7OWp"
   },
   "outputs": [],
   "source": [
    "pos_features = train_features[bool_train_labels]\n",
    "neg_features = train_features[~bool_train_labels]\n",
    "\n",
    "pos_labels = train_labels[bool_train_labels]\n",
    "neg_labels = train_labels[~bool_train_labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WgBVbX7P7QrL"
   },
   "source": [
    "#### Using NumPy\n",
    "\n",
    "You can balance the dataset manually by choosing the right number of random \n",
    "indices from the positive examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:41.753793Z",
     "iopub.status.busy": "2024-01-17T02:21:41.753538Z",
     "iopub.status.idle": "2024-01-17T02:21:41.776721Z",
     "shell.execute_reply": "2024-01-17T02:21:41.776062Z"
    },
    "id": "BUzGjSkwqT88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181977, 29)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = np.arange(len(pos_features))\n",
    "choices = np.random.choice(ids, len(neg_features))\n",
    "\n",
    "res_pos_features = pos_features[choices]\n",
    "res_pos_labels = pos_labels[choices]\n",
    "\n",
    "res_pos_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:41.780206Z",
     "iopub.status.busy": "2024-01-17T02:21:41.779856Z",
     "iopub.status.idle": "2024-01-17T02:21:41.883029Z",
     "shell.execute_reply": "2024-01-17T02:21:41.882370Z"
    },
    "id": "7ie_FFet6cep"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363954, 29)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n",
    "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n",
    "\n",
    "order = np.arange(len(resampled_labels))\n",
    "np.random.shuffle(order)\n",
    "resampled_features = resampled_features[order]\n",
    "resampled_labels = resampled_labels[order]\n",
    "\n",
    "resampled_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IYfJe2Kc-FAz"
   },
   "source": [
    "#### Using `tf.data`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "usyixaST8v5P"
   },
   "source": [
    "If you're using `tf.data` the easiest way to produce balanced examples is to start with a `positive` and a `negative` dataset, and merge them. See [the tf.data guide](../../guide/data.ipynb) for more examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:41.886714Z",
     "iopub.status.busy": "2024-01-17T02:21:41.886435Z",
     "iopub.status.idle": "2024-01-17T02:21:42.003767Z",
     "shell.execute_reply": "2024-01-17T02:21:42.003104Z"
    },
    "id": "yF4OZ-rI6xb6"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 100000\n",
    "\n",
    "def make_ds(features, labels):\n",
    "  ds = tf.data.Dataset.from_tensor_slices((features, labels))#.cache()\n",
    "  ds = ds.shuffle(BUFFER_SIZE).repeat()\n",
    "  return ds\n",
    "\n",
    "pos_ds = make_ds(pos_features, pos_labels)\n",
    "neg_ds = make_ds(neg_features, neg_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RNQUx-OA-oJc"
   },
   "source": [
    "Each dataset provides `(feature, label)` pairs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:42.007494Z",
     "iopub.status.busy": "2024-01-17T02:21:42.007251Z",
     "iopub.status.idle": "2024-01-17T02:21:42.024267Z",
     "shell.execute_reply": "2024-01-17T02:21:42.023517Z"
    },
    "id": "llXc9rNH7Fbz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      " [ 4.57437149e-03  1.41282803e+00 -1.70738347e+00  7.86145002e-01\n",
      "  2.34322123e+00 -1.32760854e+00  1.68238195e+00 -7.10272314e-01\n",
      "  8.18760297e-01 -3.09684905e+00  2.01295966e+00 -3.98984767e+00\n",
      "  1.02827419e+00 -5.00000000e+00 -1.25820263e+00  1.91494135e+00\n",
      "  5.00000000e+00  3.32009026e+00 -2.75342824e+00 -8.47588695e-03\n",
      " -7.83382558e-01 -1.24259811e+00 -6.45039879e-01 -1.71393384e-02\n",
      "  1.13211907e+00 -1.52256293e+00 -1.08919872e+00 -1.06657977e+00\n",
      " -1.45889491e+00]\n",
      "\n",
      "Label:  1\n"
     ]
    }
   ],
   "source": [
    "for features, label in pos_ds.take(1):\n",
    "  print(\"Features:\\n\", features.numpy())\n",
    "  print()\n",
    "  print(\"Label: \", label.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sLEfjZO0-vbN"
   },
   "source": [
    "Merge the two together using `tf.data.Dataset.sample_from_datasets`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:42.027466Z",
     "iopub.status.busy": "2024-01-17T02:21:42.027224Z",
     "iopub.status.idle": "2024-01-17T02:21:42.049491Z",
     "shell.execute_reply": "2024-01-17T02:21:42.048841Z"
    },
    "id": "e7w9UQPT9wzE"
   },
   "outputs": [],
   "source": [
    "resampled_ds = tf.data.Dataset.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n",
    "resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:42.052890Z",
     "iopub.status.busy": "2024-01-17T02:21:42.052631Z",
     "iopub.status.idle": "2024-01-17T02:21:42.293903Z",
     "shell.execute_reply": "2024-01-17T02:21:42.293231Z"
    },
    "id": "EWXARdTdAuQK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.50341796875\n"
     ]
    }
   ],
   "source": [
    "for features, label in resampled_ds.take(1):\n",
    "  print(label.numpy().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "irgqf3YxAyN0"
   },
   "source": [
    "To use this dataset, you'll need the number of steps per epoch.\n",
    "\n",
    "The definition of \"epoch\" in this case is less clear. Say it's the number of batches required to see each negative example once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:42.297704Z",
     "iopub.status.busy": "2024-01-17T02:21:42.297074Z",
     "iopub.status.idle": "2024-01-17T02:21:42.302012Z",
     "shell.execute_reply": "2024-01-17T02:21:42.301413Z"
    },
    "id": "xH-7K46AAxpq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)\n",
    "resampled_steps_per_epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XZ1BvEpcBVHP"
   },
   "source": [
    "### Train on the oversampled data\n",
    "\n",
    "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n",
    "\n",
    "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:21:42.305074Z",
     "iopub.status.busy": "2024-01-17T02:21:42.304812Z",
     "iopub.status.idle": "2024-01-17T02:22:45.564244Z",
     "shell.execute_reply": "2024-01-17T02:22:45.563511Z"
    },
    "id": "soRQ89JYqd6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 6:52 - loss: 0.7700 - cross entropy: 0.0506 - Brier score: 0.0122 - tp: 935.0000 - fp: 900.0000 - tn: 57007.0000 - fn: 168.0000 - accuracy: 0.9819 - precision: 0.5095 - recall: 0.8477 - auc: 0.9897 - prc: 0.8138"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 3s - loss: 0.7286 - cross entropy: 0.1320 - Brier score: 0.0412 - tp: 4604.0000 - fp: 3754.0000 - tn: 58159.0000 - fn: 685.0000 - accuracy: 0.9339 - precision: 0.5508 - recall: 0.8705 - auc: 0.9818 - prc: 0.8537"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.7176 - cross entropy: 0.1795 - Brier score: 0.0583 - tp: 7398.0000 - fp: 5889.0000 - tn: 59021.0000 - fn: 1038.0000 - accuracy: 0.9056 - precision: 0.5568 - recall: 0.8770 - auc: 0.9749 - prc: 0.8635"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 5s - loss: 0.7100 - cross entropy: 0.2189 - Brier score: 0.0725 - tp: 10116.0000 - fp: 8029.0000 - tn: 59941.0000 - fn: 1404.0000 - accuracy: 0.8813 - precision: 0.5575 - recall: 0.8781 - auc: 0.9685 - prc: 0.8678"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 5s - loss: 0.6957 - cross entropy: 0.2494 - Brier score: 0.0837 - tp: 12913.0000 - fp: 10062.0000 - tn: 60929.0000 - fn: 1730.0000 - accuracy: 0.8623 - precision: 0.5620 - recall: 0.8819 - auc: 0.9636 - prc: 0.8739"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 5s - loss: 0.6869 - cross entropy: 0.2759 - Brier score: 0.0934 - tp: 15650.0000 - fp: 12093.0000 - tn: 61958.0000 - fn: 2077.0000 - accuracy: 0.8456 - precision: 0.5641 - recall: 0.8828 - auc: 0.9588 - prc: 0.8772"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 5s - loss: 0.6796 - cross entropy: 0.2986 - Brier score: 0.1017 - tp: 18384.0000 - fp: 14142.0000 - tn: 63014.0000 - fn: 2382.0000 - accuracy: 0.8313 - precision: 0.5652 - recall: 0.8853 - auc: 0.9547 - prc: 0.8801"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.6697 - cross entropy: 0.3166 - Brier score: 0.1084 - tp: 21193.0000 - fp: 16082.0000 - tn: 64105.0000 - fn: 2686.0000 - accuracy: 0.8197 - precision: 0.5686 - recall: 0.8875 - auc: 0.9515 - prc: 0.8836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.6619 - cross entropy: 0.3326 - Brier score: 0.1142 - tp: 23941.0000 - fp: 17992.0000 - tn: 65265.0000 - fn: 3012.0000 - accuracy: 0.8094 - precision: 0.5709 - recall: 0.8882 - auc: 0.9482 - prc: 0.8859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.6555 - cross entropy: 0.3467 - Brier score: 0.1194 - tp: 26734.0000 - fp: 19935.0000 - tn: 66369.0000 - fn: 3316.0000 - accuracy: 0.8002 - precision: 0.5728 - recall: 0.8897 - auc: 0.9454 - prc: 0.8882"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.6488 - cross entropy: 0.3586 - Brier score: 0.1239 - tp: 29480.0000 - fp: 21839.0000 - tn: 67551.0000 - fn: 3628.0000 - accuracy: 0.7921 - precision: 0.5744 - recall: 0.8904 - auc: 0.9429 - prc: 0.8904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.6415 - cross entropy: 0.3684 - Brier score: 0.1276 - tp: 32224.0000 - fp: 23706.0000 - tn: 68816.0000 - fn: 3896.0000 - accuracy: 0.7854 - precision: 0.5761 - recall: 0.8921 - auc: 0.9411 - prc: 0.8928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.6354 - cross entropy: 0.3773 - Brier score: 0.1310 - tp: 34998.0000 - fp: 25518.0000 - tn: 70087.0000 - fn: 4183.0000 - accuracy: 0.7796 - precision: 0.5783 - recall: 0.8932 - auc: 0.9392 - prc: 0.8948"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.6285 - cross entropy: 0.3845 - Brier score: 0.1336 - tp: 37772.0000 - fp: 27229.0000 - tn: 71474.0000 - fn: 4455.0000 - accuracy: 0.7752 - precision: 0.5811 - recall: 0.8945 - auc: 0.9379 - prc: 0.8970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.6231 - cross entropy: 0.3913 - Brier score: 0.1361 - tp: 40522.0000 - fp: 28939.0000 - tn: 72868.0000 - fn: 4745.0000 - accuracy: 0.7710 - precision: 0.5834 - recall: 0.8952 - auc: 0.9365 - prc: 0.8986"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.6166 - cross entropy: 0.3965 - Brier score: 0.1380 - tp: 43303.0000 - fp: 30556.0000 - tn: 74324.0000 - fn: 5035.0000 - accuracy: 0.7677 - precision: 0.5863 - recall: 0.8958 - auc: 0.9353 - prc: 0.9004"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.6106 - cross entropy: 0.4012 - Brier score: 0.1397 - tp: 46041.0000 - fp: 32185.0000 - tn: 75835.0000 - fn: 5301.0000 - accuracy: 0.7648 - precision: 0.5886 - recall: 0.8968 - auc: 0.9344 - prc: 0.9021"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.6044 - cross entropy: 0.4049 - Brier score: 0.1412 - tp: 48835.0000 - fp: 33753.0000 - tn: 77326.0000 - fn: 5592.0000 - accuracy: 0.7623 - precision: 0.5913 - recall: 0.8973 - auc: 0.9334 - prc: 0.9037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.5977 - cross entropy: 0.4076 - Brier score: 0.1422 - tp: 51632.0000 - fp: 35227.0000 - tn: 78923.0000 - fn: 5868.0000 - accuracy: 0.7606 - precision: 0.5944 - recall: 0.8979 - auc: 0.9327 - prc: 0.9053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.5912 - cross entropy: 0.4097 - Brier score: 0.1430 - tp: 54428.0000 - fp: 36650.0000 - tn: 80587.0000 - fn: 6129.0000 - accuracy: 0.7594 - precision: 0.5976 - recall: 0.8988 - auc: 0.9324 - prc: 0.9071"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.5853 - cross entropy: 0.4117 - Brier score: 0.1437 - tp: 57221.0000 - fp: 38054.0000 - tn: 82283.0000 - fn: 6380.0000 - accuracy: 0.7584 - precision: 0.6006 - recall: 0.8997 - auc: 0.9320 - prc: 0.9087"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.5791 - cross entropy: 0.4130 - Brier score: 0.1442 - tp: 60031.0000 - fp: 39389.0000 - tn: 84000.0000 - fn: 6662.0000 - accuracy: 0.7577 - precision: 0.6038 - recall: 0.9001 - auc: 0.9316 - prc: 0.9101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 4s - loss: 0.5736 - cross entropy: 0.4143 - Brier score: 0.1446 - tp: 62772.0000 - fp: 40630.0000 - tn: 85852.0000 - fn: 6972.0000 - accuracy: 0.7574 - precision: 0.6071 - recall: 0.9000 - auc: 0.9312 - prc: 0.9113"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 3s - loss: 0.5679 - cross entropy: 0.4150 - Brier score: 0.1448 - tp: 65544.0000 - fp: 41842.0000 - tn: 87713.0000 - fn: 7271.0000 - accuracy: 0.7573 - precision: 0.6104 - recall: 0.9001 - auc: 0.9308 - prc: 0.9125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.5622 - cross entropy: 0.4154 - Brier score: 0.1449 - tp: 68335.0000 - fp: 43025.0000 - tn: 89613.0000 - fn: 7541.0000 - accuracy: 0.7575 - precision: 0.6136 - recall: 0.9006 - auc: 0.9307 - prc: 0.9139"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.5570 - cross entropy: 0.4157 - Brier score: 0.1450 - tp: 71070.0000 - fp: 44209.0000 - tn: 91554.0000 - fn: 7825.0000 - accuracy: 0.7576 - precision: 0.6165 - recall: 0.9008 - auc: 0.9306 - prc: 0.9150"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.5516 - cross entropy: 0.4157 - Brier score: 0.1449 - tp: 73875.0000 - fp: 45315.0000 - tn: 93524.0000 - fn: 8088.0000 - accuracy: 0.7581 - precision: 0.6198 - recall: 0.9013 - auc: 0.9306 - prc: 0.9163"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.5466 - cross entropy: 0.4156 - Brier score: 0.1449 - tp: 76640.0000 - fp: 46408.0000 - tn: 95528.0000 - fn: 8370.0000 - accuracy: 0.7586 - precision: 0.6228 - recall: 0.9015 - auc: 0.9306 - prc: 0.9174"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.5415 - cross entropy: 0.4152 - Brier score: 0.1447 - tp: 79455.0000 - fp: 47477.0000 - tn: 97518.0000 - fn: 8640.0000 - accuracy: 0.7592 - precision: 0.6260 - recall: 0.9019 - auc: 0.9307 - prc: 0.9186"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.5365 - cross entropy: 0.4146 - Brier score: 0.1443 - tp: 82276.0000 - fp: 48443.0000 - tn: 99591.0000 - fn: 8924.0000 - accuracy: 0.7602 - precision: 0.6294 - recall: 0.9021 - auc: 0.9307 - prc: 0.9197"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.5319 - cross entropy: 0.4142 - Brier score: 0.1441 - tp: 85045.0000 - fp: 49398.0000 - tn: 101716.0000 - fn: 9219.0000 - accuracy: 0.7611 - precision: 0.6326 - recall: 0.9022 - auc: 0.9308 - prc: 0.9206"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.5270 - cross entropy: 0.4133 - Brier score: 0.1437 - tp: 87819.0000 - fp: 50277.0000 - tn: 103902.0000 - fn: 9524.0000 - accuracy: 0.7622 - precision: 0.6359 - recall: 0.9022 - auc: 0.9309 - prc: 0.9216"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.5224 - cross entropy: 0.4124 - Brier score: 0.1433 - tp: 90606.0000 - fp: 51158.0000 - tn: 106089.0000 - fn: 9813.0000 - accuracy: 0.7634 - precision: 0.6391 - recall: 0.9023 - auc: 0.9309 - prc: 0.9225"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.5179 - cross entropy: 0.4114 - Brier score: 0.1428 - tp: 93365.0000 - fp: 51995.0000 - tn: 108350.0000 - fn: 10100.0000 - accuracy: 0.7646 - precision: 0.6423 - recall: 0.9024 - auc: 0.9311 - prc: 0.9233"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/278 [==========>...................] - ETA: 3s - loss: 0.5150 - cross entropy: 0.4108 - Brier score: 0.1425 - tp: 95215.0000 - fp: 52548.0000 - tn: 109850.0000 - fn: 10293.0000 - accuracy: 0.7654 - precision: 0.6444 - recall: 0.9024 - auc: 0.9312 - prc: 0.9240"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106/278 [==========>...................] - ETA: 3s - loss: 0.5106 - cross entropy: 0.4096 - Brier score: 0.1420 - tp: 98021.0000 - fp: 53362.0000 - tn: 112107.0000 - fn: 10560.0000 - accuracy: 0.7668 - precision: 0.6475 - recall: 0.9027 - auc: 0.9315 - prc: 0.9249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/278 [==========>...................] - ETA: 3s - loss: 0.5065 - cross entropy: 0.4085 - Brier score: 0.1416 - tp: 100774.0000 - fp: 54170.0000 - tn: 114403.0000 - fn: 10847.0000 - accuracy: 0.7680 - precision: 0.6504 - recall: 0.9028 - auc: 0.9317 - prc: 0.9258"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/278 [===========>..................] - ETA: 3s - loss: 0.5023 - cross entropy: 0.4073 - Brier score: 0.1410 - tp: 103601.0000 - fp: 54891.0000 - tn: 116724.0000 - fn: 11122.0000 - accuracy: 0.7695 - precision: 0.6537 - recall: 0.9031 - auc: 0.9319 - prc: 0.9266"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/278 [===========>..................] - ETA: 3s - loss: 0.4986 - cross entropy: 0.4063 - Brier score: 0.1406 - tp: 106347.0000 - fp: 55654.0000 - tn: 119061.0000 - fn: 11420.0000 - accuracy: 0.7707 - precision: 0.6565 - recall: 0.9030 - auc: 0.9320 - prc: 0.9273"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "118/278 [===========>..................] - ETA: 3s - loss: 0.4945 - cross entropy: 0.4049 - Brier score: 0.1400 - tp: 109159.0000 - fp: 56355.0000 - tn: 121411.0000 - fn: 11701.0000 - accuracy: 0.7721 - precision: 0.6595 - recall: 0.9032 - auc: 0.9323 - prc: 0.9281"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/278 [============>.................] - ETA: 3s - loss: 0.4908 - cross entropy: 0.4037 - Brier score: 0.1395 - tp: 111903.0000 - fp: 57066.0000 - tn: 123814.0000 - fn: 11987.0000 - accuracy: 0.7734 - precision: 0.6623 - recall: 0.9032 - auc: 0.9325 - prc: 0.9288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/278 [============>.................] - ETA: 3s - loss: 0.4869 - cross entropy: 0.4022 - Brier score: 0.1388 - tp: 114723.0000 - fp: 57693.0000 - tn: 126249.0000 - fn: 12249.0000 - accuracy: 0.7750 - precision: 0.6654 - recall: 0.9035 - auc: 0.9329 - prc: 0.9296"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/278 [============>.................] - ETA: 3s - loss: 0.4831 - cross entropy: 0.4008 - Brier score: 0.1382 - tp: 117590.0000 - fp: 58319.0000 - tn: 128615.0000 - fn: 12534.0000 - accuracy: 0.7765 - precision: 0.6685 - recall: 0.9037 - auc: 0.9332 - prc: 0.9304"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/278 [=============>................] - ETA: 2s - loss: 0.4796 - cross entropy: 0.3994 - Brier score: 0.1376 - tp: 120358.0000 - fp: 58965.0000 - tn: 131058.0000 - fn: 12821.0000 - accuracy: 0.7779 - precision: 0.6712 - recall: 0.9037 - auc: 0.9334 - prc: 0.9311"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/278 [=============>................] - ETA: 2s - loss: 0.4761 - cross entropy: 0.3981 - Brier score: 0.1370 - tp: 123163.0000 - fp: 59556.0000 - tn: 133515.0000 - fn: 13112.0000 - accuracy: 0.7794 - precision: 0.6741 - recall: 0.9038 - auc: 0.9337 - prc: 0.9318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "136/278 [=============>................] - ETA: 2s - loss: 0.4724 - cross entropy: 0.3964 - Brier score: 0.1363 - tp: 125984.0000 - fp: 60125.0000 - tn: 136016.0000 - fn: 13365.0000 - accuracy: 0.7809 - precision: 0.6769 - recall: 0.9041 - auc: 0.9341 - prc: 0.9326"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/278 [==============>...............] - ETA: 2s - loss: 0.4689 - cross entropy: 0.3948 - Brier score: 0.1357 - tp: 128823.0000 - fp: 60659.0000 - tn: 138508.0000 - fn: 13644.0000 - accuracy: 0.7825 - precision: 0.6799 - recall: 0.9042 - auc: 0.9345 - prc: 0.9333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/278 [==============>...............] - ETA: 2s - loss: 0.4656 - cross entropy: 0.3934 - Brier score: 0.1351 - tp: 131651.0000 - fp: 61224.0000 - tn: 140979.0000 - fn: 13924.0000 - accuracy: 0.7839 - precision: 0.6826 - recall: 0.9044 - auc: 0.9348 - prc: 0.9339"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/278 [==============>...............] - ETA: 2s - loss: 0.4624 - cross entropy: 0.3920 - Brier score: 0.1345 - tp: 134402.0000 - fp: 61814.0000 - tn: 143513.0000 - fn: 14193.0000 - accuracy: 0.7852 - precision: 0.6850 - recall: 0.9045 - auc: 0.9351 - prc: 0.9345"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "148/278 [==============>...............] - ETA: 2s - loss: 0.4592 - cross entropy: 0.3905 - Brier score: 0.1338 - tp: 137197.0000 - fp: 62332.0000 - tn: 146053.0000 - fn: 14484.0000 - accuracy: 0.7867 - precision: 0.6876 - recall: 0.9045 - auc: 0.9354 - prc: 0.9351"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/278 [===============>..............] - ETA: 2s - loss: 0.4561 - cross entropy: 0.3890 - Brier score: 0.1332 - tp: 139999.0000 - fp: 62849.0000 - tn: 148585.0000 - fn: 14777.0000 - accuracy: 0.7880 - precision: 0.6902 - recall: 0.9045 - auc: 0.9357 - prc: 0.9357"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/278 [===============>..............] - ETA: 2s - loss: 0.4529 - cross entropy: 0.3874 - Brier score: 0.1326 - tp: 142805.0000 - fp: 63370.0000 - tn: 151130.0000 - fn: 15049.0000 - accuracy: 0.7894 - precision: 0.6926 - recall: 0.9047 - auc: 0.9360 - prc: 0.9364"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/278 [===============>..............] - ETA: 2s - loss: 0.4499 - cross entropy: 0.3859 - Brier score: 0.1319 - tp: 145574.0000 - fp: 63835.0000 - tn: 153756.0000 - fn: 15333.0000 - accuracy: 0.7908 - precision: 0.6952 - recall: 0.9047 - auc: 0.9364 - prc: 0.9369"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/278 [================>.............] - ETA: 2s - loss: 0.4469 - cross entropy: 0.3844 - Brier score: 0.1313 - tp: 148313.0000 - fp: 64289.0000 - tn: 156432.0000 - fn: 15608.0000 - accuracy: 0.7923 - precision: 0.6976 - recall: 0.9048 - auc: 0.9367 - prc: 0.9375"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/278 [================>.............] - ETA: 2s - loss: 0.4440 - cross entropy: 0.3829 - Brier score: 0.1307 - tp: 151142.0000 - fp: 64771.0000 - tn: 158989.0000 - fn: 15884.0000 - accuracy: 0.7936 - precision: 0.7000 - recall: 0.9049 - auc: 0.9370 - prc: 0.9381"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "166/278 [================>.............] - ETA: 2s - loss: 0.4413 - cross entropy: 0.3815 - Brier score: 0.1301 - tp: 153938.0000 - fp: 65223.0000 - tn: 161597.0000 - fn: 16172.0000 - accuracy: 0.7949 - precision: 0.7024 - recall: 0.9049 - auc: 0.9373 - prc: 0.9386"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/278 [=================>............] - ETA: 2s - loss: 0.4384 - cross entropy: 0.3800 - Brier score: 0.1294 - tp: 156745.0000 - fp: 65666.0000 - tn: 164213.0000 - fn: 16450.0000 - accuracy: 0.7963 - precision: 0.7048 - recall: 0.9050 - auc: 0.9377 - prc: 0.9391"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172/278 [=================>............] - ETA: 2s - loss: 0.4357 - cross entropy: 0.3785 - Brier score: 0.1288 - tp: 159576.0000 - fp: 66132.0000 - tn: 166798.0000 - fn: 16712.0000 - accuracy: 0.7976 - precision: 0.7070 - recall: 0.9052 - auc: 0.9380 - prc: 0.9397"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/278 [=================>............] - ETA: 2s - loss: 0.4330 - cross entropy: 0.3770 - Brier score: 0.1282 - tp: 162364.0000 - fp: 66564.0000 - tn: 169446.0000 - fn: 16988.0000 - accuracy: 0.7988 - precision: 0.7092 - recall: 0.9053 - auc: 0.9383 - prc: 0.9402"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/278 [==================>...........] - ETA: 2s - loss: 0.4302 - cross entropy: 0.3754 - Brier score: 0.1276 - tp: 165116.0000 - fp: 66962.0000 - tn: 172182.0000 - fn: 17246.0000 - accuracy: 0.8002 - precision: 0.7115 - recall: 0.9054 - auc: 0.9387 - prc: 0.9407"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/278 [==================>...........] - ETA: 1s - loss: 0.4276 - cross entropy: 0.3739 - Brier score: 0.1269 - tp: 167848.0000 - fp: 67357.0000 - tn: 174908.0000 - fn: 17537.0000 - accuracy: 0.8015 - precision: 0.7136 - recall: 0.9054 - auc: 0.9390 - prc: 0.9412"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/278 [==================>...........] - ETA: 1s - loss: 0.4249 - cross entropy: 0.3724 - Brier score: 0.1263 - tp: 170685.0000 - fp: 67725.0000 - tn: 177563.0000 - fn: 17821.0000 - accuracy: 0.8028 - precision: 0.7159 - recall: 0.9055 - auc: 0.9394 - prc: 0.9417"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "187/278 [===================>..........] - ETA: 1s - loss: 0.4224 - cross entropy: 0.3709 - Brier score: 0.1257 - tp: 173478.0000 - fp: 68101.0000 - tn: 180282.0000 - fn: 18077.0000 - accuracy: 0.8041 - precision: 0.7181 - recall: 0.9056 - auc: 0.9397 - prc: 0.9422"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/278 [===================>..........] - ETA: 1s - loss: 0.4199 - cross entropy: 0.3694 - Brier score: 0.1251 - tp: 176320.0000 - fp: 68473.0000 - tn: 182928.0000 - fn: 18361.0000 - accuracy: 0.8053 - precision: 0.7203 - recall: 0.9057 - auc: 0.9400 - prc: 0.9426"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/278 [===================>..........] - ETA: 1s - loss: 0.4173 - cross entropy: 0.3678 - Brier score: 0.1245 - tp: 179134.0000 - fp: 68820.0000 - tn: 185654.0000 - fn: 18618.0000 - accuracy: 0.8066 - precision: 0.7224 - recall: 0.9059 - auc: 0.9404 - prc: 0.9432"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/278 [====================>.........] - ETA: 1s - loss: 0.4148 - cross entropy: 0.3663 - Brier score: 0.1238 - tp: 181995.0000 - fp: 69174.0000 - tn: 188311.0000 - fn: 18890.0000 - accuracy: 0.8079 - precision: 0.7246 - recall: 0.9060 - auc: 0.9408 - prc: 0.9436"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/278 [====================>.........] - ETA: 1s - loss: 0.4125 - cross entropy: 0.3650 - Brier score: 0.1233 - tp: 184762.0000 - fp: 69573.0000 - tn: 191019.0000 - fn: 19160.0000 - accuracy: 0.8090 - precision: 0.7265 - recall: 0.9060 - auc: 0.9411 - prc: 0.9440"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/278 [====================>.........] - ETA: 1s - loss: 0.4102 - cross entropy: 0.3635 - Brier score: 0.1227 - tp: 187534.0000 - fp: 69903.0000 - tn: 193809.0000 - fn: 19412.0000 - accuracy: 0.8102 - precision: 0.7285 - recall: 0.9062 - auc: 0.9414 - prc: 0.9445"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "205/278 [=====================>........] - ETA: 1s - loss: 0.4078 - cross entropy: 0.3620 - Brier score: 0.1221 - tp: 190327.0000 - fp: 70250.0000 - tn: 196551.0000 - fn: 19674.0000 - accuracy: 0.8114 - precision: 0.7304 - recall: 0.9063 - auc: 0.9418 - prc: 0.9449"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/278 [=====================>........] - ETA: 1s - loss: 0.4055 - cross entropy: 0.3606 - Brier score: 0.1215 - tp: 193166.0000 - fp: 70591.0000 - tn: 199256.0000 - fn: 19933.0000 - accuracy: 0.8126 - precision: 0.7324 - recall: 0.9065 - auc: 0.9421 - prc: 0.9454"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/278 [=====================>........] - ETA: 1s - loss: 0.4034 - cross entropy: 0.3593 - Brier score: 0.1210 - tp: 195867.0000 - fp: 70933.0000 - tn: 202077.0000 - fn: 20213.0000 - accuracy: 0.8136 - precision: 0.7341 - recall: 0.9065 - auc: 0.9424 - prc: 0.9458"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/278 [======================>.......] - ETA: 1s - loss: 0.4012 - cross entropy: 0.3579 - Brier score: 0.1204 - tp: 198716.0000 - fp: 71270.0000 - tn: 204776.0000 - fn: 20472.0000 - accuracy: 0.8148 - precision: 0.7360 - recall: 0.9066 - auc: 0.9428 - prc: 0.9462"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/278 [======================>.......] - ETA: 1s - loss: 0.3990 - cross entropy: 0.3565 - Brier score: 0.1199 - tp: 201514.0000 - fp: 71560.0000 - tn: 207551.0000 - fn: 20753.0000 - accuracy: 0.8159 - precision: 0.7379 - recall: 0.9066 - auc: 0.9431 - prc: 0.9466"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "220/278 [======================>.......] - ETA: 1s - loss: 0.3969 - cross entropy: 0.3551 - Brier score: 0.1193 - tp: 204370.0000 - fp: 71866.0000 - tn: 210286.0000 - fn: 21000.0000 - accuracy: 0.8170 - precision: 0.7398 - recall: 0.9068 - auc: 0.9434 - prc: 0.9470"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/278 [=======================>......] - ETA: 1s - loss: 0.3948 - cross entropy: 0.3538 - Brier score: 0.1188 - tp: 207126.0000 - fp: 72187.0000 - tn: 213076.0000 - fn: 21277.0000 - accuracy: 0.8180 - precision: 0.7416 - recall: 0.9068 - auc: 0.9437 - prc: 0.9474"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "226/278 [=======================>......] - ETA: 1s - loss: 0.3928 - cross entropy: 0.3525 - Brier score: 0.1183 - tp: 209889.0000 - fp: 72478.0000 - tn: 215883.0000 - fn: 21560.0000 - accuracy: 0.8191 - precision: 0.7433 - recall: 0.9068 - auc: 0.9440 - prc: 0.9478"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/278 [=======================>......] - ETA: 0s - loss: 0.3908 - cross entropy: 0.3511 - Brier score: 0.1177 - tp: 212701.0000 - fp: 72742.0000 - tn: 218677.0000 - fn: 21834.0000 - accuracy: 0.8202 - precision: 0.7452 - recall: 0.9069 - auc: 0.9443 - prc: 0.9481"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/278 [========================>.....] - ETA: 0s - loss: 0.3887 - cross entropy: 0.3498 - Brier score: 0.1172 - tp: 215469.0000 - fp: 73032.0000 - tn: 221500.0000 - fn: 22097.0000 - accuracy: 0.8212 - precision: 0.7469 - recall: 0.9070 - auc: 0.9446 - prc: 0.9485"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/278 [========================>.....] - ETA: 0s - loss: 0.3867 - cross entropy: 0.3484 - Brier score: 0.1166 - tp: 218303.0000 - fp: 73313.0000 - tn: 224272.0000 - fn: 22354.0000 - accuracy: 0.8223 - precision: 0.7486 - recall: 0.9071 - auc: 0.9449 - prc: 0.9489"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/278 [========================>.....] - ETA: 0s - loss: 0.3847 - cross entropy: 0.3471 - Brier score: 0.1161 - tp: 221128.0000 - fp: 73575.0000 - tn: 227076.0000 - fn: 22607.0000 - accuracy: 0.8233 - precision: 0.7503 - recall: 0.9072 - auc: 0.9453 - prc: 0.9493"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/278 [=========================>....] - ETA: 0s - loss: 0.3828 - cross entropy: 0.3458 - Brier score: 0.1156 - tp: 223994.0000 - fp: 73857.0000 - tn: 229796.0000 - fn: 22883.0000 - accuracy: 0.8243 - precision: 0.7520 - recall: 0.9073 - auc: 0.9455 - prc: 0.9496"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/278 [=========================>....] - ETA: 0s - loss: 0.3809 - cross entropy: 0.3445 - Brier score: 0.1151 - tp: 226810.0000 - fp: 74113.0000 - tn: 232599.0000 - fn: 23152.0000 - accuracy: 0.8253 - precision: 0.7537 - recall: 0.9074 - auc: 0.9458 - prc: 0.9500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/278 [=========================>....] - ETA: 0s - loss: 0.3790 - cross entropy: 0.3431 - Brier score: 0.1145 - tp: 229597.0000 - fp: 74386.0000 - tn: 235437.0000 - fn: 23398.0000 - accuracy: 0.8263 - precision: 0.7553 - recall: 0.9075 - auc: 0.9461 - prc: 0.9503"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "250/278 [=========================>....] - ETA: 0s - loss: 0.3773 - cross entropy: 0.3420 - Brier score: 0.1141 - tp: 232408.0000 - fp: 74681.0000 - tn: 238191.0000 - fn: 23682.0000 - accuracy: 0.8271 - precision: 0.7568 - recall: 0.9075 - auc: 0.9464 - prc: 0.9506"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/278 [==========================>...] - ETA: 0s - loss: 0.3754 - cross entropy: 0.3407 - Brier score: 0.1136 - tp: 235249.0000 - fp: 74915.0000 - tn: 241003.0000 - fn: 23939.0000 - accuracy: 0.8281 - precision: 0.7585 - recall: 0.9076 - auc: 0.9467 - prc: 0.9510"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/278 [==========================>...] - ETA: 0s - loss: 0.3736 - cross entropy: 0.3394 - Brier score: 0.1131 - tp: 238090.0000 - fp: 75175.0000 - tn: 243779.0000 - fn: 24206.0000 - accuracy: 0.8290 - precision: 0.7600 - recall: 0.9077 - auc: 0.9470 - prc: 0.9513"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "259/278 [==========================>...] - ETA: 0s - loss: 0.3719 - cross entropy: 0.3382 - Brier score: 0.1126 - tp: 240921.0000 - fp: 75417.0000 - tn: 246583.0000 - fn: 24473.0000 - accuracy: 0.8299 - precision: 0.7616 - recall: 0.9078 - auc: 0.9473 - prc: 0.9517"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "262/278 [===========================>..] - ETA: 0s - loss: 0.3701 - cross entropy: 0.3370 - Brier score: 0.1121 - tp: 243760.0000 - fp: 75674.0000 - tn: 249390.0000 - fn: 24714.0000 - accuracy: 0.8309 - precision: 0.7631 - recall: 0.9079 - auc: 0.9476 - prc: 0.9520"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "265/278 [===========================>..] - ETA: 0s - loss: 0.3684 - cross entropy: 0.3358 - Brier score: 0.1116 - tp: 246572.0000 - fp: 75910.0000 - tn: 252222.0000 - fn: 24978.0000 - accuracy: 0.8318 - precision: 0.7646 - recall: 0.9080 - auc: 0.9478 - prc: 0.9523"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "268/278 [===========================>..] - ETA: 0s - loss: 0.3668 - cross entropy: 0.3346 - Brier score: 0.1112 - tp: 249366.0000 - fp: 76183.0000 - tn: 255030.0000 - fn: 25247.0000 - accuracy: 0.8326 - precision: 0.7660 - recall: 0.9081 - auc: 0.9481 - prc: 0.9526"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "271/278 [============================>.] - ETA: 0s - loss: 0.3651 - cross entropy: 0.3334 - Brier score: 0.1107 - tp: 252226.0000 - fp: 76416.0000 - tn: 257837.0000 - fn: 25491.0000 - accuracy: 0.8335 - precision: 0.7675 - recall: 0.9082 - auc: 0.9484 - prc: 0.9529"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "274/278 [============================>.] - ETA: 0s - loss: 0.3634 - cross entropy: 0.3322 - Brier score: 0.1102 - tp: 255013.0000 - fp: 76648.0000 - tn: 260703.0000 - fn: 25750.0000 - accuracy: 0.8343 - precision: 0.7689 - recall: 0.9083 - auc: 0.9487 - prc: 0.9532"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "277/278 [============================>.] - ETA: 0s - loss: 0.3618 - cross entropy: 0.3310 - Brier score: 0.1097 - tp: 257809.0000 - fp: 76881.0000 - tn: 263576.0000 - fn: 25992.0000 - accuracy: 0.8352 - precision: 0.7703 - recall: 0.9084 - auc: 0.9490 - prc: 0.9535"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 8s 22ms/step - loss: 0.3612 - cross entropy: 0.3306 - Brier score: 0.1096 - tp: 258749.0000 - fp: 76958.0000 - tn: 264513.0000 - fn: 26086.0000 - accuracy: 0.8355 - precision: 0.7708 - recall: 0.9084 - auc: 0.9490 - prc: 0.9536 - val_loss: 0.2021 - val_cross entropy: 0.2021 - val_Brier score: 0.0446 - val_tp: 75.0000 - val_fp: 1144.0000 - val_tn: 44343.0000 - val_fn: 7.0000 - val_accuracy: 0.9747 - val_precision: 0.0615 - val_recall: 0.9146 - val_auc: 0.9741 - val_prc: 0.7919\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.2209 - cross entropy: 0.2209 - Brier score: 0.0649 - tp: 944.0000 - fp: 95.0000 - tn: 945.0000 - fn: 64.0000 - accuracy: 0.9224 - precision: 0.9086 - recall: 0.9365 - auc: 0.9744 - prc: 0.9796"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.2112 - cross entropy: 0.2112 - Brier score: 0.0629 - tp: 4757.0000 - fp: 418.0000 - tn: 4675.0000 - fn: 390.0000 - accuracy: 0.9211 - precision: 0.9192 - recall: 0.9242 - auc: 0.9749 - prc: 0.9805"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.2106 - cross entropy: 0.2106 - Brier score: 0.0628 - tp: 7620.0000 - fp: 641.0000 - tn: 7462.0000 - fn: 661.0000 - accuracy: 0.9205 - precision: 0.9224 - recall: 0.9202 - auc: 0.9747 - prc: 0.9806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.2095 - cross entropy: 0.2095 - Brier score: 0.0625 - tp: 10436.0000 - fp: 866.0000 - tn: 10315.0000 - fn: 911.0000 - accuracy: 0.9211 - precision: 0.9234 - recall: 0.9197 - auc: 0.9750 - prc: 0.9808"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.2114 - cross entropy: 0.2114 - Brier score: 0.0629 - tp: 13229.0000 - fp: 1123.0000 - tn: 13161.0000 - fn: 1159.0000 - accuracy: 0.9204 - precision: 0.9218 - recall: 0.9194 - auc: 0.9746 - prc: 0.9803"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.2114 - cross entropy: 0.2114 - Brier score: 0.0629 - tp: 16019.0000 - fp: 1341.0000 - tn: 16039.0000 - fn: 1417.0000 - accuracy: 0.9208 - precision: 0.9228 - recall: 0.9187 - auc: 0.9744 - prc: 0.9801"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.2101 - cross entropy: 0.2101 - Brier score: 0.0625 - tp: 18849.0000 - fp: 1568.0000 - tn: 18884.0000 - fn: 1659.0000 - accuracy: 0.9212 - precision: 0.9232 - recall: 0.9191 - auc: 0.9747 - prc: 0.9804"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.2090 - cross entropy: 0.2090 - Brier score: 0.0621 - tp: 21695.0000 - fp: 1768.0000 - tn: 21720.0000 - fn: 1921.0000 - accuracy: 0.9217 - precision: 0.9246 - recall: 0.9187 - auc: 0.9750 - prc: 0.9806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.2088 - cross entropy: 0.2088 - Brier score: 0.0620 - tp: 24467.0000 - fp: 1994.0000 - tn: 24623.0000 - fn: 2164.0000 - accuracy: 0.9219 - precision: 0.9246 - recall: 0.9187 - auc: 0.9750 - prc: 0.9805"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.2081 - cross entropy: 0.2081 - Brier score: 0.0618 - tp: 27284.0000 - fp: 2201.0000 - tn: 27481.0000 - fn: 2426.0000 - accuracy: 0.9221 - precision: 0.9254 - recall: 0.9183 - auc: 0.9751 - prc: 0.9806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.2077 - cross entropy: 0.2077 - Brier score: 0.0617 - tp: 30126.0000 - fp: 2423.0000 - tn: 30320.0000 - fn: 2667.0000 - accuracy: 0.9223 - precision: 0.9256 - recall: 0.9187 - auc: 0.9753 - prc: 0.9807"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.2067 - cross entropy: 0.2067 - Brier score: 0.0614 - tp: 32893.0000 - fp: 2640.0000 - tn: 33234.0000 - fn: 2913.0000 - accuracy: 0.9225 - precision: 0.9257 - recall: 0.9186 - auc: 0.9756 - prc: 0.9809"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.2059 - cross entropy: 0.2059 - Brier score: 0.0611 - tp: 35769.0000 - fp: 2849.0000 - tn: 36065.0000 - fn: 3141.0000 - accuracy: 0.9230 - precision: 0.9262 - recall: 0.9193 - auc: 0.9758 - prc: 0.9811"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.2051 - cross entropy: 0.2051 - Brier score: 0.0609 - tp: 38599.0000 - fp: 3051.0000 - tn: 38920.0000 - fn: 3398.0000 - accuracy: 0.9232 - precision: 0.9267 - recall: 0.9191 - auc: 0.9760 - prc: 0.9812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.2049 - cross entropy: 0.2049 - Brier score: 0.0608 - tp: 41431.0000 - fp: 3268.0000 - tn: 41761.0000 - fn: 3652.0000 - accuracy: 0.9232 - precision: 0.9269 - recall: 0.9190 - auc: 0.9759 - prc: 0.9812"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.2044 - cross entropy: 0.2044 - Brier score: 0.0606 - tp: 44213.0000 - fp: 3467.0000 - tn: 44684.0000 - fn: 3892.0000 - accuracy: 0.9235 - precision: 0.9273 - recall: 0.9191 - auc: 0.9761 - prc: 0.9813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.2038 - cross entropy: 0.2038 - Brier score: 0.0605 - tp: 47039.0000 - fp: 3677.0000 - tn: 47549.0000 - fn: 4135.0000 - accuracy: 0.9237 - precision: 0.9275 - recall: 0.9192 - auc: 0.9762 - prc: 0.9814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.2034 - cross entropy: 0.2034 - Brier score: 0.0603 - tp: 49802.0000 - fp: 3896.0000 - tn: 50448.0000 - fn: 4398.0000 - accuracy: 0.9236 - precision: 0.9274 - recall: 0.9189 - auc: 0.9762 - prc: 0.9814"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.2027 - cross entropy: 0.2027 - Brier score: 0.0601 - tp: 52602.0000 - fp: 4079.0000 - tn: 53364.0000 - fn: 4643.0000 - accuracy: 0.9240 - precision: 0.9280 - recall: 0.9189 - auc: 0.9764 - prc: 0.9815"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.2022 - cross entropy: 0.2022 - Brier score: 0.0599 - tp: 55405.0000 - fp: 4287.0000 - tn: 56237.0000 - fn: 4903.0000 - accuracy: 0.9239 - precision: 0.9282 - recall: 0.9187 - auc: 0.9765 - prc: 0.9815"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.2020 - cross entropy: 0.2020 - Brier score: 0.0598 - tp: 58259.0000 - fp: 4495.0000 - tn: 59082.0000 - fn: 5140.0000 - accuracy: 0.9241 - precision: 0.9284 - recall: 0.9189 - auc: 0.9765 - prc: 0.9816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.2016 - cross entropy: 0.2016 - Brier score: 0.0597 - tp: 61061.0000 - fp: 4679.0000 - tn: 62005.0000 - fn: 5375.0000 - accuracy: 0.9245 - precision: 0.9288 - recall: 0.9191 - auc: 0.9766 - prc: 0.9816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 3s - loss: 0.2014 - cross entropy: 0.2014 - Brier score: 0.0596 - tp: 63865.0000 - fp: 4879.0000 - tn: 64901.0000 - fn: 5619.0000 - accuracy: 0.9246 - precision: 0.9290 - recall: 0.9191 - auc: 0.9766 - prc: 0.9816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 3s - loss: 0.2013 - cross entropy: 0.2013 - Brier score: 0.0595 - tp: 66667.0000 - fp: 5083.0000 - tn: 67794.0000 - fn: 5864.0000 - accuracy: 0.9247 - precision: 0.9292 - recall: 0.9192 - auc: 0.9766 - prc: 0.9816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.2010 - cross entropy: 0.2010 - Brier score: 0.0594 - tp: 69421.0000 - fp: 5275.0000 - tn: 70729.0000 - fn: 6127.0000 - accuracy: 0.9248 - precision: 0.9294 - recall: 0.9189 - auc: 0.9766 - prc: 0.9816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.2003 - cross entropy: 0.2003 - Brier score: 0.0592 - tp: 72287.0000 - fp: 5465.0000 - tn: 73581.0000 - fn: 6363.0000 - accuracy: 0.9250 - precision: 0.9297 - recall: 0.9191 - auc: 0.9768 - prc: 0.9817"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.2000 - cross entropy: 0.2000 - Brier score: 0.0591 - tp: 75083.0000 - fp: 5673.0000 - tn: 76480.0000 - fn: 6604.0000 - accuracy: 0.9251 - precision: 0.9298 - recall: 0.9192 - auc: 0.9768 - prc: 0.9817"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.1993 - cross entropy: 0.1993 - Brier score: 0.0589 - tp: 77973.0000 - fp: 5841.0000 - tn: 79323.0000 - fn: 6847.0000 - accuracy: 0.9254 - precision: 0.9303 - recall: 0.9193 - auc: 0.9769 - prc: 0.9818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.1991 - cross entropy: 0.1991 - Brier score: 0.0588 - tp: 80772.0000 - fp: 6013.0000 - tn: 82248.0000 - fn: 7095.0000 - accuracy: 0.9256 - precision: 0.9307 - recall: 0.9193 - auc: 0.9770 - prc: 0.9818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.1987 - cross entropy: 0.1987 - Brier score: 0.0586 - tp: 83529.0000 - fp: 6201.0000 - tn: 85210.0000 - fn: 7332.0000 - accuracy: 0.9258 - precision: 0.9309 - recall: 0.9193 - auc: 0.9770 - prc: 0.9818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.1981 - cross entropy: 0.1981 - Brier score: 0.0584 - tp: 86407.0000 - fp: 6384.0000 - tn: 88056.0000 - fn: 7569.0000 - accuracy: 0.9259 - precision: 0.9312 - recall: 0.9195 - auc: 0.9772 - prc: 0.9820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.1980 - cross entropy: 0.1980 - Brier score: 0.0584 - tp: 89154.0000 - fp: 6576.0000 - tn: 90995.0000 - fn: 7835.0000 - accuracy: 0.9259 - precision: 0.9313 - recall: 0.9192 - auc: 0.9772 - prc: 0.9819"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.1975 - cross entropy: 0.1975 - Brier score: 0.0582 - tp: 92044.0000 - fp: 6771.0000 - tn: 93847.0000 - fn: 8042.0000 - accuracy: 0.9262 - precision: 0.9315 - recall: 0.9196 - auc: 0.9773 - prc: 0.9820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.1970 - cross entropy: 0.1970 - Brier score: 0.0581 - tp: 94857.0000 - fp: 6959.0000 - tn: 96743.0000 - fn: 8289.0000 - accuracy: 0.9263 - precision: 0.9317 - recall: 0.9196 - auc: 0.9774 - prc: 0.9821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/278 [==========>...................] - ETA: 3s - loss: 0.1965 - cross entropy: 0.1965 - Brier score: 0.0579 - tp: 97723.0000 - fp: 7122.0000 - tn: 99622.0000 - fn: 8525.0000 - accuracy: 0.9265 - precision: 0.9321 - recall: 0.9198 - auc: 0.9775 - prc: 0.9822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/278 [==========>...................] - ETA: 3s - loss: 0.1960 - cross entropy: 0.1960 - Brier score: 0.0578 - tp: 100523.0000 - fp: 7292.0000 - tn: 102542.0000 - fn: 8779.0000 - accuracy: 0.9267 - precision: 0.9324 - recall: 0.9197 - auc: 0.9776 - prc: 0.9822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/278 [==========>...................] - ETA: 3s - loss: 0.1957 - cross entropy: 0.1957 - Brier score: 0.0577 - tp: 103355.0000 - fp: 7462.0000 - tn: 105449.0000 - fn: 9014.0000 - accuracy: 0.9269 - precision: 0.9327 - recall: 0.9198 - auc: 0.9777 - prc: 0.9823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/278 [===========>..................] - ETA: 3s - loss: 0.1952 - cross entropy: 0.1952 - Brier score: 0.0575 - tp: 106199.0000 - fp: 7626.0000 - tn: 108349.0000 - fn: 9250.0000 - accuracy: 0.9271 - precision: 0.9330 - recall: 0.9199 - auc: 0.9778 - prc: 0.9823"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/278 [===========>..................] - ETA: 3s - loss: 0.1947 - cross entropy: 0.1947 - Brier score: 0.0573 - tp: 109082.0000 - fp: 7783.0000 - tn: 111215.0000 - fn: 9488.0000 - accuracy: 0.9273 - precision: 0.9334 - recall: 0.9200 - auc: 0.9778 - prc: 0.9824"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/278 [===========>..................] - ETA: 3s - loss: 0.1944 - cross entropy: 0.1944 - Brier score: 0.0572 - tp: 111842.0000 - fp: 7954.0000 - tn: 114188.0000 - fn: 9728.0000 - accuracy: 0.9274 - precision: 0.9336 - recall: 0.9200 - auc: 0.9779 - prc: 0.9825"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/278 [============>.................] - ETA: 2s - loss: 0.1939 - cross entropy: 0.1939 - Brier score: 0.0570 - tp: 114596.0000 - fp: 8132.0000 - tn: 117182.0000 - fn: 9946.0000 - accuracy: 0.9276 - precision: 0.9337 - recall: 0.9201 - auc: 0.9781 - prc: 0.9825"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/278 [============>.................] - ETA: 2s - loss: 0.1934 - cross entropy: 0.1934 - Brier score: 0.0569 - tp: 117385.0000 - fp: 8311.0000 - tn: 120118.0000 - fn: 10186.0000 - accuracy: 0.9277 - precision: 0.9339 - recall: 0.9202 - auc: 0.9782 - prc: 0.9826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/278 [============>.................] - ETA: 2s - loss: 0.1930 - cross entropy: 0.1930 - Brier score: 0.0568 - tp: 120273.0000 - fp: 8474.0000 - tn: 122982.0000 - fn: 10415.0000 - accuracy: 0.9279 - precision: 0.9342 - recall: 0.9203 - auc: 0.9782 - prc: 0.9827"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/278 [=============>................] - ETA: 2s - loss: 0.1925 - cross entropy: 0.1925 - Brier score: 0.0566 - tp: 123187.0000 - fp: 8623.0000 - tn: 125857.0000 - fn: 10621.0000 - accuracy: 0.9283 - precision: 0.9346 - recall: 0.9206 - auc: 0.9784 - prc: 0.9828"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/278 [=============>................] - ETA: 2s - loss: 0.1920 - cross entropy: 0.1920 - Brier score: 0.0565 - tp: 126057.0000 - fp: 8796.0000 - tn: 128736.0000 - fn: 10843.0000 - accuracy: 0.9284 - precision: 0.9348 - recall: 0.9208 - auc: 0.9785 - prc: 0.9828"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/278 [=============>................] - ETA: 2s - loss: 0.1917 - cross entropy: 0.1917 - Brier score: 0.0564 - tp: 128878.0000 - fp: 8974.0000 - tn: 131660.0000 - fn: 11064.0000 - accuracy: 0.9286 - precision: 0.9349 - recall: 0.9209 - auc: 0.9786 - prc: 0.9829"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/278 [==============>...............] - ETA: 2s - loss: 0.1913 - cross entropy: 0.1913 - Brier score: 0.0563 - tp: 131745.0000 - fp: 9142.0000 - tn: 134532.0000 - fn: 11301.0000 - accuracy: 0.9287 - precision: 0.9351 - recall: 0.9210 - auc: 0.9786 - prc: 0.9829"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/278 [==============>...............] - ETA: 2s - loss: 0.1909 - cross entropy: 0.1909 - Brier score: 0.0561 - tp: 134603.0000 - fp: 9319.0000 - tn: 137423.0000 - fn: 11519.0000 - accuracy: 0.9288 - precision: 0.9352 - recall: 0.9212 - auc: 0.9787 - prc: 0.9830"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/278 [==============>...............] - ETA: 2s - loss: 0.1904 - cross entropy: 0.1904 - Brier score: 0.0560 - tp: 137470.0000 - fp: 9468.0000 - tn: 140333.0000 - fn: 11737.0000 - accuracy: 0.9291 - precision: 0.9356 - recall: 0.9213 - auc: 0.9788 - prc: 0.9831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/278 [===============>..............] - ETA: 2s - loss: 0.1899 - cross entropy: 0.1899 - Brier score: 0.0558 - tp: 140305.0000 - fp: 9615.0000 - tn: 143263.0000 - fn: 11969.0000 - accuracy: 0.9293 - precision: 0.9359 - recall: 0.9214 - auc: 0.9789 - prc: 0.9832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/278 [===============>..............] - ETA: 2s - loss: 0.1894 - cross entropy: 0.1894 - Brier score: 0.0557 - tp: 143170.0000 - fp: 9764.0000 - tn: 146171.0000 - fn: 12191.0000 - accuracy: 0.9295 - precision: 0.9362 - recall: 0.9215 - auc: 0.9790 - prc: 0.9832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/278 [===============>..............] - ETA: 2s - loss: 0.1890 - cross entropy: 0.1890 - Brier score: 0.0555 - tp: 146096.0000 - fp: 9920.0000 - tn: 149005.0000 - fn: 12419.0000 - accuracy: 0.9296 - precision: 0.9364 - recall: 0.9217 - auc: 0.9791 - prc: 0.9833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/278 [================>.............] - ETA: 2s - loss: 0.1885 - cross entropy: 0.1885 - Brier score: 0.0554 - tp: 148928.0000 - fp: 10070.0000 - tn: 151931.0000 - fn: 12655.0000 - accuracy: 0.9298 - precision: 0.9367 - recall: 0.9217 - auc: 0.9792 - prc: 0.9834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/278 [================>.............] - ETA: 2s - loss: 0.1881 - cross entropy: 0.1881 - Brier score: 0.0553 - tp: 151817.0000 - fp: 10221.0000 - tn: 154814.0000 - fn: 12876.0000 - accuracy: 0.9300 - precision: 0.9369 - recall: 0.9218 - auc: 0.9792 - prc: 0.9834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/278 [================>.............] - ETA: 2s - loss: 0.1878 - cross entropy: 0.1878 - Brier score: 0.0552 - tp: 154658.0000 - fp: 10372.0000 - tn: 157741.0000 - fn: 13101.0000 - accuracy: 0.9301 - precision: 0.9372 - recall: 0.9219 - auc: 0.9793 - prc: 0.9835"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/278 [=================>............] - ETA: 2s - loss: 0.1874 - cross entropy: 0.1874 - Brier score: 0.0550 - tp: 157508.0000 - fp: 10517.0000 - tn: 160676.0000 - fn: 13315.0000 - accuracy: 0.9303 - precision: 0.9374 - recall: 0.9221 - auc: 0.9794 - prc: 0.9836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/278 [=================>............] - ETA: 2s - loss: 0.1871 - cross entropy: 0.1871 - Brier score: 0.0549 - tp: 160379.0000 - fp: 10688.0000 - tn: 163568.0000 - fn: 13525.0000 - accuracy: 0.9305 - precision: 0.9375 - recall: 0.9222 - auc: 0.9795 - prc: 0.9836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/278 [=================>............] - ETA: 2s - loss: 0.1868 - cross entropy: 0.1868 - Brier score: 0.0548 - tp: 163196.0000 - fp: 10871.0000 - tn: 166480.0000 - fn: 13757.0000 - accuracy: 0.9305 - precision: 0.9375 - recall: 0.9223 - auc: 0.9795 - prc: 0.9836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/278 [=================>............] - ETA: 1s - loss: 0.1865 - cross entropy: 0.1865 - Brier score: 0.0547 - tp: 166092.0000 - fp: 11037.0000 - tn: 169353.0000 - fn: 13966.0000 - accuracy: 0.9306 - precision: 0.9377 - recall: 0.9224 - auc: 0.9796 - prc: 0.9837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/278 [==================>...........] - ETA: 1s - loss: 0.1860 - cross entropy: 0.1860 - Brier score: 0.0546 - tp: 168976.0000 - fp: 11177.0000 - tn: 172241.0000 - fn: 14198.0000 - accuracy: 0.9308 - precision: 0.9380 - recall: 0.9225 - auc: 0.9797 - prc: 0.9838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/278 [==================>...........] - ETA: 1s - loss: 0.1856 - cross entropy: 0.1856 - Brier score: 0.0545 - tp: 171824.0000 - fp: 11332.0000 - tn: 175150.0000 - fn: 14430.0000 - accuracy: 0.9309 - precision: 0.9381 - recall: 0.9225 - auc: 0.9798 - prc: 0.9838"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/278 [==================>...........] - ETA: 1s - loss: 0.1854 - cross entropy: 0.1854 - Brier score: 0.0544 - tp: 174708.0000 - fp: 11492.0000 - tn: 178034.0000 - fn: 14646.0000 - accuracy: 0.9310 - precision: 0.9383 - recall: 0.9227 - auc: 0.9798 - prc: 0.9839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/278 [===================>..........] - ETA: 1s - loss: 0.1850 - cross entropy: 0.1850 - Brier score: 0.0543 - tp: 177606.0000 - fp: 11639.0000 - tn: 180904.0000 - fn: 14875.0000 - accuracy: 0.9311 - precision: 0.9385 - recall: 0.9227 - auc: 0.9799 - prc: 0.9839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/278 [===================>..........] - ETA: 1s - loss: 0.1847 - cross entropy: 0.1847 - Brier score: 0.0542 - tp: 180477.0000 - fp: 11817.0000 - tn: 183780.0000 - fn: 15094.0000 - accuracy: 0.9312 - precision: 0.9385 - recall: 0.9228 - auc: 0.9800 - prc: 0.9840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/278 [===================>..........] - ETA: 1s - loss: 0.1844 - cross entropy: 0.1844 - Brier score: 0.0541 - tp: 183242.0000 - fp: 11963.0000 - tn: 186773.0000 - fn: 15334.0000 - accuracy: 0.9313 - precision: 0.9387 - recall: 0.9228 - auc: 0.9800 - prc: 0.9840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/278 [====================>.........] - ETA: 1s - loss: 0.1841 - cross entropy: 0.1841 - Brier score: 0.0540 - tp: 186110.0000 - fp: 12122.0000 - tn: 189673.0000 - fn: 15551.0000 - accuracy: 0.9314 - precision: 0.9388 - recall: 0.9229 - auc: 0.9801 - prc: 0.9840"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/278 [====================>.........] - ETA: 1s - loss: 0.1837 - cross entropy: 0.1837 - Brier score: 0.0539 - tp: 188947.0000 - fp: 12257.0000 - tn: 192630.0000 - fn: 15766.0000 - accuracy: 0.9316 - precision: 0.9391 - recall: 0.9230 - auc: 0.9802 - prc: 0.9841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/278 [====================>.........] - ETA: 1s - loss: 0.1833 - cross entropy: 0.1833 - Brier score: 0.0538 - tp: 191723.0000 - fp: 12378.0000 - tn: 195652.0000 - fn: 15991.0000 - accuracy: 0.9318 - precision: 0.9394 - recall: 0.9230 - auc: 0.9802 - prc: 0.9841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/278 [=====================>........] - ETA: 1s - loss: 0.1829 - cross entropy: 0.1829 - Brier score: 0.0537 - tp: 194604.0000 - fp: 12519.0000 - tn: 198573.0000 - fn: 16192.0000 - accuracy: 0.9319 - precision: 0.9396 - recall: 0.9232 - auc: 0.9803 - prc: 0.9842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/278 [=====================>........] - ETA: 1s - loss: 0.1826 - cross entropy: 0.1826 - Brier score: 0.0536 - tp: 197468.0000 - fp: 12666.0000 - tn: 201478.0000 - fn: 16420.0000 - accuracy: 0.9320 - precision: 0.9397 - recall: 0.9232 - auc: 0.9804 - prc: 0.9842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/278 [=====================>........] - ETA: 1s - loss: 0.1823 - cross entropy: 0.1823 - Brier score: 0.0535 - tp: 200298.0000 - fp: 12793.0000 - tn: 204430.0000 - fn: 16655.0000 - accuracy: 0.9322 - precision: 0.9400 - recall: 0.9232 - auc: 0.9805 - prc: 0.9843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "215/278 [======================>.......] - ETA: 1s - loss: 0.1819 - cross entropy: 0.1819 - Brier score: 0.0534 - tp: 203208.0000 - fp: 12925.0000 - tn: 207300.0000 - fn: 16887.0000 - accuracy: 0.9323 - precision: 0.9402 - recall: 0.9233 - auc: 0.9805 - prc: 0.9843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/278 [======================>.......] - ETA: 1s - loss: 0.1816 - cross entropy: 0.1816 - Brier score: 0.0533 - tp: 206007.0000 - fp: 13045.0000 - tn: 210299.0000 - fn: 17113.0000 - accuracy: 0.9325 - precision: 0.9404 - recall: 0.9233 - auc: 0.9806 - prc: 0.9844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/278 [======================>.......] - ETA: 1s - loss: 0.1812 - cross entropy: 0.1812 - Brier score: 0.0532 - tp: 208818.0000 - fp: 13183.0000 - tn: 213278.0000 - fn: 17329.0000 - accuracy: 0.9326 - precision: 0.9406 - recall: 0.9234 - auc: 0.9807 - prc: 0.9844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/278 [=======================>......] - ETA: 1s - loss: 0.1810 - cross entropy: 0.1810 - Brier score: 0.0531 - tp: 211678.0000 - fp: 13336.0000 - tn: 216199.0000 - fn: 17539.0000 - accuracy: 0.9327 - precision: 0.9407 - recall: 0.9235 - auc: 0.9807 - prc: 0.9845"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/278 [=======================>......] - ETA: 0s - loss: 0.1807 - cross entropy: 0.1807 - Brier score: 0.0530 - tp: 214534.0000 - fp: 13486.0000 - tn: 219118.0000 - fn: 17758.0000 - accuracy: 0.9328 - precision: 0.9409 - recall: 0.9236 - auc: 0.9808 - prc: 0.9845"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/278 [=======================>......] - ETA: 0s - loss: 0.1804 - cross entropy: 0.1804 - Brier score: 0.0529 - tp: 217334.0000 - fp: 13629.0000 - tn: 222120.0000 - fn: 17957.0000 - accuracy: 0.9329 - precision: 0.9410 - recall: 0.9237 - auc: 0.9808 - prc: 0.9845"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/278 [========================>.....] - ETA: 0s - loss: 0.1801 - cross entropy: 0.1801 - Brier score: 0.0528 - tp: 220158.0000 - fp: 13794.0000 - tn: 225051.0000 - fn: 18181.0000 - accuracy: 0.9330 - precision: 0.9410 - recall: 0.9237 - auc: 0.9809 - prc: 0.9846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/278 [========================>.....] - ETA: 0s - loss: 0.1798 - cross entropy: 0.1798 - Brier score: 0.0527 - tp: 222981.0000 - fp: 13942.0000 - tn: 228012.0000 - fn: 18393.0000 - accuracy: 0.9331 - precision: 0.9412 - recall: 0.9238 - auc: 0.9809 - prc: 0.9846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/278 [========================>.....] - ETA: 0s - loss: 0.1795 - cross entropy: 0.1795 - Brier score: 0.0526 - tp: 225806.0000 - fp: 14077.0000 - tn: 230969.0000 - fn: 18620.0000 - accuracy: 0.9332 - precision: 0.9413 - recall: 0.9238 - auc: 0.9810 - prc: 0.9846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/278 [=========================>....] - ETA: 0s - loss: 0.1792 - cross entropy: 0.1792 - Brier score: 0.0525 - tp: 228657.0000 - fp: 14219.0000 - tn: 233921.0000 - fn: 18819.0000 - accuracy: 0.9333 - precision: 0.9415 - recall: 0.9240 - auc: 0.9810 - prc: 0.9847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/278 [=========================>....] - ETA: 0s - loss: 0.1789 - cross entropy: 0.1789 - Brier score: 0.0524 - tp: 231471.0000 - fp: 14363.0000 - tn: 236877.0000 - fn: 19049.0000 - accuracy: 0.9334 - precision: 0.9416 - recall: 0.9240 - auc: 0.9811 - prc: 0.9847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/278 [=========================>....] - ETA: 0s - loss: 0.1787 - cross entropy: 0.1787 - Brier score: 0.0524 - tp: 234305.0000 - fp: 14506.0000 - tn: 239813.0000 - fn: 19280.0000 - accuracy: 0.9335 - precision: 0.9417 - recall: 0.9240 - auc: 0.9811 - prc: 0.9848"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/278 [==========================>...] - ETA: 0s - loss: 0.1784 - cross entropy: 0.1784 - Brier score: 0.0523 - tp: 237179.0000 - fp: 14641.0000 - tn: 242711.0000 - fn: 19517.0000 - accuracy: 0.9336 - precision: 0.9419 - recall: 0.9240 - auc: 0.9812 - prc: 0.9848"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/278 [==========================>...] - ETA: 0s - loss: 0.1781 - cross entropy: 0.1781 - Brier score: 0.0522 - tp: 240013.0000 - fp: 14785.0000 - tn: 245663.0000 - fn: 19731.0000 - accuracy: 0.9336 - precision: 0.9420 - recall: 0.9240 - auc: 0.9812 - prc: 0.9848"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/278 [==========================>...] - ETA: 0s - loss: 0.1778 - cross entropy: 0.1778 - Brier score: 0.0521 - tp: 242885.0000 - fp: 14907.0000 - tn: 248607.0000 - fn: 19937.0000 - accuracy: 0.9338 - precision: 0.9422 - recall: 0.9241 - auc: 0.9813 - prc: 0.9849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "260/278 [===========================>..] - ETA: 0s - loss: 0.1775 - cross entropy: 0.1775 - Brier score: 0.0520 - tp: 245816.0000 - fp: 15055.0000 - tn: 251467.0000 - fn: 20142.0000 - accuracy: 0.9339 - precision: 0.9423 - recall: 0.9243 - auc: 0.9814 - prc: 0.9849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/278 [===========================>..] - ETA: 0s - loss: 0.1772 - cross entropy: 0.1772 - Brier score: 0.0519 - tp: 248643.0000 - fp: 15199.0000 - tn: 254430.0000 - fn: 20352.0000 - accuracy: 0.9340 - precision: 0.9424 - recall: 0.9243 - auc: 0.9814 - prc: 0.9849"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "266/278 [===========================>..] - ETA: 0s - loss: 0.1769 - cross entropy: 0.1769 - Brier score: 0.0518 - tp: 251483.0000 - fp: 15332.0000 - tn: 257403.0000 - fn: 20550.0000 - accuracy: 0.9341 - precision: 0.9425 - recall: 0.9245 - auc: 0.9815 - prc: 0.9850"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "269/278 [============================>.] - ETA: 0s - loss: 0.1766 - cross entropy: 0.1766 - Brier score: 0.0517 - tp: 254389.0000 - fp: 15463.0000 - tn: 260310.0000 - fn: 20750.0000 - accuracy: 0.9343 - precision: 0.9427 - recall: 0.9246 - auc: 0.9815 - prc: 0.9850"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "272/278 [============================>.] - ETA: 0s - loss: 0.1763 - cross entropy: 0.1763 - Brier score: 0.0516 - tp: 257239.0000 - fp: 15598.0000 - tn: 263256.0000 - fn: 20963.0000 - accuracy: 0.9344 - precision: 0.9428 - recall: 0.9246 - auc: 0.9816 - prc: 0.9851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "275/278 [============================>.] - ETA: 0s - loss: 0.1760 - cross entropy: 0.1760 - Brier score: 0.0516 - tp: 260104.0000 - fp: 15742.0000 - tn: 266188.0000 - fn: 21166.0000 - accuracy: 0.9345 - precision: 0.9429 - recall: 0.9247 - auc: 0.9816 - prc: 0.9851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - ETA: 0s - loss: 0.1757 - cross entropy: 0.1757 - Brier score: 0.0515 - tp: 262968.0000 - fp: 15885.0000 - tn: 269124.0000 - fn: 21367.0000 - accuracy: 0.9346 - precision: 0.9430 - recall: 0.9249 - auc: 0.9817 - prc: 0.9852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 5s 20ms/step - loss: 0.1757 - cross entropy: 0.1757 - Brier score: 0.0515 - tp: 262968.0000 - fp: 15885.0000 - tn: 269124.0000 - fn: 21367.0000 - accuracy: 0.9346 - precision: 0.9430 - recall: 0.9249 - auc: 0.9817 - prc: 0.9852 - val_loss: 0.1003 - val_cross entropy: 0.1003 - val_Brier score: 0.0205 - val_tp: 76.0000 - val_fp: 858.0000 - val_tn: 44629.0000 - val_fn: 6.0000 - val_accuracy: 0.9810 - val_precision: 0.0814 - val_recall: 0.9268 - val_auc: 0.9777 - val_prc: 0.7702\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.1495 - cross entropy: 0.1495 - Brier score: 0.0440 - tp: 932.0000 - fp: 38.0000 - tn: 1002.0000 - fn: 76.0000 - accuracy: 0.9443 - precision: 0.9608 - recall: 0.9246 - auc: 0.9857 - prc: 0.9880"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.1545 - cross entropy: 0.1545 - Brier score: 0.0449 - tp: 4686.0000 - fp: 214.0000 - tn: 4952.0000 - fn: 388.0000 - accuracy: 0.9412 - precision: 0.9563 - recall: 0.9235 - auc: 0.9854 - prc: 0.9872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.1537 - cross entropy: 0.1537 - Brier score: 0.0448 - tp: 7509.0000 - fp: 340.0000 - tn: 7917.0000 - fn: 618.0000 - accuracy: 0.9415 - precision: 0.9567 - recall: 0.9240 - auc: 0.9855 - prc: 0.9874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.1527 - cross entropy: 0.1527 - Brier score: 0.0444 - tp: 10353.0000 - fp: 465.0000 - tn: 10901.0000 - fn: 809.0000 - accuracy: 0.9434 - precision: 0.9570 - recall: 0.9275 - auc: 0.9857 - prc: 0.9875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.1515 - cross entropy: 0.1515 - Brier score: 0.0440 - tp: 13255.0000 - fp: 580.0000 - tn: 13817.0000 - fn: 1020.0000 - accuracy: 0.9442 - precision: 0.9581 - recall: 0.9285 - auc: 0.9860 - prc: 0.9879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.1506 - cross entropy: 0.1506 - Brier score: 0.0439 - tp: 16108.0000 - fp: 724.0000 - tn: 16758.0000 - fn: 1226.0000 - accuracy: 0.9440 - precision: 0.9570 - recall: 0.9293 - auc: 0.9862 - prc: 0.9880"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.1500 - cross entropy: 0.1500 - Brier score: 0.0435 - tp: 19048.0000 - fp: 825.0000 - tn: 19659.0000 - fn: 1428.0000 - accuracy: 0.9450 - precision: 0.9585 - recall: 0.9303 - auc: 0.9864 - prc: 0.9882"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 5s - loss: 0.1499 - cross entropy: 0.1499 - Brier score: 0.0436 - tp: 21866.0000 - fp: 955.0000 - tn: 22633.0000 - fn: 1650.0000 - accuracy: 0.9447 - precision: 0.9582 - recall: 0.9298 - auc: 0.9864 - prc: 0.9883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 5s - loss: 0.1490 - cross entropy: 0.1490 - Brier score: 0.0433 - tp: 24745.0000 - fp: 1083.0000 - tn: 25567.0000 - fn: 1853.0000 - accuracy: 0.9449 - precision: 0.9581 - recall: 0.9303 - auc: 0.9866 - prc: 0.9885"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.1491 - cross entropy: 0.1491 - Brier score: 0.0434 - tp: 27603.0000 - fp: 1216.0000 - tn: 28511.0000 - fn: 2062.0000 - accuracy: 0.9448 - precision: 0.9578 - recall: 0.9305 - auc: 0.9866 - prc: 0.9885"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.1484 - cross entropy: 0.1484 - Brier score: 0.0432 - tp: 30523.0000 - fp: 1342.0000 - tn: 31415.0000 - fn: 2256.0000 - accuracy: 0.9451 - precision: 0.9579 - recall: 0.9312 - auc: 0.9868 - prc: 0.9886"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.1482 - cross entropy: 0.1482 - Brier score: 0.0432 - tp: 33392.0000 - fp: 1485.0000 - tn: 34346.0000 - fn: 2457.0000 - accuracy: 0.9450 - precision: 0.9574 - recall: 0.9315 - auc: 0.9868 - prc: 0.9887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.1481 - cross entropy: 0.1481 - Brier score: 0.0432 - tp: 36268.0000 - fp: 1632.0000 - tn: 37277.0000 - fn: 2647.0000 - accuracy: 0.9450 - precision: 0.9569 - recall: 0.9320 - auc: 0.9868 - prc: 0.9887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 40/278 [===>..........................] - ETA: 4s - loss: 0.1482 - cross entropy: 0.1482 - Brier score: 0.0432 - tp: 38175.0000 - fp: 1716.0000 - tn: 39249.0000 - fn: 2780.0000 - accuracy: 0.9451 - precision: 0.9570 - recall: 0.9321 - auc: 0.9868 - prc: 0.9887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 42/278 [===>..........................] - ETA: 4s - loss: 0.1481 - cross entropy: 0.1481 - Brier score: 0.0431 - tp: 40117.0000 - fp: 1800.0000 - tn: 41201.0000 - fn: 2898.0000 - accuracy: 0.9454 - precision: 0.9571 - recall: 0.9326 - auc: 0.9868 - prc: 0.9887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.1478 - cross entropy: 0.1478 - Brier score: 0.0430 - tp: 42048.0000 - fp: 1883.0000 - tn: 43158.0000 - fn: 3023.0000 - accuracy: 0.9456 - precision: 0.9571 - recall: 0.9329 - auc: 0.9869 - prc: 0.9888"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 46/278 [===>..........................] - ETA: 4s - loss: 0.1477 - cross entropy: 0.1477 - Brier score: 0.0429 - tp: 43947.0000 - fp: 1962.0000 - tn: 45131.0000 - fn: 3168.0000 - accuracy: 0.9455 - precision: 0.9573 - recall: 0.9328 - auc: 0.9869 - prc: 0.9888"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/278 [====>.........................] - ETA: 4s - loss: 0.1470 - cross entropy: 0.1470 - Brier score: 0.0428 - tp: 46784.0000 - fp: 2076.0000 - tn: 48123.0000 - fn: 3369.0000 - accuracy: 0.9457 - precision: 0.9575 - recall: 0.9328 - auc: 0.9871 - prc: 0.9889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 52/278 [====>.........................] - ETA: 4s - loss: 0.1473 - cross entropy: 0.1473 - Brier score: 0.0428 - tp: 49647.0000 - fp: 2215.0000 - tn: 51053.0000 - fn: 3581.0000 - accuracy: 0.9456 - precision: 0.9573 - recall: 0.9327 - auc: 0.9870 - prc: 0.9888"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/278 [====>.........................] - ETA: 4s - loss: 0.1470 - cross entropy: 0.1470 - Brier score: 0.0427 - tp: 52483.0000 - fp: 2336.0000 - tn: 54036.0000 - fn: 3785.0000 - accuracy: 0.9457 - precision: 0.9574 - recall: 0.9327 - auc: 0.9870 - prc: 0.9889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 58/278 [=====>........................] - ETA: 4s - loss: 0.1468 - cross entropy: 0.1468 - Brier score: 0.0427 - tp: 55326.0000 - fp: 2453.0000 - tn: 57007.0000 - fn: 3998.0000 - accuracy: 0.9457 - precision: 0.9575 - recall: 0.9326 - auc: 0.9871 - prc: 0.9889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/278 [=====>........................] - ETA: 4s - loss: 0.1467 - cross entropy: 0.1467 - Brier score: 0.0427 - tp: 58198.0000 - fp: 2598.0000 - tn: 59925.0000 - fn: 4207.0000 - accuracy: 0.9455 - precision: 0.9573 - recall: 0.9326 - auc: 0.9871 - prc: 0.9889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/278 [=====>........................] - ETA: 4s - loss: 0.1465 - cross entropy: 0.1465 - Brier score: 0.0427 - tp: 61100.0000 - fp: 2709.0000 - tn: 62844.0000 - fn: 4419.0000 - accuracy: 0.9456 - precision: 0.9575 - recall: 0.9326 - auc: 0.9871 - prc: 0.9890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/278 [======>.......................] - ETA: 4s - loss: 0.1461 - cross entropy: 0.1461 - Brier score: 0.0425 - tp: 63961.0000 - fp: 2829.0000 - tn: 65812.0000 - fn: 4614.0000 - accuracy: 0.9458 - precision: 0.9576 - recall: 0.9327 - auc: 0.9872 - prc: 0.9890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/278 [======>.......................] - ETA: 4s - loss: 0.1460 - cross entropy: 0.1460 - Brier score: 0.0425 - tp: 66818.0000 - fp: 2953.0000 - tn: 68756.0000 - fn: 4833.0000 - accuracy: 0.9457 - precision: 0.9577 - recall: 0.9325 - auc: 0.9872 - prc: 0.9890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 73/278 [======>.......................] - ETA: 4s - loss: 0.1460 - cross entropy: 0.1460 - Brier score: 0.0425 - tp: 69700.0000 - fp: 3090.0000 - tn: 71688.0000 - fn: 5026.0000 - accuracy: 0.9457 - precision: 0.9575 - recall: 0.9327 - auc: 0.9872 - prc: 0.9890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 76/278 [=======>......................] - ETA: 4s - loss: 0.1457 - cross entropy: 0.1457 - Brier score: 0.0424 - tp: 72523.0000 - fp: 3192.0000 - tn: 74709.0000 - fn: 5224.0000 - accuracy: 0.9459 - precision: 0.9578 - recall: 0.9328 - auc: 0.9873 - prc: 0.9891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/278 [=======>......................] - ETA: 4s - loss: 0.1454 - cross entropy: 0.1454 - Brier score: 0.0424 - tp: 75387.0000 - fp: 3332.0000 - tn: 77653.0000 - fn: 5420.0000 - accuracy: 0.9459 - precision: 0.9577 - recall: 0.9329 - auc: 0.9873 - prc: 0.9891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/278 [=======>......................] - ETA: 4s - loss: 0.1451 - cross entropy: 0.1451 - Brier score: 0.0423 - tp: 78308.0000 - fp: 3456.0000 - tn: 80570.0000 - fn: 5602.0000 - accuracy: 0.9461 - precision: 0.9577 - recall: 0.9332 - auc: 0.9873 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/278 [========>.....................] - ETA: 3s - loss: 0.1449 - cross entropy: 0.1449 - Brier score: 0.0422 - tp: 81141.0000 - fp: 3568.0000 - tn: 83572.0000 - fn: 5799.0000 - accuracy: 0.9462 - precision: 0.9579 - recall: 0.9333 - auc: 0.9874 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 88/278 [========>.....................] - ETA: 3s - loss: 0.1449 - cross entropy: 0.1449 - Brier score: 0.0422 - tp: 84012.0000 - fp: 3704.0000 - tn: 86503.0000 - fn: 6005.0000 - accuracy: 0.9461 - precision: 0.9578 - recall: 0.9333 - auc: 0.9874 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/278 [========>.....................] - ETA: 3s - loss: 0.1448 - cross entropy: 0.1448 - Brier score: 0.0422 - tp: 86905.0000 - fp: 3816.0000 - tn: 89435.0000 - fn: 6212.0000 - accuracy: 0.9462 - precision: 0.9579 - recall: 0.9333 - auc: 0.9874 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/278 [=========>....................] - ETA: 3s - loss: 0.1447 - cross entropy: 0.1447 - Brier score: 0.0421 - tp: 89782.0000 - fp: 3935.0000 - tn: 92387.0000 - fn: 6408.0000 - accuracy: 0.9463 - precision: 0.9580 - recall: 0.9334 - auc: 0.9874 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/278 [=========>....................] - ETA: 3s - loss: 0.1444 - cross entropy: 0.1444 - Brier score: 0.0421 - tp: 92680.0000 - fp: 4049.0000 - tn: 95328.0000 - fn: 6599.0000 - accuracy: 0.9464 - precision: 0.9581 - recall: 0.9335 - auc: 0.9874 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/278 [=========>....................] - ETA: 3s - loss: 0.1441 - cross entropy: 0.1441 - Brier score: 0.0420 - tp: 95599.0000 - fp: 4160.0000 - tn: 98239.0000 - fn: 6802.0000 - accuracy: 0.9465 - precision: 0.9583 - recall: 0.9336 - auc: 0.9875 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/278 [==========>...................] - ETA: 3s - loss: 0.1439 - cross entropy: 0.1439 - Brier score: 0.0419 - tp: 98483.0000 - fp: 4266.0000 - tn: 101191.0000 - fn: 7004.0000 - accuracy: 0.9466 - precision: 0.9585 - recall: 0.9336 - auc: 0.9875 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106/278 [==========>...................] - ETA: 3s - loss: 0.1437 - cross entropy: 0.1437 - Brier score: 0.0419 - tp: 101347.0000 - fp: 4381.0000 - tn: 104166.0000 - fn: 7194.0000 - accuracy: 0.9467 - precision: 0.9586 - recall: 0.9337 - auc: 0.9876 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/278 [==========>...................] - ETA: 3s - loss: 0.1436 - cross entropy: 0.1436 - Brier score: 0.0419 - tp: 104248.0000 - fp: 4509.0000 - tn: 107079.0000 - fn: 7396.0000 - accuracy: 0.9467 - precision: 0.9585 - recall: 0.9338 - auc: 0.9876 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/278 [===========>..................] - ETA: 3s - loss: 0.1435 - cross entropy: 0.1435 - Brier score: 0.0418 - tp: 107094.0000 - fp: 4636.0000 - tn: 110059.0000 - fn: 7587.0000 - accuracy: 0.9467 - precision: 0.9585 - recall: 0.9338 - auc: 0.9876 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/278 [===========>..................] - ETA: 3s - loss: 0.1432 - cross entropy: 0.1432 - Brier score: 0.0418 - tp: 109969.0000 - fp: 4762.0000 - tn: 113027.0000 - fn: 7762.0000 - accuracy: 0.9468 - precision: 0.9585 - recall: 0.9341 - auc: 0.9877 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "118/278 [===========>..................] - ETA: 3s - loss: 0.1430 - cross entropy: 0.1430 - Brier score: 0.0417 - tp: 112835.0000 - fp: 4880.0000 - tn: 115987.0000 - fn: 7962.0000 - accuracy: 0.9469 - precision: 0.9585 - recall: 0.9341 - auc: 0.9877 - prc: 0.9895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/278 [============>.................] - ETA: 3s - loss: 0.1428 - cross entropy: 0.1428 - Brier score: 0.0417 - tp: 115705.0000 - fp: 5002.0000 - tn: 118953.0000 - fn: 8148.0000 - accuracy: 0.9469 - precision: 0.9586 - recall: 0.9342 - auc: 0.9877 - prc: 0.9895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/278 [============>.................] - ETA: 3s - loss: 0.1428 - cross entropy: 0.1428 - Brier score: 0.0417 - tp: 118571.0000 - fp: 5123.0000 - tn: 121901.0000 - fn: 8357.0000 - accuracy: 0.9469 - precision: 0.9586 - recall: 0.9342 - auc: 0.9877 - prc: 0.9895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/278 [============>.................] - ETA: 3s - loss: 0.1426 - cross entropy: 0.1426 - Brier score: 0.0416 - tp: 121433.0000 - fp: 5232.0000 - tn: 124873.0000 - fn: 8558.0000 - accuracy: 0.9470 - precision: 0.9587 - recall: 0.9342 - auc: 0.9878 - prc: 0.9895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/278 [=============>................] - ETA: 2s - loss: 0.1423 - cross entropy: 0.1423 - Brier score: 0.0416 - tp: 124357.0000 - fp: 5350.0000 - tn: 127787.0000 - fn: 8746.0000 - accuracy: 0.9471 - precision: 0.9588 - recall: 0.9343 - auc: 0.9878 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/278 [=============>................] - ETA: 2s - loss: 0.1422 - cross entropy: 0.1422 - Brier score: 0.0415 - tp: 127223.0000 - fp: 5473.0000 - tn: 130731.0000 - fn: 8957.0000 - accuracy: 0.9470 - precision: 0.9588 - recall: 0.9342 - auc: 0.9879 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "136/278 [=============>................] - ETA: 2s - loss: 0.1420 - cross entropy: 0.1420 - Brier score: 0.0415 - tp: 130196.0000 - fp: 5585.0000 - tn: 133596.0000 - fn: 9151.0000 - accuracy: 0.9471 - precision: 0.9589 - recall: 0.9343 - auc: 0.9879 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/278 [==============>...............] - ETA: 2s - loss: 0.1418 - cross entropy: 0.1418 - Brier score: 0.0415 - tp: 133045.0000 - fp: 5717.0000 - tn: 136564.0000 - fn: 9346.0000 - accuracy: 0.9471 - precision: 0.9588 - recall: 0.9344 - auc: 0.9879 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/278 [==============>...............] - ETA: 2s - loss: 0.1417 - cross entropy: 0.1417 - Brier score: 0.0414 - tp: 135939.0000 - fp: 5837.0000 - tn: 139479.0000 - fn: 9561.0000 - accuracy: 0.9471 - precision: 0.9588 - recall: 0.9343 - auc: 0.9879 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/278 [==============>...............] - ETA: 2s - loss: 0.1416 - cross entropy: 0.1416 - Brier score: 0.0414 - tp: 138835.0000 - fp: 5952.0000 - tn: 142407.0000 - fn: 9766.0000 - accuracy: 0.9471 - precision: 0.9589 - recall: 0.9343 - auc: 0.9879 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "148/278 [==============>...............] - ETA: 2s - loss: 0.1416 - cross entropy: 0.1416 - Brier score: 0.0414 - tp: 141664.0000 - fp: 6079.0000 - tn: 145376.0000 - fn: 9985.0000 - accuracy: 0.9470 - precision: 0.9589 - recall: 0.9342 - auc: 0.9879 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/278 [===============>..............] - ETA: 2s - loss: 0.1414 - cross entropy: 0.1414 - Brier score: 0.0414 - tp: 144553.0000 - fp: 6196.0000 - tn: 148321.0000 - fn: 10178.0000 - accuracy: 0.9471 - precision: 0.9589 - recall: 0.9342 - auc: 0.9880 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/278 [===============>..............] - ETA: 2s - loss: 0.1412 - cross entropy: 0.1412 - Brier score: 0.0413 - tp: 147414.0000 - fp: 6299.0000 - tn: 151317.0000 - fn: 10362.0000 - accuracy: 0.9472 - precision: 0.9590 - recall: 0.9343 - auc: 0.9880 - prc: 0.9897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/278 [===============>..............] - ETA: 2s - loss: 0.1411 - cross entropy: 0.1411 - Brier score: 0.0413 - tp: 150284.0000 - fp: 6426.0000 - tn: 154278.0000 - fn: 10548.0000 - accuracy: 0.9472 - precision: 0.9590 - recall: 0.9344 - auc: 0.9880 - prc: 0.9897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/278 [================>.............] - ETA: 2s - loss: 0.1410 - cross entropy: 0.1410 - Brier score: 0.0412 - tp: 153139.0000 - fp: 6553.0000 - tn: 157248.0000 - fn: 10740.0000 - accuracy: 0.9472 - precision: 0.9590 - recall: 0.9345 - auc: 0.9880 - prc: 0.9897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/278 [================>.............] - ETA: 2s - loss: 0.1408 - cross entropy: 0.1408 - Brier score: 0.0412 - tp: 156046.0000 - fp: 6642.0000 - tn: 160194.0000 - fn: 10942.0000 - accuracy: 0.9473 - precision: 0.9592 - recall: 0.9345 - auc: 0.9881 - prc: 0.9897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "166/278 [================>.............] - ETA: 2s - loss: 0.1406 - cross entropy: 0.1406 - Brier score: 0.0411 - tp: 158932.0000 - fp: 6760.0000 - tn: 163157.0000 - fn: 11119.0000 - accuracy: 0.9474 - precision: 0.9592 - recall: 0.9346 - auc: 0.9881 - prc: 0.9897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/278 [=================>............] - ETA: 2s - loss: 0.1405 - cross entropy: 0.1405 - Brier score: 0.0411 - tp: 161768.0000 - fp: 6874.0000 - tn: 166155.0000 - fn: 11315.0000 - accuracy: 0.9474 - precision: 0.9592 - recall: 0.9346 - auc: 0.9881 - prc: 0.9897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172/278 [=================>............] - ETA: 2s - loss: 0.1404 - cross entropy: 0.1404 - Brier score: 0.0411 - tp: 164671.0000 - fp: 7000.0000 - tn: 169072.0000 - fn: 11513.0000 - accuracy: 0.9474 - precision: 0.9592 - recall: 0.9347 - auc: 0.9882 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/278 [=================>............] - ETA: 2s - loss: 0.1403 - cross entropy: 0.1403 - Brier score: 0.0411 - tp: 167498.0000 - fp: 7112.0000 - tn: 172055.0000 - fn: 11735.0000 - accuracy: 0.9474 - precision: 0.9593 - recall: 0.9345 - auc: 0.9882 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/278 [==================>...........] - ETA: 2s - loss: 0.1402 - cross entropy: 0.1402 - Brier score: 0.0410 - tp: 170391.0000 - fp: 7241.0000 - tn: 174988.0000 - fn: 11924.0000 - accuracy: 0.9474 - precision: 0.9592 - recall: 0.9346 - auc: 0.9882 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/278 [==================>...........] - ETA: 1s - loss: 0.1403 - cross entropy: 0.1403 - Brier score: 0.0410 - tp: 173246.0000 - fp: 7356.0000 - tn: 177956.0000 - fn: 12130.0000 - accuracy: 0.9474 - precision: 0.9593 - recall: 0.9346 - auc: 0.9882 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/278 [==================>...........] - ETA: 1s - loss: 0.1401 - cross entropy: 0.1401 - Brier score: 0.0410 - tp: 176127.0000 - fp: 7488.0000 - tn: 180915.0000 - fn: 12302.0000 - accuracy: 0.9475 - precision: 0.9592 - recall: 0.9347 - auc: 0.9882 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "187/278 [===================>..........] - ETA: 1s - loss: 0.1400 - cross entropy: 0.1400 - Brier score: 0.0409 - tp: 179051.0000 - fp: 7598.0000 - tn: 183819.0000 - fn: 12508.0000 - accuracy: 0.9475 - precision: 0.9593 - recall: 0.9347 - auc: 0.9883 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/278 [===================>..........] - ETA: 1s - loss: 0.1398 - cross entropy: 0.1398 - Brier score: 0.0409 - tp: 181950.0000 - fp: 7704.0000 - tn: 186749.0000 - fn: 12717.0000 - accuracy: 0.9475 - precision: 0.9594 - recall: 0.9347 - auc: 0.9883 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/278 [===================>..........] - ETA: 1s - loss: 0.1398 - cross entropy: 0.1398 - Brier score: 0.0409 - tp: 184828.0000 - fp: 7836.0000 - tn: 189687.0000 - fn: 12913.0000 - accuracy: 0.9475 - precision: 0.9593 - recall: 0.9347 - auc: 0.9883 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/278 [====================>.........] - ETA: 1s - loss: 0.1396 - cross entropy: 0.1396 - Brier score: 0.0409 - tp: 187691.0000 - fp: 7967.0000 - tn: 192639.0000 - fn: 13111.0000 - accuracy: 0.9475 - precision: 0.9593 - recall: 0.9347 - auc: 0.9883 - prc: 0.9898"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/278 [====================>.........] - ETA: 1s - loss: 0.1395 - cross entropy: 0.1395 - Brier score: 0.0408 - tp: 190631.0000 - fp: 8071.0000 - tn: 195551.0000 - fn: 13299.0000 - accuracy: 0.9476 - precision: 0.9594 - recall: 0.9348 - auc: 0.9883 - prc: 0.9899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/278 [====================>.........] - ETA: 1s - loss: 0.1394 - cross entropy: 0.1394 - Brier score: 0.0408 - tp: 193524.0000 - fp: 8195.0000 - tn: 198484.0000 - fn: 13493.0000 - accuracy: 0.9476 - precision: 0.9594 - recall: 0.9348 - auc: 0.9884 - prc: 0.9899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "205/278 [=====================>........] - ETA: 1s - loss: 0.1393 - cross entropy: 0.1393 - Brier score: 0.0408 - tp: 196382.0000 - fp: 8309.0000 - tn: 201466.0000 - fn: 13683.0000 - accuracy: 0.9476 - precision: 0.9594 - recall: 0.9349 - auc: 0.9884 - prc: 0.9899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/278 [=====================>........] - ETA: 1s - loss: 0.1392 - cross entropy: 0.1392 - Brier score: 0.0407 - tp: 199289.0000 - fp: 8421.0000 - tn: 204400.0000 - fn: 13874.0000 - accuracy: 0.9477 - precision: 0.9595 - recall: 0.9349 - auc: 0.9884 - prc: 0.9899"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/278 [=====================>........] - ETA: 1s - loss: 0.1389 - cross entropy: 0.1389 - Brier score: 0.0407 - tp: 202184.0000 - fp: 8530.0000 - tn: 207366.0000 - fn: 14048.0000 - accuracy: 0.9478 - precision: 0.9595 - recall: 0.9350 - auc: 0.9885 - prc: 0.9900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/278 [======================>.......] - ETA: 1s - loss: 0.1388 - cross entropy: 0.1388 - Brier score: 0.0406 - tp: 205063.0000 - fp: 8633.0000 - tn: 210332.0000 - fn: 14244.0000 - accuracy: 0.9478 - precision: 0.9596 - recall: 0.9350 - auc: 0.9885 - prc: 0.9900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/278 [======================>.......] - ETA: 1s - loss: 0.1385 - cross entropy: 0.1385 - Brier score: 0.0406 - tp: 207945.0000 - fp: 8736.0000 - tn: 213297.0000 - fn: 14438.0000 - accuracy: 0.9479 - precision: 0.9597 - recall: 0.9351 - auc: 0.9885 - prc: 0.9900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "220/278 [======================>.......] - ETA: 1s - loss: 0.1384 - cross entropy: 0.1384 - Brier score: 0.0405 - tp: 210821.0000 - fp: 8843.0000 - tn: 216274.0000 - fn: 14622.0000 - accuracy: 0.9479 - precision: 0.9597 - recall: 0.9351 - auc: 0.9886 - prc: 0.9900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/278 [=======================>......] - ETA: 1s - loss: 0.1383 - cross entropy: 0.1383 - Brier score: 0.0405 - tp: 213669.0000 - fp: 8962.0000 - tn: 219265.0000 - fn: 14808.0000 - accuracy: 0.9480 - precision: 0.9597 - recall: 0.9352 - auc: 0.9886 - prc: 0.9900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "226/278 [=======================>......] - ETA: 1s - loss: 0.1381 - cross entropy: 0.1381 - Brier score: 0.0405 - tp: 216577.0000 - fp: 9072.0000 - tn: 222216.0000 - fn: 14983.0000 - accuracy: 0.9480 - precision: 0.9598 - recall: 0.9353 - auc: 0.9886 - prc: 0.9901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/278 [=======================>......] - ETA: 0s - loss: 0.1379 - cross entropy: 0.1379 - Brier score: 0.0404 - tp: 219441.0000 - fp: 9178.0000 - tn: 225197.0000 - fn: 15176.0000 - accuracy: 0.9481 - precision: 0.9599 - recall: 0.9353 - auc: 0.9886 - prc: 0.9901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/278 [========================>.....] - ETA: 0s - loss: 0.1378 - cross entropy: 0.1378 - Brier score: 0.0404 - tp: 222272.0000 - fp: 9304.0000 - tn: 228211.0000 - fn: 15349.0000 - accuracy: 0.9481 - precision: 0.9598 - recall: 0.9354 - auc: 0.9887 - prc: 0.9901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/278 [========================>.....] - ETA: 0s - loss: 0.1376 - cross entropy: 0.1376 - Brier score: 0.0403 - tp: 225185.0000 - fp: 9410.0000 - tn: 231139.0000 - fn: 15546.0000 - accuracy: 0.9481 - precision: 0.9599 - recall: 0.9354 - auc: 0.9887 - prc: 0.9901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/278 [========================>.....] - ETA: 0s - loss: 0.1375 - cross entropy: 0.1375 - Brier score: 0.0403 - tp: 227143.0000 - fp: 9490.0000 - tn: 233082.0000 - fn: 15661.0000 - accuracy: 0.9482 - precision: 0.9599 - recall: 0.9355 - auc: 0.9887 - prc: 0.9901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/278 [========================>.....] - ETA: 0s - loss: 0.1374 - cross entropy: 0.1374 - Brier score: 0.0403 - tp: 230037.0000 - fp: 9604.0000 - tn: 236027.0000 - fn: 15852.0000 - accuracy: 0.9482 - precision: 0.9599 - recall: 0.9355 - auc: 0.9887 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/278 [=========================>....] - ETA: 0s - loss: 0.1374 - cross entropy: 0.1374 - Brier score: 0.0403 - tp: 232918.0000 - fp: 9728.0000 - tn: 238973.0000 - fn: 16045.0000 - accuracy: 0.9482 - precision: 0.9599 - recall: 0.9356 - auc: 0.9887 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/278 [=========================>....] - ETA: 0s - loss: 0.1373 - cross entropy: 0.1373 - Brier score: 0.0403 - tp: 235847.0000 - fp: 9829.0000 - tn: 241882.0000 - fn: 16250.0000 - accuracy: 0.9482 - precision: 0.9600 - recall: 0.9355 - auc: 0.9888 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/278 [=========================>....] - ETA: 0s - loss: 0.1372 - cross entropy: 0.1372 - Brier score: 0.0402 - tp: 238686.0000 - fp: 9936.0000 - tn: 244898.0000 - fn: 16432.0000 - accuracy: 0.9483 - precision: 0.9600 - recall: 0.9356 - auc: 0.9888 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/278 [==========================>...] - ETA: 0s - loss: 0.1370 - cross entropy: 0.1370 - Brier score: 0.0402 - tp: 241608.0000 - fp: 10059.0000 - tn: 247811.0000 - fn: 16618.0000 - accuracy: 0.9483 - precision: 0.9600 - recall: 0.9356 - auc: 0.9888 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/278 [==========================>...] - ETA: 0s - loss: 0.1369 - cross entropy: 0.1369 - Brier score: 0.0402 - tp: 244479.0000 - fp: 10174.0000 - tn: 250779.0000 - fn: 16808.0000 - accuracy: 0.9483 - precision: 0.9600 - recall: 0.9357 - auc: 0.9888 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "258/278 [==========================>...] - ETA: 0s - loss: 0.1367 - cross entropy: 0.1367 - Brier score: 0.0401 - tp: 247431.0000 - fp: 10273.0000 - tn: 253694.0000 - fn: 16986.0000 - accuracy: 0.9484 - precision: 0.9601 - recall: 0.9358 - auc: 0.9889 - prc: 0.9902"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "261/278 [===========================>..] - ETA: 0s - loss: 0.1366 - cross entropy: 0.1366 - Brier score: 0.0401 - tp: 250273.0000 - fp: 10369.0000 - tn: 256703.0000 - fn: 17183.0000 - accuracy: 0.9485 - precision: 0.9602 - recall: 0.9358 - auc: 0.9889 - prc: 0.9903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "264/278 [===========================>..] - ETA: 0s - loss: 0.1365 - cross entropy: 0.1365 - Brier score: 0.0400 - tp: 253180.0000 - fp: 10507.0000 - tn: 259630.0000 - fn: 17355.0000 - accuracy: 0.9485 - precision: 0.9602 - recall: 0.9358 - auc: 0.9889 - prc: 0.9903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/278 [===========================>..] - ETA: 0s - loss: 0.1364 - cross entropy: 0.1364 - Brier score: 0.0400 - tp: 256071.0000 - fp: 10612.0000 - tn: 262602.0000 - fn: 17531.0000 - accuracy: 0.9485 - precision: 0.9602 - recall: 0.9359 - auc: 0.9889 - prc: 0.9903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/278 [============================>.] - ETA: 0s - loss: 0.1362 - cross entropy: 0.1362 - Brier score: 0.0399 - tp: 258947.0000 - fp: 10732.0000 - tn: 265576.0000 - fn: 17705.0000 - accuracy: 0.9486 - precision: 0.9602 - recall: 0.9360 - auc: 0.9890 - prc: 0.9903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "273/278 [============================>.] - ETA: 0s - loss: 0.1361 - cross entropy: 0.1361 - Brier score: 0.0399 - tp: 261871.0000 - fp: 10833.0000 - tn: 268514.0000 - fn: 17886.0000 - accuracy: 0.9486 - precision: 0.9603 - recall: 0.9361 - auc: 0.9890 - prc: 0.9903"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "276/278 [============================>.] - ETA: 0s - loss: 0.1359 - cross entropy: 0.1359 - Brier score: 0.0399 - tp: 264753.0000 - fp: 10932.0000 - tn: 271490.0000 - fn: 18073.0000 - accuracy: 0.9487 - precision: 0.9603 - recall: 0.9361 - auc: 0.9890 - prc: 0.9904"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 6s 21ms/step - loss: 0.1358 - cross entropy: 0.1358 - Brier score: 0.0398 - tp: 266700.0000 - fp: 11006.0000 - tn: 273451.0000 - fn: 18187.0000 - accuracy: 0.9487 - precision: 0.9604 - recall: 0.9362 - auc: 0.9891 - prc: 0.9904 - val_loss: 0.0725 - val_cross entropy: 0.0725 - val_Brier score: 0.0158 - val_tp: 76.0000 - val_fp: 790.0000 - val_tn: 44697.0000 - val_fn: 6.0000 - val_accuracy: 0.9825 - val_precision: 0.0878 - val_recall: 0.9268 - val_auc: 0.9766 - val_prc: 0.7553\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.1189 - cross entropy: 0.1189 - Brier score: 0.0348 - tp: 991.0000 - fp: 31.0000 - tn: 968.0000 - fn: 58.0000 - accuracy: 0.9565 - precision: 0.9697 - recall: 0.9447 - auc: 0.9917 - prc: 0.9931"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.1208 - cross entropy: 0.1208 - Brier score: 0.0357 - tp: 4871.0000 - fp: 178.0000 - tn: 4900.0000 - fn: 291.0000 - accuracy: 0.9542 - precision: 0.9647 - recall: 0.9436 - auc: 0.9918 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.1198 - cross entropy: 0.1198 - Brier score: 0.0355 - tp: 7755.0000 - fp: 290.0000 - tn: 7869.0000 - fn: 470.0000 - accuracy: 0.9536 - precision: 0.9640 - recall: 0.9429 - auc: 0.9920 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.1201 - cross entropy: 0.1201 - Brier score: 0.0356 - tp: 10625.0000 - fp: 394.0000 - tn: 10839.0000 - fn: 670.0000 - accuracy: 0.9528 - precision: 0.9642 - recall: 0.9407 - auc: 0.9918 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.1200 - cross entropy: 0.1200 - Brier score: 0.0356 - tp: 13575.0000 - fp: 498.0000 - tn: 13757.0000 - fn: 842.0000 - accuracy: 0.9533 - precision: 0.9646 - recall: 0.9416 - auc: 0.9918 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.1208 - cross entropy: 0.1208 - Brier score: 0.0358 - tp: 16414.0000 - fp: 606.0000 - tn: 16762.0000 - fn: 1034.0000 - accuracy: 0.9529 - precision: 0.9644 - recall: 0.9407 - auc: 0.9917 - prc: 0.9924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.1217 - cross entropy: 0.1217 - Brier score: 0.0361 - tp: 19298.0000 - fp: 719.0000 - tn: 19717.0000 - fn: 1226.0000 - accuracy: 0.9525 - precision: 0.9641 - recall: 0.9403 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.1229 - cross entropy: 0.1229 - Brier score: 0.0363 - tp: 22195.0000 - fp: 833.0000 - tn: 22658.0000 - fn: 1418.0000 - accuracy: 0.9522 - precision: 0.9638 - recall: 0.9399 - auc: 0.9913 - prc: 0.9920"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 25/278 [=>............................] - ETA: 4s - loss: 0.1225 - cross entropy: 0.1225 - Brier score: 0.0362 - tp: 24150.0000 - fp: 907.0000 - tn: 24610.0000 - fn: 1533.0000 - accuracy: 0.9523 - precision: 0.9638 - recall: 0.9403 - auc: 0.9914 - prc: 0.9921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 28/278 [==>...........................] - ETA: 4s - loss: 0.1221 - cross entropy: 0.1221 - Brier score: 0.0360 - tp: 27025.0000 - fp: 1010.0000 - tn: 27602.0000 - fn: 1707.0000 - accuracy: 0.9526 - precision: 0.9640 - recall: 0.9406 - auc: 0.9915 - prc: 0.9921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/278 [==>...........................] - ETA: 4s - loss: 0.1217 - cross entropy: 0.1217 - Brier score: 0.0359 - tp: 29910.0000 - fp: 1120.0000 - tn: 30579.0000 - fn: 1879.0000 - accuracy: 0.9528 - precision: 0.9639 - recall: 0.9409 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/278 [==>...........................] - ETA: 4s - loss: 0.1221 - cross entropy: 0.1221 - Brier score: 0.0361 - tp: 32810.0000 - fp: 1245.0000 - tn: 33515.0000 - fn: 2062.0000 - accuracy: 0.9525 - precision: 0.9634 - recall: 0.9409 - auc: 0.9915 - prc: 0.9921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/278 [==>...........................] - ETA: 4s - loss: 0.1220 - cross entropy: 0.1220 - Brier score: 0.0360 - tp: 35720.0000 - fp: 1360.0000 - tn: 36463.0000 - fn: 2233.0000 - accuracy: 0.9526 - precision: 0.9633 - recall: 0.9412 - auc: 0.9915 - prc: 0.9921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 40/278 [===>..........................] - ETA: 4s - loss: 0.1221 - cross entropy: 0.1221 - Brier score: 0.0361 - tp: 38608.0000 - fp: 1470.0000 - tn: 39426.0000 - fn: 2416.0000 - accuracy: 0.9526 - precision: 0.9633 - recall: 0.9411 - auc: 0.9915 - prc: 0.9921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 43/278 [===>..........................] - ETA: 4s - loss: 0.1221 - cross entropy: 0.1221 - Brier score: 0.0361 - tp: 41501.0000 - fp: 1565.0000 - tn: 42397.0000 - fn: 2601.0000 - accuracy: 0.9527 - precision: 0.9637 - recall: 0.9410 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 46/278 [===>..........................] - ETA: 4s - loss: 0.1220 - cross entropy: 0.1220 - Brier score: 0.0361 - tp: 44389.0000 - fp: 1688.0000 - tn: 45355.0000 - fn: 2776.0000 - accuracy: 0.9526 - precision: 0.9634 - recall: 0.9411 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/278 [====>.........................] - ETA: 4s - loss: 0.1218 - cross entropy: 0.1218 - Brier score: 0.0361 - tp: 47221.0000 - fp: 1811.0000 - tn: 48370.0000 - fn: 2950.0000 - accuracy: 0.9526 - precision: 0.9631 - recall: 0.9412 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 52/278 [====>.........................] - ETA: 4s - loss: 0.1217 - cross entropy: 0.1217 - Brier score: 0.0360 - tp: 50116.0000 - fp: 1922.0000 - tn: 51327.0000 - fn: 3131.0000 - accuracy: 0.9526 - precision: 0.9631 - recall: 0.9412 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/278 [====>.........................] - ETA: 4s - loss: 0.1216 - cross entropy: 0.1216 - Brier score: 0.0360 - tp: 53017.0000 - fp: 2030.0000 - tn: 54290.0000 - fn: 3303.0000 - accuracy: 0.9527 - precision: 0.9631 - recall: 0.9414 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 58/278 [=====>........................] - ETA: 4s - loss: 0.1215 - cross entropy: 0.1215 - Brier score: 0.0360 - tp: 55846.0000 - fp: 2156.0000 - tn: 57319.0000 - fn: 3463.0000 - accuracy: 0.9527 - precision: 0.9628 - recall: 0.9416 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/278 [=====>........................] - ETA: 4s - loss: 0.1215 - cross entropy: 0.1215 - Brier score: 0.0360 - tp: 58670.0000 - fp: 2274.0000 - tn: 60338.0000 - fn: 3646.0000 - accuracy: 0.9526 - precision: 0.9627 - recall: 0.9415 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/278 [=====>........................] - ETA: 4s - loss: 0.1214 - cross entropy: 0.1214 - Brier score: 0.0359 - tp: 61525.0000 - fp: 2379.0000 - tn: 63352.0000 - fn: 3816.0000 - accuracy: 0.9527 - precision: 0.9628 - recall: 0.9416 - auc: 0.9915 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/278 [======>.......................] - ETA: 4s - loss: 0.1212 - cross entropy: 0.1212 - Brier score: 0.0359 - tp: 64377.0000 - fp: 2478.0000 - tn: 66356.0000 - fn: 4005.0000 - accuracy: 0.9528 - precision: 0.9629 - recall: 0.9414 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/278 [======>.......................] - ETA: 4s - loss: 0.1211 - cross entropy: 0.1211 - Brier score: 0.0358 - tp: 67208.0000 - fp: 2592.0000 - tn: 69393.0000 - fn: 4167.0000 - accuracy: 0.9529 - precision: 0.9629 - recall: 0.9416 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 73/278 [======>.......................] - ETA: 3s - loss: 0.1212 - cross entropy: 0.1212 - Brier score: 0.0358 - tp: 70121.0000 - fp: 2720.0000 - tn: 72319.0000 - fn: 4344.0000 - accuracy: 0.9528 - precision: 0.9627 - recall: 0.9417 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 76/278 [=======>......................] - ETA: 3s - loss: 0.1211 - cross entropy: 0.1211 - Brier score: 0.0358 - tp: 73042.0000 - fp: 2829.0000 - tn: 75265.0000 - fn: 4512.0000 - accuracy: 0.9528 - precision: 0.9627 - recall: 0.9418 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 79/278 [=======>......................] - ETA: 3s - loss: 0.1215 - cross entropy: 0.1215 - Brier score: 0.0358 - tp: 75955.0000 - fp: 2931.0000 - tn: 78214.0000 - fn: 4692.0000 - accuracy: 0.9529 - precision: 0.9628 - recall: 0.9418 - auc: 0.9916 - prc: 0.9921"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 82/278 [=======>......................] - ETA: 3s - loss: 0.1213 - cross entropy: 0.1213 - Brier score: 0.0358 - tp: 78877.0000 - fp: 3035.0000 - tn: 81159.0000 - fn: 4865.0000 - accuracy: 0.9530 - precision: 0.9629 - recall: 0.9419 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/278 [========>.....................] - ETA: 3s - loss: 0.1213 - cross entropy: 0.1213 - Brier score: 0.0358 - tp: 81790.0000 - fp: 3133.0000 - tn: 84116.0000 - fn: 5041.0000 - accuracy: 0.9530 - precision: 0.9631 - recall: 0.9419 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 88/278 [========>.....................] - ETA: 3s - loss: 0.1211 - cross entropy: 0.1211 - Brier score: 0.0357 - tp: 84708.0000 - fp: 3244.0000 - tn: 87064.0000 - fn: 5208.0000 - accuracy: 0.9531 - precision: 0.9631 - recall: 0.9421 - auc: 0.9916 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/278 [========>.....................] - ETA: 3s - loss: 0.1209 - cross entropy: 0.1209 - Brier score: 0.0357 - tp: 87509.0000 - fp: 3360.0000 - tn: 90128.0000 - fn: 5371.0000 - accuracy: 0.9532 - precision: 0.9630 - recall: 0.9422 - auc: 0.9917 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/278 [=========>....................] - ETA: 3s - loss: 0.1207 - cross entropy: 0.1207 - Brier score: 0.0356 - tp: 90467.0000 - fp: 3455.0000 - tn: 93039.0000 - fn: 5551.0000 - accuracy: 0.9532 - precision: 0.9632 - recall: 0.9422 - auc: 0.9917 - prc: 0.9922"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/278 [=========>....................] - ETA: 3s - loss: 0.1206 - cross entropy: 0.1206 - Brier score: 0.0356 - tp: 93376.0000 - fp: 3559.0000 - tn: 95974.0000 - fn: 5747.0000 - accuracy: 0.9532 - precision: 0.9633 - recall: 0.9420 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/278 [=========>....................] - ETA: 3s - loss: 0.1203 - cross entropy: 0.1203 - Brier score: 0.0355 - tp: 96348.0000 - fp: 3663.0000 - tn: 98863.0000 - fn: 5926.0000 - accuracy: 0.9532 - precision: 0.9634 - recall: 0.9421 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/278 [==========>...................] - ETA: 3s - loss: 0.1202 - cross entropy: 0.1202 - Brier score: 0.0355 - tp: 99218.0000 - fp: 3781.0000 - tn: 101845.0000 - fn: 6100.0000 - accuracy: 0.9532 - precision: 0.9633 - recall: 0.9421 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106/278 [==========>...................] - ETA: 3s - loss: 0.1202 - cross entropy: 0.1202 - Brier score: 0.0355 - tp: 102115.0000 - fp: 3902.0000 - tn: 104788.0000 - fn: 6283.0000 - accuracy: 0.9531 - precision: 0.9632 - recall: 0.9420 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/278 [==========>...................] - ETA: 3s - loss: 0.1203 - cross entropy: 0.1203 - Brier score: 0.0355 - tp: 104973.0000 - fp: 4020.0000 - tn: 107787.0000 - fn: 6452.0000 - accuracy: 0.9531 - precision: 0.9631 - recall: 0.9421 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/278 [===========>..................] - ETA: 3s - loss: 0.1201 - cross entropy: 0.1201 - Brier score: 0.0355 - tp: 107901.0000 - fp: 4129.0000 - tn: 110718.0000 - fn: 6628.0000 - accuracy: 0.9531 - precision: 0.9631 - recall: 0.9421 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/278 [===========>..................] - ETA: 3s - loss: 0.1203 - cross entropy: 0.1203 - Brier score: 0.0355 - tp: 110786.0000 - fp: 4252.0000 - tn: 113671.0000 - fn: 6811.0000 - accuracy: 0.9530 - precision: 0.9630 - recall: 0.9421 - auc: 0.9917 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "118/278 [===========>..................] - ETA: 3s - loss: 0.1200 - cross entropy: 0.1200 - Brier score: 0.0354 - tp: 113711.0000 - fp: 4344.0000 - tn: 116634.0000 - fn: 6975.0000 - accuracy: 0.9532 - precision: 0.9632 - recall: 0.9422 - auc: 0.9918 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/278 [============>.................] - ETA: 3s - loss: 0.1200 - cross entropy: 0.1200 - Brier score: 0.0354 - tp: 116610.0000 - fp: 4467.0000 - tn: 119586.0000 - fn: 7145.0000 - accuracy: 0.9531 - precision: 0.9631 - recall: 0.9423 - auc: 0.9918 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/278 [============>.................] - ETA: 2s - loss: 0.1198 - cross entropy: 0.1198 - Brier score: 0.0354 - tp: 119503.0000 - fp: 4585.0000 - tn: 122560.0000 - fn: 7304.0000 - accuracy: 0.9532 - precision: 0.9631 - recall: 0.9424 - auc: 0.9918 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/278 [============>.................] - ETA: 2s - loss: 0.1198 - cross entropy: 0.1198 - Brier score: 0.0354 - tp: 122426.0000 - fp: 4695.0000 - tn: 125498.0000 - fn: 7477.0000 - accuracy: 0.9532 - precision: 0.9631 - recall: 0.9424 - auc: 0.9918 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/278 [=============>................] - ETA: 2s - loss: 0.1198 - cross entropy: 0.1198 - Brier score: 0.0354 - tp: 125418.0000 - fp: 4809.0000 - tn: 128370.0000 - fn: 7643.0000 - accuracy: 0.9532 - precision: 0.9631 - recall: 0.9426 - auc: 0.9918 - prc: 0.9923"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/278 [=============>................] - ETA: 2s - loss: 0.1197 - cross entropy: 0.1197 - Brier score: 0.0354 - tp: 128281.0000 - fp: 4927.0000 - tn: 131362.0000 - fn: 7814.0000 - accuracy: 0.9532 - precision: 0.9630 - recall: 0.9426 - auc: 0.9918 - prc: 0.9924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "136/278 [=============>................] - ETA: 2s - loss: 0.1196 - cross entropy: 0.1196 - Brier score: 0.0353 - tp: 131207.0000 - fp: 5023.0000 - tn: 134301.0000 - fn: 7997.0000 - accuracy: 0.9533 - precision: 0.9631 - recall: 0.9426 - auc: 0.9918 - prc: 0.9924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/278 [==============>...............] - ETA: 2s - loss: 0.1194 - cross entropy: 0.1194 - Brier score: 0.0353 - tp: 134185.0000 - fp: 5124.0000 - tn: 137200.0000 - fn: 8163.0000 - accuracy: 0.9533 - precision: 0.9632 - recall: 0.9427 - auc: 0.9919 - prc: 0.9924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/278 [==============>...............] - ETA: 2s - loss: 0.1192 - cross entropy: 0.1192 - Brier score: 0.0353 - tp: 137071.0000 - fp: 5213.0000 - tn: 140189.0000 - fn: 8343.0000 - accuracy: 0.9534 - precision: 0.9634 - recall: 0.9426 - auc: 0.9919 - prc: 0.9924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/278 [==============>...............] - ETA: 2s - loss: 0.1191 - cross entropy: 0.1191 - Brier score: 0.0352 - tp: 140019.0000 - fp: 5316.0000 - tn: 143106.0000 - fn: 8519.0000 - accuracy: 0.9534 - precision: 0.9634 - recall: 0.9426 - auc: 0.9919 - prc: 0.9924"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "148/278 [==============>...............] - ETA: 2s - loss: 0.1189 - cross entropy: 0.1189 - Brier score: 0.0352 - tp: 142940.0000 - fp: 5419.0000 - tn: 146066.0000 - fn: 8679.0000 - accuracy: 0.9535 - precision: 0.9635 - recall: 0.9428 - auc: 0.9919 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/278 [===============>..............] - ETA: 2s - loss: 0.1188 - cross entropy: 0.1188 - Brier score: 0.0351 - tp: 145887.0000 - fp: 5523.0000 - tn: 148993.0000 - fn: 8845.0000 - accuracy: 0.9535 - precision: 0.9635 - recall: 0.9428 - auc: 0.9919 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/278 [===============>..............] - ETA: 2s - loss: 0.1188 - cross entropy: 0.1188 - Brier score: 0.0351 - tp: 148774.0000 - fp: 5632.0000 - tn: 151974.0000 - fn: 9012.0000 - accuracy: 0.9536 - precision: 0.9635 - recall: 0.9429 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/278 [===============>..............] - ETA: 2s - loss: 0.1187 - cross entropy: 0.1187 - Brier score: 0.0351 - tp: 151670.0000 - fp: 5732.0000 - tn: 154961.0000 - fn: 9173.0000 - accuracy: 0.9536 - precision: 0.9636 - recall: 0.9430 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/278 [================>.............] - ETA: 2s - loss: 0.1186 - cross entropy: 0.1186 - Brier score: 0.0351 - tp: 154549.0000 - fp: 5831.0000 - tn: 157964.0000 - fn: 9336.0000 - accuracy: 0.9537 - precision: 0.9636 - recall: 0.9430 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/278 [================>.............] - ETA: 2s - loss: 0.1185 - cross entropy: 0.1185 - Brier score: 0.0350 - tp: 157392.0000 - fp: 5927.0000 - tn: 161008.0000 - fn: 9497.0000 - accuracy: 0.9538 - precision: 0.9637 - recall: 0.9431 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "166/278 [================>.............] - ETA: 2s - loss: 0.1184 - cross entropy: 0.1184 - Brier score: 0.0350 - tp: 160310.0000 - fp: 6031.0000 - tn: 163975.0000 - fn: 9652.0000 - accuracy: 0.9539 - precision: 0.9637 - recall: 0.9432 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/278 [=================>............] - ETA: 2s - loss: 0.1182 - cross entropy: 0.1182 - Brier score: 0.0350 - tp: 163198.0000 - fp: 6133.0000 - tn: 166960.0000 - fn: 9821.0000 - accuracy: 0.9539 - precision: 0.9638 - recall: 0.9432 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172/278 [=================>............] - ETA: 2s - loss: 0.1182 - cross entropy: 0.1182 - Brier score: 0.0349 - tp: 166154.0000 - fp: 6227.0000 - tn: 169874.0000 - fn: 10001.0000 - accuracy: 0.9539 - precision: 0.9639 - recall: 0.9432 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/278 [=================>............] - ETA: 1s - loss: 0.1182 - cross entropy: 0.1182 - Brier score: 0.0350 - tp: 169011.0000 - fp: 6343.0000 - tn: 172855.0000 - fn: 10191.0000 - accuracy: 0.9539 - precision: 0.9638 - recall: 0.9431 - auc: 0.9920 - prc: 0.9925"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/278 [==================>...........] - ETA: 1s - loss: 0.1181 - cross entropy: 0.1181 - Brier score: 0.0349 - tp: 171878.0000 - fp: 6438.0000 - tn: 175872.0000 - fn: 10356.0000 - accuracy: 0.9539 - precision: 0.9639 - recall: 0.9432 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/278 [==================>...........] - ETA: 1s - loss: 0.1180 - cross entropy: 0.1180 - Brier score: 0.0349 - tp: 174839.0000 - fp: 6532.0000 - tn: 178791.0000 - fn: 10526.0000 - accuracy: 0.9540 - precision: 0.9640 - recall: 0.9432 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/278 [==================>...........] - ETA: 1s - loss: 0.1180 - cross entropy: 0.1180 - Brier score: 0.0349 - tp: 177755.0000 - fp: 6645.0000 - tn: 181720.0000 - fn: 10712.0000 - accuracy: 0.9539 - precision: 0.9640 - recall: 0.9432 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "187/278 [===================>..........] - ETA: 1s - loss: 0.1179 - cross entropy: 0.1179 - Brier score: 0.0349 - tp: 180628.0000 - fp: 6754.0000 - tn: 184692.0000 - fn: 10902.0000 - accuracy: 0.9539 - precision: 0.9640 - recall: 0.9431 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/278 [===================>..........] - ETA: 1s - loss: 0.1179 - cross entropy: 0.1179 - Brier score: 0.0349 - tp: 183557.0000 - fp: 6864.0000 - tn: 187628.0000 - fn: 11071.0000 - accuracy: 0.9539 - precision: 0.9640 - recall: 0.9431 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/278 [===================>..........] - ETA: 1s - loss: 0.1178 - cross entropy: 0.1178 - Brier score: 0.0349 - tp: 186527.0000 - fp: 6958.0000 - tn: 190524.0000 - fn: 11255.0000 - accuracy: 0.9539 - precision: 0.9640 - recall: 0.9431 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/278 [====================>.........] - ETA: 1s - loss: 0.1176 - cross entropy: 0.1176 - Brier score: 0.0348 - tp: 189443.0000 - fp: 7043.0000 - tn: 193502.0000 - fn: 11420.0000 - accuracy: 0.9540 - precision: 0.9642 - recall: 0.9431 - auc: 0.9921 - prc: 0.9926"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/278 [====================>.........] - ETA: 1s - loss: 0.1175 - cross entropy: 0.1175 - Brier score: 0.0348 - tp: 192346.0000 - fp: 7143.0000 - tn: 196471.0000 - fn: 11592.0000 - accuracy: 0.9540 - precision: 0.9642 - recall: 0.9432 - auc: 0.9921 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/278 [====================>.........] - ETA: 1s - loss: 0.1173 - cross entropy: 0.1173 - Brier score: 0.0348 - tp: 195281.0000 - fp: 7256.0000 - tn: 199406.0000 - fn: 11753.0000 - accuracy: 0.9541 - precision: 0.9642 - recall: 0.9432 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "205/278 [=====================>........] - ETA: 1s - loss: 0.1173 - cross entropy: 0.1173 - Brier score: 0.0347 - tp: 198128.0000 - fp: 7342.0000 - tn: 202457.0000 - fn: 11913.0000 - accuracy: 0.9541 - precision: 0.9643 - recall: 0.9433 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/278 [=====================>........] - ETA: 1s - loss: 0.1172 - cross entropy: 0.1172 - Brier score: 0.0347 - tp: 200079.0000 - fp: 7413.0000 - tn: 204404.0000 - fn: 12040.0000 - accuracy: 0.9541 - precision: 0.9643 - recall: 0.9432 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/278 [=====================>........] - ETA: 1s - loss: 0.1171 - cross entropy: 0.1171 - Brier score: 0.0347 - tp: 203008.0000 - fp: 7508.0000 - tn: 207356.0000 - fn: 12208.0000 - accuracy: 0.9542 - precision: 0.9643 - recall: 0.9433 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/278 [=====================>........] - ETA: 1s - loss: 0.1170 - cross entropy: 0.1170 - Brier score: 0.0347 - tp: 205952.0000 - fp: 7618.0000 - tn: 210280.0000 - fn: 12374.0000 - accuracy: 0.9542 - precision: 0.9643 - recall: 0.9433 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "216/278 [======================>.......] - ETA: 1s - loss: 0.1169 - cross entropy: 0.1169 - Brier score: 0.0347 - tp: 208837.0000 - fp: 7713.0000 - tn: 213276.0000 - fn: 12542.0000 - accuracy: 0.9542 - precision: 0.9644 - recall: 0.9433 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "219/278 [======================>.......] - ETA: 1s - loss: 0.1168 - cross entropy: 0.1168 - Brier score: 0.0346 - tp: 211752.0000 - fp: 7813.0000 - tn: 216221.0000 - fn: 12726.0000 - accuracy: 0.9542 - precision: 0.9644 - recall: 0.9433 - auc: 0.9922 - prc: 0.9927"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/278 [======================>.......] - ETA: 1s - loss: 0.1167 - cross entropy: 0.1167 - Brier score: 0.0346 - tp: 214664.0000 - fp: 7913.0000 - tn: 219182.0000 - fn: 12897.0000 - accuracy: 0.9542 - precision: 0.9644 - recall: 0.9433 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/278 [=======================>......] - ETA: 1s - loss: 0.1166 - cross entropy: 0.1166 - Brier score: 0.0346 - tp: 217575.0000 - fp: 8010.0000 - tn: 222148.0000 - fn: 13067.0000 - accuracy: 0.9543 - precision: 0.9645 - recall: 0.9433 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "228/278 [=======================>......] - ETA: 1s - loss: 0.1165 - cross entropy: 0.1165 - Brier score: 0.0345 - tp: 220501.0000 - fp: 8121.0000 - tn: 225090.0000 - fn: 13232.0000 - accuracy: 0.9543 - precision: 0.9645 - recall: 0.9434 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "231/278 [=======================>......] - ETA: 0s - loss: 0.1163 - cross entropy: 0.1163 - Brier score: 0.0345 - tp: 223440.0000 - fp: 8207.0000 - tn: 228041.0000 - fn: 13400.0000 - accuracy: 0.9543 - precision: 0.9646 - recall: 0.9434 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "234/278 [========================>.....] - ETA: 0s - loss: 0.1163 - cross entropy: 0.1163 - Brier score: 0.0345 - tp: 226329.0000 - fp: 8286.0000 - tn: 231035.0000 - fn: 13582.0000 - accuracy: 0.9544 - precision: 0.9647 - recall: 0.9434 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/278 [========================>.....] - ETA: 0s - loss: 0.1162 - cross entropy: 0.1162 - Brier score: 0.0344 - tp: 229180.0000 - fp: 8378.0000 - tn: 234067.0000 - fn: 13751.0000 - accuracy: 0.9544 - precision: 0.9647 - recall: 0.9434 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/278 [========================>.....] - ETA: 0s - loss: 0.1162 - cross entropy: 0.1162 - Brier score: 0.0344 - tp: 231155.0000 - fp: 8438.0000 - tn: 236000.0000 - fn: 13879.0000 - accuracy: 0.9544 - precision: 0.9648 - recall: 0.9434 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/278 [=========================>....] - ETA: 0s - loss: 0.1162 - cross entropy: 0.1162 - Brier score: 0.0344 - tp: 233105.0000 - fp: 8514.0000 - tn: 237974.0000 - fn: 13975.0000 - accuracy: 0.9544 - precision: 0.9648 - recall: 0.9434 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/278 [=========================>....] - ETA: 0s - loss: 0.1161 - cross entropy: 0.1161 - Brier score: 0.0344 - tp: 235055.0000 - fp: 8580.0000 - tn: 239942.0000 - fn: 14087.0000 - accuracy: 0.9545 - precision: 0.9648 - recall: 0.9435 - auc: 0.9923 - prc: 0.9928"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/278 [=========================>....] - ETA: 0s - loss: 0.1160 - cross entropy: 0.1160 - Brier score: 0.0344 - tp: 238013.0000 - fp: 8685.0000 - tn: 242859.0000 - fn: 14251.0000 - accuracy: 0.9545 - precision: 0.9648 - recall: 0.9435 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/278 [=========================>....] - ETA: 0s - loss: 0.1159 - cross entropy: 0.1159 - Brier score: 0.0344 - tp: 240931.0000 - fp: 8780.0000 - tn: 245829.0000 - fn: 14412.0000 - accuracy: 0.9545 - precision: 0.9648 - recall: 0.9436 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/278 [==========================>...] - ETA: 0s - loss: 0.1158 - cross entropy: 0.1158 - Brier score: 0.0344 - tp: 243772.0000 - fp: 8892.0000 - tn: 248841.0000 - fn: 14591.0000 - accuracy: 0.9545 - precision: 0.9648 - recall: 0.9435 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/278 [==========================>...] - ETA: 0s - loss: 0.1158 - cross entropy: 0.1158 - Brier score: 0.0343 - tp: 246699.0000 - fp: 8986.0000 - tn: 251802.0000 - fn: 14753.0000 - accuracy: 0.9545 - precision: 0.9649 - recall: 0.9436 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "258/278 [==========================>...] - ETA: 0s - loss: 0.1157 - cross entropy: 0.1157 - Brier score: 0.0343 - tp: 249653.0000 - fp: 9084.0000 - tn: 254736.0000 - fn: 14911.0000 - accuracy: 0.9546 - precision: 0.9649 - recall: 0.9436 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "261/278 [===========================>..] - ETA: 0s - loss: 0.1156 - cross entropy: 0.1156 - Brier score: 0.0343 - tp: 252564.0000 - fp: 9199.0000 - tn: 257711.0000 - fn: 15054.0000 - accuracy: 0.9546 - precision: 0.9649 - recall: 0.9437 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "264/278 [===========================>..] - ETA: 0s - loss: 0.1155 - cross entropy: 0.1155 - Brier score: 0.0343 - tp: 255481.0000 - fp: 9283.0000 - tn: 260685.0000 - fn: 15223.0000 - accuracy: 0.9547 - precision: 0.9649 - recall: 0.9438 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/278 [===========================>..] - ETA: 0s - loss: 0.1155 - cross entropy: 0.1155 - Brier score: 0.0342 - tp: 258456.0000 - fp: 9377.0000 - tn: 263605.0000 - fn: 15378.0000 - accuracy: 0.9547 - precision: 0.9650 - recall: 0.9438 - auc: 0.9924 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/278 [============================>.] - ETA: 0s - loss: 0.1154 - cross entropy: 0.1154 - Brier score: 0.0342 - tp: 261376.0000 - fp: 9466.0000 - tn: 266571.0000 - fn: 15547.0000 - accuracy: 0.9548 - precision: 0.9650 - recall: 0.9439 - auc: 0.9925 - prc: 0.9929"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "273/278 [============================>.] - ETA: 0s - loss: 0.1152 - cross entropy: 0.1152 - Brier score: 0.0342 - tp: 264283.0000 - fp: 9545.0000 - tn: 269569.0000 - fn: 15707.0000 - accuracy: 0.9548 - precision: 0.9651 - recall: 0.9439 - auc: 0.9925 - prc: 0.9930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "276/278 [============================>.] - ETA: 0s - loss: 0.1152 - cross entropy: 0.1152 - Brier score: 0.0341 - tp: 267219.0000 - fp: 9646.0000 - tn: 272500.0000 - fn: 15883.0000 - accuracy: 0.9548 - precision: 0.9652 - recall: 0.9439 - auc: 0.9925 - prc: 0.9930"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 6s 21ms/step - loss: 0.1151 - cross entropy: 0.1151 - Brier score: 0.0341 - tp: 269190.0000 - fp: 9719.0000 - tn: 274441.0000 - fn: 15994.0000 - accuracy: 0.9548 - precision: 0.9652 - recall: 0.9439 - auc: 0.9925 - prc: 0.9930 - val_loss: 0.0596 - val_cross entropy: 0.0596 - val_Brier score: 0.0136 - val_tp: 76.0000 - val_fp: 725.0000 - val_tn: 44762.0000 - val_fn: 6.0000 - val_accuracy: 0.9840 - val_precision: 0.0949 - val_recall: 0.9268 - val_auc: 0.9726 - val_prc: 0.7292\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.1034 - cross entropy: 0.1034 - Brier score: 0.0322 - tp: 987.0000 - fp: 33.0000 - tn: 963.0000 - fn: 65.0000 - accuracy: 0.9521 - precision: 0.9676 - recall: 0.9382 - auc: 0.9937 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.1066 - cross entropy: 0.1066 - Brier score: 0.0322 - tp: 4927.0000 - fp: 187.0000 - tn: 4861.0000 - fn: 265.0000 - accuracy: 0.9559 - precision: 0.9634 - recall: 0.9490 - auc: 0.9936 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0318 - tp: 7781.0000 - fp: 281.0000 - tn: 7895.0000 - fn: 427.0000 - accuracy: 0.9568 - precision: 0.9651 - recall: 0.9480 - auc: 0.9938 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.1077 - cross entropy: 0.1077 - Brier score: 0.0324 - tp: 10678.0000 - fp: 384.0000 - tn: 10857.0000 - fn: 609.0000 - accuracy: 0.9559 - precision: 0.9653 - recall: 0.9460 - auc: 0.9935 - prc: 0.9938"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.1065 - cross entropy: 0.1065 - Brier score: 0.0321 - tp: 13582.0000 - fp: 465.0000 - tn: 13843.0000 - fn: 782.0000 - accuracy: 0.9565 - precision: 0.9669 - recall: 0.9456 - auc: 0.9936 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.1060 - cross entropy: 0.1060 - Brier score: 0.0318 - tp: 16524.0000 - fp: 557.0000 - tn: 16803.0000 - fn: 932.0000 - accuracy: 0.9572 - precision: 0.9674 - recall: 0.9466 - auc: 0.9938 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.1059 - cross entropy: 0.1059 - Brier score: 0.0319 - tp: 19454.0000 - fp: 660.0000 - tn: 19743.0000 - fn: 1103.0000 - accuracy: 0.9570 - precision: 0.9672 - recall: 0.9463 - auc: 0.9937 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.1062 - cross entropy: 0.1062 - Brier score: 0.0320 - tp: 22383.0000 - fp: 771.0000 - tn: 22686.0000 - fn: 1264.0000 - accuracy: 0.9568 - precision: 0.9667 - recall: 0.9465 - auc: 0.9937 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0318 - tp: 25294.0000 - fp: 856.0000 - tn: 25654.0000 - fn: 1444.0000 - accuracy: 0.9568 - precision: 0.9673 - recall: 0.9460 - auc: 0.9938 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.1058 - cross entropy: 0.1058 - Brier score: 0.0319 - tp: 28160.0000 - fp: 978.0000 - tn: 28651.0000 - fn: 1603.0000 - accuracy: 0.9565 - precision: 0.9664 - recall: 0.9461 - auc: 0.9937 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.1061 - cross entropy: 0.1061 - Brier score: 0.0320 - tp: 31070.0000 - fp: 1093.0000 - tn: 31603.0000 - fn: 1770.0000 - accuracy: 0.9563 - precision: 0.9660 - recall: 0.9461 - auc: 0.9937 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.1058 - cross entropy: 0.1058 - Brier score: 0.0319 - tp: 33986.0000 - fp: 1185.0000 - tn: 34584.0000 - fn: 1925.0000 - accuracy: 0.9566 - precision: 0.9663 - recall: 0.9464 - auc: 0.9937 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0318 - tp: 36867.0000 - fp: 1280.0000 - tn: 37601.0000 - fn: 2076.0000 - accuracy: 0.9569 - precision: 0.9664 - recall: 0.9467 - auc: 0.9937 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.1051 - cross entropy: 0.1051 - Brier score: 0.0317 - tp: 39744.0000 - fp: 1379.0000 - tn: 40628.0000 - fn: 2217.0000 - accuracy: 0.9572 - precision: 0.9665 - recall: 0.9472 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.1051 - cross entropy: 0.1051 - Brier score: 0.0317 - tp: 42599.0000 - fp: 1475.0000 - tn: 43661.0000 - fn: 2377.0000 - accuracy: 0.9573 - precision: 0.9665 - recall: 0.9471 - auc: 0.9938 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.1049 - cross entropy: 0.1049 - Brier score: 0.0316 - tp: 45539.0000 - fp: 1565.0000 - tn: 46609.0000 - fn: 2543.0000 - accuracy: 0.9573 - precision: 0.9668 - recall: 0.9471 - auc: 0.9938 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0316 - tp: 48465.0000 - fp: 1677.0000 - tn: 49552.0000 - fn: 2706.0000 - accuracy: 0.9572 - precision: 0.9666 - recall: 0.9471 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.1060 - cross entropy: 0.1060 - Brier score: 0.0317 - tp: 51322.0000 - fp: 1781.0000 - tn: 52571.0000 - fn: 2870.0000 - accuracy: 0.9572 - precision: 0.9665 - recall: 0.9470 - auc: 0.9938 - prc: 0.9939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.1059 - cross entropy: 0.1059 - Brier score: 0.0317 - tp: 54194.0000 - fp: 1873.0000 - tn: 55598.0000 - fn: 3023.0000 - accuracy: 0.9573 - precision: 0.9666 - recall: 0.9472 - auc: 0.9938 - prc: 0.9939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.1058 - cross entropy: 0.1058 - Brier score: 0.0316 - tp: 57119.0000 - fp: 1988.0000 - tn: 58554.0000 - fn: 3171.0000 - accuracy: 0.9573 - precision: 0.9664 - recall: 0.9474 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.1058 - cross entropy: 0.1058 - Brier score: 0.0316 - tp: 59982.0000 - fp: 2087.0000 - tn: 61569.0000 - fn: 3338.0000 - accuracy: 0.9573 - precision: 0.9664 - recall: 0.9473 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.1057 - cross entropy: 0.1057 - Brier score: 0.0316 - tp: 62901.0000 - fp: 2188.0000 - tn: 64538.0000 - fn: 3493.0000 - accuracy: 0.9573 - precision: 0.9664 - recall: 0.9474 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 4s - loss: 0.1057 - cross entropy: 0.1057 - Brier score: 0.0316 - tp: 65793.0000 - fp: 2281.0000 - tn: 67540.0000 - fn: 3650.0000 - accuracy: 0.9574 - precision: 0.9665 - recall: 0.9474 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 4s - loss: 0.1057 - cross entropy: 0.1057 - Brier score: 0.0316 - tp: 68741.0000 - fp: 2377.0000 - tn: 70469.0000 - fn: 3821.0000 - accuracy: 0.9574 - precision: 0.9666 - recall: 0.9473 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.1056 - cross entropy: 0.1056 - Brier score: 0.0316 - tp: 71631.0000 - fp: 2478.0000 - tn: 73460.0000 - fn: 3983.0000 - accuracy: 0.9574 - precision: 0.9666 - recall: 0.9473 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.1055 - cross entropy: 0.1055 - Brier score: 0.0316 - tp: 74543.0000 - fp: 2573.0000 - tn: 76433.0000 - fn: 4147.0000 - accuracy: 0.9574 - precision: 0.9666 - recall: 0.9473 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0315 - tp: 77445.0000 - fp: 2668.0000 - tn: 79432.0000 - fn: 4295.0000 - accuracy: 0.9575 - precision: 0.9667 - recall: 0.9475 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.1057 - cross entropy: 0.1057 - Brier score: 0.0316 - tp: 80334.0000 - fp: 2774.0000 - tn: 82402.0000 - fn: 4474.0000 - accuracy: 0.9574 - precision: 0.9666 - recall: 0.9472 - auc: 0.9938 - prc: 0.9939"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0315 - tp: 83315.0000 - fp: 2864.0000 - tn: 85319.0000 - fn: 4630.0000 - accuracy: 0.9575 - precision: 0.9668 - recall: 0.9474 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0315 - tp: 86229.0000 - fp: 2968.0000 - tn: 88302.0000 - fn: 4773.0000 - accuracy: 0.9575 - precision: 0.9667 - recall: 0.9476 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0315 - tp: 89106.0000 - fp: 3068.0000 - tn: 91301.0000 - fn: 4941.0000 - accuracy: 0.9575 - precision: 0.9667 - recall: 0.9475 - auc: 0.9938 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.1052 - cross entropy: 0.1052 - Brier score: 0.0314 - tp: 91959.0000 - fp: 3162.0000 - tn: 94347.0000 - fn: 5092.0000 - accuracy: 0.9576 - precision: 0.9668 - recall: 0.9475 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.1054 - cross entropy: 0.1054 - Brier score: 0.0314 - tp: 94825.0000 - fp: 3262.0000 - tn: 97376.0000 - fn: 5241.0000 - accuracy: 0.9576 - precision: 0.9667 - recall: 0.9476 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.1052 - cross entropy: 0.1052 - Brier score: 0.0314 - tp: 97814.0000 - fp: 3367.0000 - tn: 100277.0000 - fn: 5390.0000 - accuracy: 0.9577 - precision: 0.9667 - recall: 0.9478 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/278 [==========>...................] - ETA: 3s - loss: 0.1052 - cross entropy: 0.1052 - Brier score: 0.0313 - tp: 100744.0000 - fp: 3467.0000 - tn: 103246.0000 - fn: 5535.0000 - accuracy: 0.9577 - precision: 0.9667 - recall: 0.9479 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/278 [==========>...................] - ETA: 3s - loss: 0.1052 - cross entropy: 0.1052 - Brier score: 0.0313 - tp: 103675.0000 - fp: 3578.0000 - tn: 106194.0000 - fn: 5689.0000 - accuracy: 0.9577 - precision: 0.9666 - recall: 0.9480 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/278 [==========>...................] - ETA: 3s - loss: 0.1049 - cross entropy: 0.1049 - Brier score: 0.0313 - tp: 106644.0000 - fp: 3653.0000 - tn: 109153.0000 - fn: 5830.0000 - accuracy: 0.9579 - precision: 0.9669 - recall: 0.9482 - auc: 0.9939 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/278 [===========>..................] - ETA: 3s - loss: 0.1049 - cross entropy: 0.1049 - Brier score: 0.0312 - tp: 109579.0000 - fp: 3753.0000 - tn: 112093.0000 - fn: 5999.0000 - accuracy: 0.9579 - precision: 0.9669 - recall: 0.9481 - auc: 0.9939 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/278 [===========>..................] - ETA: 3s - loss: 0.1050 - cross entropy: 0.1050 - Brier score: 0.0313 - tp: 112465.0000 - fp: 3854.0000 - tn: 115098.0000 - fn: 6151.0000 - accuracy: 0.9579 - precision: 0.9669 - recall: 0.9481 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/278 [===========>..................] - ETA: 3s - loss: 0.1051 - cross entropy: 0.1051 - Brier score: 0.0313 - tp: 115387.0000 - fp: 3959.0000 - tn: 118055.0000 - fn: 6311.0000 - accuracy: 0.9579 - precision: 0.9668 - recall: 0.9481 - auc: 0.9939 - prc: 0.9940"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/278 [============>.................] - ETA: 3s - loss: 0.1049 - cross entropy: 0.1049 - Brier score: 0.0312 - tp: 118280.0000 - fp: 4050.0000 - tn: 121090.0000 - fn: 6436.0000 - accuracy: 0.9580 - precision: 0.9669 - recall: 0.9484 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/278 [============>.................] - ETA: 3s - loss: 0.1048 - cross entropy: 0.1048 - Brier score: 0.0312 - tp: 121187.0000 - fp: 4140.0000 - tn: 124087.0000 - fn: 6586.0000 - accuracy: 0.9581 - precision: 0.9670 - recall: 0.9485 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/278 [============>.................] - ETA: 2s - loss: 0.1048 - cross entropy: 0.1048 - Brier score: 0.0312 - tp: 124083.0000 - fp: 4247.0000 - tn: 127075.0000 - fn: 6739.0000 - accuracy: 0.9581 - precision: 0.9669 - recall: 0.9485 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/278 [=============>................] - ETA: 2s - loss: 0.1047 - cross entropy: 0.1047 - Brier score: 0.0312 - tp: 126969.0000 - fp: 4340.0000 - tn: 130095.0000 - fn: 6884.0000 - accuracy: 0.9582 - precision: 0.9669 - recall: 0.9486 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/278 [=============>................] - ETA: 2s - loss: 0.1046 - cross entropy: 0.1046 - Brier score: 0.0311 - tp: 129872.0000 - fp: 4436.0000 - tn: 133088.0000 - fn: 7036.0000 - accuracy: 0.9582 - precision: 0.9670 - recall: 0.9486 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/278 [=============>................] - ETA: 2s - loss: 0.1044 - cross entropy: 0.1044 - Brier score: 0.0311 - tp: 132828.0000 - fp: 4524.0000 - tn: 136048.0000 - fn: 7176.0000 - accuracy: 0.9583 - precision: 0.9671 - recall: 0.9487 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/278 [==============>...............] - ETA: 2s - loss: 0.1044 - cross entropy: 0.1044 - Brier score: 0.0311 - tp: 135753.0000 - fp: 4637.0000 - tn: 139017.0000 - fn: 7313.0000 - accuracy: 0.9583 - precision: 0.9670 - recall: 0.9489 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/278 [==============>...............] - ETA: 2s - loss: 0.1042 - cross entropy: 0.1042 - Brier score: 0.0310 - tp: 138708.0000 - fp: 4708.0000 - tn: 141993.0000 - fn: 7455.0000 - accuracy: 0.9585 - precision: 0.9672 - recall: 0.9490 - auc: 0.9940 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/278 [==============>...............] - ETA: 2s - loss: 0.1042 - cross entropy: 0.1042 - Brier score: 0.0310 - tp: 141589.0000 - fp: 4809.0000 - tn: 145016.0000 - fn: 7594.0000 - accuracy: 0.9585 - precision: 0.9672 - recall: 0.9491 - auc: 0.9941 - prc: 0.9941"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/278 [===============>..............] - ETA: 2s - loss: 0.1040 - cross entropy: 0.1040 - Brier score: 0.0310 - tp: 144521.0000 - fp: 4894.0000 - tn: 147992.0000 - fn: 7745.0000 - accuracy: 0.9586 - precision: 0.9672 - recall: 0.9491 - auc: 0.9941 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/278 [===============>..............] - ETA: 2s - loss: 0.1038 - cross entropy: 0.1038 - Brier score: 0.0309 - tp: 147448.0000 - fp: 4977.0000 - tn: 150996.0000 - fn: 7875.0000 - accuracy: 0.9587 - precision: 0.9673 - recall: 0.9493 - auc: 0.9941 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/278 [===============>..............] - ETA: 2s - loss: 0.1038 - cross entropy: 0.1038 - Brier score: 0.0309 - tp: 150347.0000 - fp: 5076.0000 - tn: 153992.0000 - fn: 8025.0000 - accuracy: 0.9587 - precision: 0.9673 - recall: 0.9493 - auc: 0.9941 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/278 [================>.............] - ETA: 2s - loss: 0.1037 - cross entropy: 0.1037 - Brier score: 0.0309 - tp: 153261.0000 - fp: 5177.0000 - tn: 156981.0000 - fn: 8165.0000 - accuracy: 0.9588 - precision: 0.9673 - recall: 0.9494 - auc: 0.9941 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/278 [================>.............] - ETA: 2s - loss: 0.1037 - cross entropy: 0.1037 - Brier score: 0.0309 - tp: 156222.0000 - fp: 5287.0000 - tn: 159921.0000 - fn: 8298.0000 - accuracy: 0.9588 - precision: 0.9673 - recall: 0.9496 - auc: 0.9941 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/278 [================>.............] - ETA: 2s - loss: 0.1035 - cross entropy: 0.1035 - Brier score: 0.0308 - tp: 159145.0000 - fp: 5363.0000 - tn: 162915.0000 - fn: 8449.0000 - accuracy: 0.9589 - precision: 0.9674 - recall: 0.9496 - auc: 0.9941 - prc: 0.9942"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/278 [=================>............] - ETA: 2s - loss: 0.1034 - cross entropy: 0.1034 - Brier score: 0.0308 - tp: 162103.0000 - fp: 5458.0000 - tn: 165891.0000 - fn: 8564.0000 - accuracy: 0.9590 - precision: 0.9674 - recall: 0.9498 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/278 [=================>............] - ETA: 2s - loss: 0.1033 - cross entropy: 0.1033 - Brier score: 0.0307 - tp: 165001.0000 - fp: 5556.0000 - tn: 168898.0000 - fn: 8705.0000 - accuracy: 0.9590 - precision: 0.9674 - recall: 0.9499 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/278 [=================>............] - ETA: 2s - loss: 0.1032 - cross entropy: 0.1032 - Brier score: 0.0307 - tp: 167956.0000 - fp: 5654.0000 - tn: 171848.0000 - fn: 8846.0000 - accuracy: 0.9591 - precision: 0.9674 - recall: 0.9500 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/278 [=================>............] - ETA: 2s - loss: 0.1032 - cross entropy: 0.1032 - Brier score: 0.0307 - tp: 170906.0000 - fp: 5756.0000 - tn: 174801.0000 - fn: 8985.0000 - accuracy: 0.9591 - precision: 0.9674 - recall: 0.9501 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/278 [==================>...........] - ETA: 1s - loss: 0.1033 - cross entropy: 0.1033 - Brier score: 0.0307 - tp: 173862.0000 - fp: 5860.0000 - tn: 177746.0000 - fn: 9124.0000 - accuracy: 0.9591 - precision: 0.9674 - recall: 0.9501 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/278 [==================>...........] - ETA: 1s - loss: 0.1034 - cross entropy: 0.1034 - Brier score: 0.0307 - tp: 176815.0000 - fp: 5971.0000 - tn: 180695.0000 - fn: 9255.0000 - accuracy: 0.9592 - precision: 0.9673 - recall: 0.9503 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/278 [==================>...........] - ETA: 1s - loss: 0.1033 - cross entropy: 0.1033 - Brier score: 0.0307 - tp: 179734.0000 - fp: 6061.0000 - tn: 183668.0000 - fn: 9417.0000 - accuracy: 0.9591 - precision: 0.9674 - recall: 0.9502 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/278 [===================>..........] - ETA: 1s - loss: 0.1033 - cross entropy: 0.1033 - Brier score: 0.0307 - tp: 182698.0000 - fp: 6144.0000 - tn: 186621.0000 - fn: 9561.0000 - accuracy: 0.9592 - precision: 0.9675 - recall: 0.9503 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/278 [===================>..........] - ETA: 1s - loss: 0.1031 - cross entropy: 0.1031 - Brier score: 0.0306 - tp: 185632.0000 - fp: 6231.0000 - tn: 189615.0000 - fn: 9690.0000 - accuracy: 0.9593 - precision: 0.9675 - recall: 0.9504 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/278 [===================>..........] - ETA: 1s - loss: 0.1030 - cross entropy: 0.1030 - Brier score: 0.0306 - tp: 188658.0000 - fp: 6331.0000 - tn: 192491.0000 - fn: 9832.0000 - accuracy: 0.9593 - precision: 0.9675 - recall: 0.9505 - auc: 0.9942 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/278 [====================>.........] - ETA: 1s - loss: 0.1029 - cross entropy: 0.1029 - Brier score: 0.0306 - tp: 191594.0000 - fp: 6418.0000 - tn: 195484.0000 - fn: 9960.0000 - accuracy: 0.9594 - precision: 0.9676 - recall: 0.9506 - auc: 0.9943 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/278 [====================>.........] - ETA: 1s - loss: 0.1028 - cross entropy: 0.1028 - Brier score: 0.0305 - tp: 194467.0000 - fp: 6508.0000 - tn: 198533.0000 - fn: 10092.0000 - accuracy: 0.9595 - precision: 0.9676 - recall: 0.9507 - auc: 0.9943 - prc: 0.9943"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/278 [====================>.........] - ETA: 1s - loss: 0.1027 - cross entropy: 0.1027 - Brier score: 0.0305 - tp: 197393.0000 - fp: 6606.0000 - tn: 201522.0000 - fn: 10223.0000 - accuracy: 0.9595 - precision: 0.9676 - recall: 0.9508 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/278 [=====================>........] - ETA: 1s - loss: 0.1026 - cross entropy: 0.1026 - Brier score: 0.0305 - tp: 200326.0000 - fp: 6699.0000 - tn: 204495.0000 - fn: 10368.0000 - accuracy: 0.9595 - precision: 0.9676 - recall: 0.9508 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/278 [=====================>........] - ETA: 1s - loss: 0.1026 - cross entropy: 0.1026 - Brier score: 0.0304 - tp: 203198.0000 - fp: 6806.0000 - tn: 207522.0000 - fn: 10506.0000 - accuracy: 0.9596 - precision: 0.9676 - recall: 0.9508 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/278 [=====================>........] - ETA: 1s - loss: 0.1025 - cross entropy: 0.1025 - Brier score: 0.0304 - tp: 206116.0000 - fp: 6907.0000 - tn: 210501.0000 - fn: 10652.0000 - accuracy: 0.9596 - precision: 0.9676 - recall: 0.9509 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "215/278 [======================>.......] - ETA: 1s - loss: 0.1024 - cross entropy: 0.1024 - Brier score: 0.0304 - tp: 209059.0000 - fp: 7000.0000 - tn: 213470.0000 - fn: 10791.0000 - accuracy: 0.9596 - precision: 0.9676 - recall: 0.9509 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/278 [======================>.......] - ETA: 1s - loss: 0.1023 - cross entropy: 0.1023 - Brier score: 0.0304 - tp: 212021.0000 - fp: 7087.0000 - tn: 216447.0000 - fn: 10909.0000 - accuracy: 0.9597 - precision: 0.9677 - recall: 0.9511 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/278 [======================>.......] - ETA: 1s - loss: 0.1022 - cross entropy: 0.1022 - Brier score: 0.0304 - tp: 215010.0000 - fp: 7174.0000 - tn: 219370.0000 - fn: 11054.0000 - accuracy: 0.9597 - precision: 0.9677 - recall: 0.9511 - auc: 0.9943 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/278 [=======================>......] - ETA: 1s - loss: 0.1021 - cross entropy: 0.1021 - Brier score: 0.0303 - tp: 217940.0000 - fp: 7273.0000 - tn: 222353.0000 - fn: 11186.0000 - accuracy: 0.9598 - precision: 0.9677 - recall: 0.9512 - auc: 0.9944 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/278 [=======================>......] - ETA: 0s - loss: 0.1021 - cross entropy: 0.1021 - Brier score: 0.0303 - tp: 220868.0000 - fp: 7370.0000 - tn: 225341.0000 - fn: 11317.0000 - accuracy: 0.9598 - precision: 0.9677 - recall: 0.9513 - auc: 0.9944 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/278 [=======================>......] - ETA: 0s - loss: 0.1020 - cross entropy: 0.1020 - Brier score: 0.0303 - tp: 222831.0000 - fp: 7439.0000 - tn: 227314.0000 - fn: 11408.0000 - accuracy: 0.9598 - precision: 0.9677 - recall: 0.9513 - auc: 0.9944 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/278 [========================>.....] - ETA: 0s - loss: 0.1020 - cross entropy: 0.1020 - Brier score: 0.0303 - tp: 225772.0000 - fp: 7526.0000 - tn: 230306.0000 - fn: 11532.0000 - accuracy: 0.9599 - precision: 0.9677 - recall: 0.9514 - auc: 0.9944 - prc: 0.9944"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/278 [========================>.....] - ETA: 0s - loss: 0.1019 - cross entropy: 0.1019 - Brier score: 0.0303 - tp: 228735.0000 - fp: 7598.0000 - tn: 233264.0000 - fn: 11683.0000 - accuracy: 0.9599 - precision: 0.9679 - recall: 0.9514 - auc: 0.9944 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/278 [========================>.....] - ETA: 0s - loss: 0.1018 - cross entropy: 0.1018 - Brier score: 0.0302 - tp: 231663.0000 - fp: 7687.0000 - tn: 236257.0000 - fn: 11817.0000 - accuracy: 0.9600 - precision: 0.9679 - recall: 0.9515 - auc: 0.9944 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/278 [=========================>....] - ETA: 0s - loss: 0.1017 - cross entropy: 0.1017 - Brier score: 0.0302 - tp: 234583.0000 - fp: 7776.0000 - tn: 239261.0000 - fn: 11948.0000 - accuracy: 0.9600 - precision: 0.9679 - recall: 0.9515 - auc: 0.9944 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/278 [=========================>....] - ETA: 0s - loss: 0.1016 - cross entropy: 0.1016 - Brier score: 0.0302 - tp: 237404.0000 - fp: 7871.0000 - tn: 242355.0000 - fn: 12082.0000 - accuracy: 0.9601 - precision: 0.9679 - recall: 0.9516 - auc: 0.9944 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/278 [=========================>....] - ETA: 0s - loss: 0.1016 - cross entropy: 0.1016 - Brier score: 0.0302 - tp: 240412.0000 - fp: 7951.0000 - tn: 245274.0000 - fn: 12219.0000 - accuracy: 0.9601 - precision: 0.9680 - recall: 0.9516 - auc: 0.9944 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "250/278 [=========================>....] - ETA: 0s - loss: 0.1015 - cross entropy: 0.1015 - Brier score: 0.0301 - tp: 243307.0000 - fp: 8035.0000 - tn: 248304.0000 - fn: 12354.0000 - accuracy: 0.9602 - precision: 0.9680 - recall: 0.9517 - auc: 0.9944 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/278 [==========================>...] - ETA: 0s - loss: 0.1014 - cross entropy: 0.1014 - Brier score: 0.0301 - tp: 246258.0000 - fp: 8125.0000 - tn: 251280.0000 - fn: 12481.0000 - accuracy: 0.9602 - precision: 0.9681 - recall: 0.9518 - auc: 0.9945 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/278 [==========================>...] - ETA: 0s - loss: 0.1013 - cross entropy: 0.1013 - Brier score: 0.0301 - tp: 249251.0000 - fp: 8209.0000 - tn: 254220.0000 - fn: 12608.0000 - accuracy: 0.9603 - precision: 0.9681 - recall: 0.9519 - auc: 0.9945 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "259/278 [==========================>...] - ETA: 0s - loss: 0.1011 - cross entropy: 0.1011 - Brier score: 0.0300 - tp: 252277.0000 - fp: 8288.0000 - tn: 257128.0000 - fn: 12739.0000 - accuracy: 0.9604 - precision: 0.9682 - recall: 0.9519 - auc: 0.9945 - prc: 0.9945"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "262/278 [===========================>..] - ETA: 0s - loss: 0.1010 - cross entropy: 0.1010 - Brier score: 0.0300 - tp: 255309.0000 - fp: 8374.0000 - tn: 260003.0000 - fn: 12890.0000 - accuracy: 0.9604 - precision: 0.9682 - recall: 0.9519 - auc: 0.9945 - prc: 0.9946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "265/278 [===========================>..] - ETA: 0s - loss: 0.1009 - cross entropy: 0.1009 - Brier score: 0.0300 - tp: 258215.0000 - fp: 8444.0000 - tn: 263038.0000 - fn: 13023.0000 - accuracy: 0.9604 - precision: 0.9683 - recall: 0.9520 - auc: 0.9945 - prc: 0.9946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "268/278 [===========================>..] - ETA: 0s - loss: 0.1008 - cross entropy: 0.1008 - Brier score: 0.0300 - tp: 261129.0000 - fp: 8546.0000 - tn: 266015.0000 - fn: 13174.0000 - accuracy: 0.9604 - precision: 0.9683 - recall: 0.9520 - auc: 0.9945 - prc: 0.9946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "271/278 [============================>.] - ETA: 0s - loss: 0.1008 - cross entropy: 0.1008 - Brier score: 0.0300 - tp: 264123.0000 - fp: 8657.0000 - tn: 268911.0000 - fn: 13317.0000 - accuracy: 0.9604 - precision: 0.9683 - recall: 0.9520 - auc: 0.9945 - prc: 0.9946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "274/278 [============================>.] - ETA: 0s - loss: 0.1007 - cross entropy: 0.1007 - Brier score: 0.0299 - tp: 267033.0000 - fp: 8741.0000 - tn: 271936.0000 - fn: 13442.0000 - accuracy: 0.9605 - precision: 0.9683 - recall: 0.9521 - auc: 0.9945 - prc: 0.9946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "277/278 [============================>.] - ETA: 0s - loss: 0.1006 - cross entropy: 0.1006 - Brier score: 0.0299 - tp: 269972.0000 - fp: 8824.0000 - tn: 274925.0000 - fn: 13575.0000 - accuracy: 0.9605 - precision: 0.9683 - recall: 0.9521 - auc: 0.9945 - prc: 0.9946"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 6s 20ms/step - loss: 0.1006 - cross entropy: 0.1006 - Brier score: 0.0299 - tp: 270949.0000 - fp: 8853.0000 - tn: 275916.0000 - fn: 13626.0000 - accuracy: 0.9605 - precision: 0.9684 - recall: 0.9521 - auc: 0.9945 - prc: 0.9946 - val_loss: 0.0525 - val_cross entropy: 0.0525 - val_Brier score: 0.0124 - val_tp: 76.0000 - val_fp: 668.0000 - val_tn: 44819.0000 - val_fn: 6.0000 - val_accuracy: 0.9852 - val_precision: 0.1022 - val_recall: 0.9268 - val_auc: 0.9717 - val_prc: 0.7216\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.0965 - cross entropy: 0.0965 - Brier score: 0.0296 - tp: 972.0000 - fp: 32.0000 - tn: 992.0000 - fn: 52.0000 - accuracy: 0.9590 - precision: 0.9681 - recall: 0.9492 - auc: 0.9948 - prc: 0.9950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.0972 - cross entropy: 0.0972 - Brier score: 0.0286 - tp: 4875.0000 - fp: 159.0000 - tn: 4960.0000 - fn: 246.0000 - accuracy: 0.9604 - precision: 0.9684 - recall: 0.9520 - auc: 0.9951 - prc: 0.9950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.0964 - cross entropy: 0.0964 - Brier score: 0.0283 - tp: 7828.0000 - fp: 242.0000 - tn: 7923.0000 - fn: 391.0000 - accuracy: 0.9614 - precision: 0.9700 - recall: 0.9524 - auc: 0.9952 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.0964 - cross entropy: 0.0964 - Brier score: 0.0284 - tp: 10805.0000 - fp: 345.0000 - tn: 10860.0000 - fn: 518.0000 - accuracy: 0.9617 - precision: 0.9691 - recall: 0.9543 - auc: 0.9951 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.0956 - cross entropy: 0.0956 - Brier score: 0.0282 - tp: 13749.0000 - fp: 441.0000 - tn: 13838.0000 - fn: 644.0000 - accuracy: 0.9622 - precision: 0.9689 - recall: 0.9553 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.0948 - cross entropy: 0.0948 - Brier score: 0.0281 - tp: 16754.0000 - fp: 541.0000 - tn: 16742.0000 - fn: 779.0000 - accuracy: 0.9621 - precision: 0.9687 - recall: 0.9556 - auc: 0.9952 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.0948 - cross entropy: 0.0948 - Brier score: 0.0280 - tp: 19629.0000 - fp: 623.0000 - tn: 19792.0000 - fn: 916.0000 - accuracy: 0.9624 - precision: 0.9692 - recall: 0.9554 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.0944 - cross entropy: 0.0944 - Brier score: 0.0280 - tp: 22549.0000 - fp: 712.0000 - tn: 22792.0000 - fn: 1051.0000 - accuracy: 0.9626 - precision: 0.9694 - recall: 0.9555 - auc: 0.9953 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.0945 - cross entropy: 0.0945 - Brier score: 0.0281 - tp: 25514.0000 - fp: 807.0000 - tn: 25735.0000 - fn: 1192.0000 - accuracy: 0.9625 - precision: 0.9693 - recall: 0.9554 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.0946 - cross entropy: 0.0946 - Brier score: 0.0281 - tp: 28421.0000 - fp: 914.0000 - tn: 28727.0000 - fn: 1330.0000 - accuracy: 0.9622 - precision: 0.9688 - recall: 0.9553 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.0945 - cross entropy: 0.0945 - Brier score: 0.0280 - tp: 31320.0000 - fp: 1000.0000 - tn: 31746.0000 - fn: 1470.0000 - accuracy: 0.9623 - precision: 0.9691 - recall: 0.9552 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.0946 - cross entropy: 0.0946 - Brier score: 0.0281 - tp: 34264.0000 - fp: 1107.0000 - tn: 34695.0000 - fn: 1614.0000 - accuracy: 0.9620 - precision: 0.9687 - recall: 0.9550 - auc: 0.9952 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.0951 - cross entropy: 0.0951 - Brier score: 0.0281 - tp: 37243.0000 - fp: 1192.0000 - tn: 37623.0000 - fn: 1766.0000 - accuracy: 0.9620 - precision: 0.9690 - recall: 0.9547 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.0953 - cross entropy: 0.0953 - Brier score: 0.0282 - tp: 40140.0000 - fp: 1297.0000 - tn: 40640.0000 - fn: 1891.0000 - accuracy: 0.9620 - precision: 0.9687 - recall: 0.9550 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.0958 - cross entropy: 0.0958 - Brier score: 0.0283 - tp: 43030.0000 - fp: 1397.0000 - tn: 43660.0000 - fn: 2025.0000 - accuracy: 0.9620 - precision: 0.9686 - recall: 0.9551 - auc: 0.9951 - prc: 0.9950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.0958 - cross entropy: 0.0958 - Brier score: 0.0283 - tp: 45959.0000 - fp: 1504.0000 - tn: 46635.0000 - fn: 2158.0000 - accuracy: 0.9620 - precision: 0.9683 - recall: 0.9552 - auc: 0.9951 - prc: 0.9950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.0953 - cross entropy: 0.0953 - Brier score: 0.0281 - tp: 48898.0000 - fp: 1594.0000 - tn: 49618.0000 - fn: 2290.0000 - accuracy: 0.9621 - precision: 0.9684 - recall: 0.9553 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.0953 - cross entropy: 0.0953 - Brier score: 0.0282 - tp: 51838.0000 - fp: 1695.0000 - tn: 52588.0000 - fn: 2423.0000 - accuracy: 0.9621 - precision: 0.9683 - recall: 0.9553 - auc: 0.9952 - prc: 0.9950"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.0952 - cross entropy: 0.0952 - Brier score: 0.0282 - tp: 54782.0000 - fp: 1785.0000 - tn: 55564.0000 - fn: 2557.0000 - accuracy: 0.9621 - precision: 0.9684 - recall: 0.9554 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.0950 - cross entropy: 0.0950 - Brier score: 0.0281 - tp: 57725.0000 - fp: 1864.0000 - tn: 58563.0000 - fn: 2680.0000 - accuracy: 0.9624 - precision: 0.9687 - recall: 0.9556 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.0951 - cross entropy: 0.0951 - Brier score: 0.0281 - tp: 60672.0000 - fp: 1963.0000 - tn: 61533.0000 - fn: 2808.0000 - accuracy: 0.9624 - precision: 0.9687 - recall: 0.9558 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.0953 - cross entropy: 0.0953 - Brier score: 0.0282 - tp: 63553.0000 - fp: 2059.0000 - tn: 64563.0000 - fn: 2945.0000 - accuracy: 0.9624 - precision: 0.9686 - recall: 0.9557 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 4s - loss: 0.0951 - cross entropy: 0.0951 - Brier score: 0.0281 - tp: 66458.0000 - fp: 2146.0000 - tn: 67582.0000 - fn: 3078.0000 - accuracy: 0.9625 - precision: 0.9687 - recall: 0.9557 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 4s - loss: 0.0949 - cross entropy: 0.0949 - Brier score: 0.0281 - tp: 69376.0000 - fp: 2238.0000 - tn: 70596.0000 - fn: 3198.0000 - accuracy: 0.9626 - precision: 0.9687 - recall: 0.9559 - auc: 0.9952 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.0948 - cross entropy: 0.0948 - Brier score: 0.0281 - tp: 72282.0000 - fp: 2321.0000 - tn: 73615.0000 - fn: 3334.0000 - accuracy: 0.9627 - precision: 0.9689 - recall: 0.9559 - auc: 0.9953 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.0949 - cross entropy: 0.0949 - Brier score: 0.0280 - tp: 75215.0000 - fp: 2413.0000 - tn: 76600.0000 - fn: 3468.0000 - accuracy: 0.9627 - precision: 0.9689 - recall: 0.9559 - auc: 0.9953 - prc: 0.9951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.0948 - cross entropy: 0.0948 - Brier score: 0.0280 - tp: 78162.0000 - fp: 2496.0000 - tn: 79568.0000 - fn: 3614.0000 - accuracy: 0.9627 - precision: 0.9691 - recall: 0.9558 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.0946 - cross entropy: 0.0946 - Brier score: 0.0280 - tp: 81056.0000 - fp: 2585.0000 - tn: 82596.0000 - fn: 3747.0000 - accuracy: 0.9627 - precision: 0.9691 - recall: 0.9558 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.0945 - cross entropy: 0.0945 - Brier score: 0.0279 - tp: 83969.0000 - fp: 2661.0000 - tn: 85607.0000 - fn: 3891.0000 - accuracy: 0.9628 - precision: 0.9693 - recall: 0.9557 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.0944 - cross entropy: 0.0944 - Brier score: 0.0279 - tp: 86844.0000 - fp: 2753.0000 - tn: 88655.0000 - fn: 4020.0000 - accuracy: 0.9628 - precision: 0.9693 - recall: 0.9558 - auc: 0.9953 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.0941 - cross entropy: 0.0941 - Brier score: 0.0279 - tp: 89806.0000 - fp: 2826.0000 - tn: 91627.0000 - fn: 4157.0000 - accuracy: 0.9629 - precision: 0.9695 - recall: 0.9558 - auc: 0.9954 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.0941 - cross entropy: 0.0941 - Brier score: 0.0278 - tp: 92705.0000 - fp: 2924.0000 - tn: 94645.0000 - fn: 4286.0000 - accuracy: 0.9629 - precision: 0.9694 - recall: 0.9558 - auc: 0.9954 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.0939 - cross entropy: 0.0939 - Brier score: 0.0278 - tp: 95640.0000 - fp: 3002.0000 - tn: 97642.0000 - fn: 4420.0000 - accuracy: 0.9630 - precision: 0.9696 - recall: 0.9558 - auc: 0.9954 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.0938 - cross entropy: 0.0938 - Brier score: 0.0277 - tp: 98568.0000 - fp: 3088.0000 - tn: 100625.0000 - fn: 4567.0000 - accuracy: 0.9630 - precision: 0.9696 - recall: 0.9557 - auc: 0.9954 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/278 [==========>...................] - ETA: 3s - loss: 0.0938 - cross entropy: 0.0938 - Brier score: 0.0277 - tp: 101468.0000 - fp: 3166.0000 - tn: 103659.0000 - fn: 4699.0000 - accuracy: 0.9631 - precision: 0.9697 - recall: 0.9557 - auc: 0.9954 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/278 [==========>...................] - ETA: 3s - loss: 0.0935 - cross entropy: 0.0935 - Brier score: 0.0276 - tp: 104397.0000 - fp: 3242.0000 - tn: 106684.0000 - fn: 4813.0000 - accuracy: 0.9632 - precision: 0.9699 - recall: 0.9559 - auc: 0.9954 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/278 [==========>...................] - ETA: 3s - loss: 0.0934 - cross entropy: 0.0934 - Brier score: 0.0276 - tp: 107331.0000 - fp: 3339.0000 - tn: 109657.0000 - fn: 4953.0000 - accuracy: 0.9632 - precision: 0.9698 - recall: 0.9559 - auc: 0.9954 - prc: 0.9953"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/278 [===========>..................] - ETA: 3s - loss: 0.0932 - cross entropy: 0.0932 - Brier score: 0.0276 - tp: 110274.0000 - fp: 3403.0000 - tn: 112645.0000 - fn: 5102.0000 - accuracy: 0.9632 - precision: 0.9701 - recall: 0.9558 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/278 [===========>..................] - ETA: 3s - loss: 0.0931 - cross entropy: 0.0931 - Brier score: 0.0275 - tp: 113156.0000 - fp: 3486.0000 - tn: 115696.0000 - fn: 5230.0000 - accuracy: 0.9633 - precision: 0.9701 - recall: 0.9558 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/278 [===========>..................] - ETA: 3s - loss: 0.0931 - cross entropy: 0.0931 - Brier score: 0.0275 - tp: 116097.0000 - fp: 3580.0000 - tn: 118657.0000 - fn: 5378.0000 - accuracy: 0.9632 - precision: 0.9701 - recall: 0.9557 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/278 [============>.................] - ETA: 3s - loss: 0.0931 - cross entropy: 0.0931 - Brier score: 0.0275 - tp: 119068.0000 - fp: 3677.0000 - tn: 121599.0000 - fn: 5512.0000 - accuracy: 0.9632 - precision: 0.9700 - recall: 0.9558 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/278 [============>.................] - ETA: 3s - loss: 0.0929 - cross entropy: 0.0929 - Brier score: 0.0275 - tp: 122040.0000 - fp: 3765.0000 - tn: 124569.0000 - fn: 5626.0000 - accuracy: 0.9633 - precision: 0.9701 - recall: 0.9559 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/278 [============>.................] - ETA: 2s - loss: 0.0929 - cross entropy: 0.0929 - Brier score: 0.0275 - tp: 124971.0000 - fp: 3846.0000 - tn: 127556.0000 - fn: 5771.0000 - accuracy: 0.9633 - precision: 0.9701 - recall: 0.9559 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/278 [=============>................] - ETA: 2s - loss: 0.0930 - cross entropy: 0.0930 - Brier score: 0.0275 - tp: 127943.0000 - fp: 3931.0000 - tn: 130511.0000 - fn: 5903.0000 - accuracy: 0.9633 - precision: 0.9702 - recall: 0.9559 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/278 [=============>................] - ETA: 2s - loss: 0.0928 - cross entropy: 0.0928 - Brier score: 0.0274 - tp: 130802.0000 - fp: 4025.0000 - tn: 133586.0000 - fn: 6019.0000 - accuracy: 0.9634 - precision: 0.9701 - recall: 0.9560 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/278 [=============>................] - ETA: 2s - loss: 0.0929 - cross entropy: 0.0929 - Brier score: 0.0274 - tp: 133784.0000 - fp: 4125.0000 - tn: 136512.0000 - fn: 6155.0000 - accuracy: 0.9634 - precision: 0.9701 - recall: 0.9560 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/278 [==============>...............] - ETA: 2s - loss: 0.0928 - cross entropy: 0.0928 - Brier score: 0.0274 - tp: 136700.0000 - fp: 4217.0000 - tn: 139515.0000 - fn: 6288.0000 - accuracy: 0.9634 - precision: 0.9701 - recall: 0.9560 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/278 [==============>...............] - ETA: 2s - loss: 0.0927 - cross entropy: 0.0927 - Brier score: 0.0274 - tp: 139683.0000 - fp: 4309.0000 - tn: 142460.0000 - fn: 6412.0000 - accuracy: 0.9634 - precision: 0.9701 - recall: 0.9561 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/278 [==============>...............] - ETA: 2s - loss: 0.0926 - cross entropy: 0.0926 - Brier score: 0.0274 - tp: 142629.0000 - fp: 4385.0000 - tn: 145457.0000 - fn: 6537.0000 - accuracy: 0.9635 - precision: 0.9702 - recall: 0.9562 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/278 [===============>..............] - ETA: 2s - loss: 0.0925 - cross entropy: 0.0925 - Brier score: 0.0273 - tp: 145599.0000 - fp: 4462.0000 - tn: 148420.0000 - fn: 6671.0000 - accuracy: 0.9635 - precision: 0.9703 - recall: 0.9562 - auc: 0.9955 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/278 [===============>..............] - ETA: 2s - loss: 0.0924 - cross entropy: 0.0924 - Brier score: 0.0273 - tp: 148505.0000 - fp: 4543.0000 - tn: 151449.0000 - fn: 6799.0000 - accuracy: 0.9636 - precision: 0.9703 - recall: 0.9562 - auc: 0.9956 - prc: 0.9954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/278 [===============>..............] - ETA: 2s - loss: 0.0922 - cross entropy: 0.0922 - Brier score: 0.0273 - tp: 151502.0000 - fp: 4613.0000 - tn: 154395.0000 - fn: 6930.0000 - accuracy: 0.9636 - precision: 0.9705 - recall: 0.9563 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/278 [================>.............] - ETA: 2s - loss: 0.0921 - cross entropy: 0.0921 - Brier score: 0.0273 - tp: 154480.0000 - fp: 4699.0000 - tn: 157343.0000 - fn: 7062.0000 - accuracy: 0.9637 - precision: 0.9705 - recall: 0.9563 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/278 [================>.............] - ETA: 2s - loss: 0.0920 - cross entropy: 0.0920 - Brier score: 0.0272 - tp: 157395.0000 - fp: 4772.0000 - tn: 160364.0000 - fn: 7197.0000 - accuracy: 0.9637 - precision: 0.9706 - recall: 0.9563 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/278 [================>.............] - ETA: 2s - loss: 0.0920 - cross entropy: 0.0920 - Brier score: 0.0272 - tp: 160306.0000 - fp: 4873.0000 - tn: 163366.0000 - fn: 7327.0000 - accuracy: 0.9637 - precision: 0.9705 - recall: 0.9563 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/278 [=================>............] - ETA: 2s - loss: 0.0920 - cross entropy: 0.0920 - Brier score: 0.0272 - tp: 163273.0000 - fp: 4963.0000 - tn: 166335.0000 - fn: 7445.0000 - accuracy: 0.9637 - precision: 0.9705 - recall: 0.9564 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/278 [=================>............] - ETA: 2s - loss: 0.0919 - cross entropy: 0.0919 - Brier score: 0.0272 - tp: 166306.0000 - fp: 5057.0000 - tn: 169224.0000 - fn: 7573.0000 - accuracy: 0.9637 - precision: 0.9705 - recall: 0.9564 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/278 [=================>............] - ETA: 2s - loss: 0.0918 - cross entropy: 0.0918 - Brier score: 0.0272 - tp: 169285.0000 - fp: 5143.0000 - tn: 172185.0000 - fn: 7691.0000 - accuracy: 0.9638 - precision: 0.9705 - recall: 0.9565 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/278 [=================>............] - ETA: 2s - loss: 0.0918 - cross entropy: 0.0918 - Brier score: 0.0272 - tp: 172215.0000 - fp: 5224.0000 - tn: 175181.0000 - fn: 7828.0000 - accuracy: 0.9638 - precision: 0.9706 - recall: 0.9565 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/278 [==================>...........] - ETA: 1s - loss: 0.0918 - cross entropy: 0.0918 - Brier score: 0.0271 - tp: 175214.0000 - fp: 5307.0000 - tn: 178116.0000 - fn: 7955.0000 - accuracy: 0.9638 - precision: 0.9706 - recall: 0.9566 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/278 [==================>...........] - ETA: 1s - loss: 0.0917 - cross entropy: 0.0917 - Brier score: 0.0271 - tp: 178163.0000 - fp: 5405.0000 - tn: 181088.0000 - fn: 8080.0000 - accuracy: 0.9638 - precision: 0.9706 - recall: 0.9566 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/278 [==================>...........] - ETA: 1s - loss: 0.0916 - cross entropy: 0.0916 - Brier score: 0.0271 - tp: 181112.0000 - fp: 5482.0000 - tn: 184065.0000 - fn: 8221.0000 - accuracy: 0.9638 - precision: 0.9706 - recall: 0.9566 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/278 [===================>..........] - ETA: 1s - loss: 0.0916 - cross entropy: 0.0916 - Brier score: 0.0271 - tp: 183999.0000 - fp: 5562.0000 - tn: 187110.0000 - fn: 8353.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9566 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/278 [===================>..........] - ETA: 1s - loss: 0.0915 - cross entropy: 0.0915 - Brier score: 0.0271 - tp: 186998.0000 - fp: 5646.0000 - tn: 190044.0000 - fn: 8480.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9566 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/278 [===================>..........] - ETA: 1s - loss: 0.0914 - cross entropy: 0.0914 - Brier score: 0.0271 - tp: 189937.0000 - fp: 5725.0000 - tn: 193048.0000 - fn: 8602.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/278 [====================>.........] - ETA: 1s - loss: 0.0915 - cross entropy: 0.0915 - Brier score: 0.0271 - tp: 192824.0000 - fp: 5828.0000 - tn: 196078.0000 - fn: 8726.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/278 [====================>.........] - ETA: 1s - loss: 0.0914 - cross entropy: 0.0914 - Brier score: 0.0271 - tp: 195766.0000 - fp: 5912.0000 - tn: 199061.0000 - fn: 8861.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/278 [====================>.........] - ETA: 1s - loss: 0.0915 - cross entropy: 0.0915 - Brier score: 0.0271 - tp: 198769.0000 - fp: 6003.0000 - tn: 201965.0000 - fn: 9007.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/278 [=====================>........] - ETA: 1s - loss: 0.0915 - cross entropy: 0.0915 - Brier score: 0.0271 - tp: 201704.0000 - fp: 6086.0000 - tn: 204950.0000 - fn: 9148.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9566 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/278 [=====================>........] - ETA: 1s - loss: 0.0914 - cross entropy: 0.0914 - Brier score: 0.0271 - tp: 204678.0000 - fp: 6183.0000 - tn: 207897.0000 - fn: 9274.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9956 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/278 [=====================>........] - ETA: 1s - loss: 0.0913 - cross entropy: 0.0913 - Brier score: 0.0270 - tp: 207699.0000 - fp: 6269.0000 - tn: 210803.0000 - fn: 9405.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "215/278 [======================>.......] - ETA: 1s - loss: 0.0913 - cross entropy: 0.0913 - Brier score: 0.0270 - tp: 210630.0000 - fp: 6367.0000 - tn: 213801.0000 - fn: 9522.0000 - accuracy: 0.9639 - precision: 0.9707 - recall: 0.9567 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/278 [======================>.......] - ETA: 1s - loss: 0.0913 - cross entropy: 0.0913 - Brier score: 0.0270 - tp: 213558.0000 - fp: 6466.0000 - tn: 216789.0000 - fn: 9651.0000 - accuracy: 0.9639 - precision: 0.9706 - recall: 0.9568 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/278 [======================>.......] - ETA: 1s - loss: 0.0913 - cross entropy: 0.0913 - Brier score: 0.0270 - tp: 216476.0000 - fp: 6551.0000 - tn: 219805.0000 - fn: 9776.0000 - accuracy: 0.9639 - precision: 0.9706 - recall: 0.9568 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/278 [=======================>......] - ETA: 1s - loss: 0.0913 - cross entropy: 0.0913 - Brier score: 0.0270 - tp: 219472.0000 - fp: 6637.0000 - tn: 222747.0000 - fn: 9896.0000 - accuracy: 0.9640 - precision: 0.9706 - recall: 0.9569 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/278 [=======================>......] - ETA: 1s - loss: 0.0912 - cross entropy: 0.0912 - Brier score: 0.0270 - tp: 222434.0000 - fp: 6722.0000 - tn: 225713.0000 - fn: 10027.0000 - accuracy: 0.9640 - precision: 0.9707 - recall: 0.9569 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/278 [=======================>......] - ETA: 0s - loss: 0.0912 - cross entropy: 0.0912 - Brier score: 0.0270 - tp: 225418.0000 - fp: 6805.0000 - tn: 228682.0000 - fn: 10135.0000 - accuracy: 0.9640 - precision: 0.9707 - recall: 0.9570 - auc: 0.9957 - prc: 0.9955"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/278 [========================>.....] - ETA: 0s - loss: 0.0911 - cross entropy: 0.0911 - Brier score: 0.0270 - tp: 228367.0000 - fp: 6872.0000 - tn: 231684.0000 - fn: 10261.0000 - accuracy: 0.9641 - precision: 0.9708 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/278 [========================>.....] - ETA: 0s - loss: 0.0911 - cross entropy: 0.0911 - Brier score: 0.0269 - tp: 231371.0000 - fp: 6945.0000 - tn: 234617.0000 - fn: 10395.0000 - accuracy: 0.9641 - precision: 0.9709 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/278 [========================>.....] - ETA: 0s - loss: 0.0910 - cross entropy: 0.0910 - Brier score: 0.0269 - tp: 234346.0000 - fp: 7023.0000 - tn: 237572.0000 - fn: 10531.0000 - accuracy: 0.9641 - precision: 0.9709 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/278 [=========================>....] - ETA: 0s - loss: 0.0909 - cross entropy: 0.0909 - Brier score: 0.0269 - tp: 237281.0000 - fp: 7098.0000 - tn: 240564.0000 - fn: 10673.0000 - accuracy: 0.9641 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/278 [=========================>....] - ETA: 0s - loss: 0.0910 - cross entropy: 0.0910 - Brier score: 0.0269 - tp: 240186.0000 - fp: 7183.0000 - tn: 243588.0000 - fn: 10803.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/278 [=========================>....] - ETA: 0s - loss: 0.0909 - cross entropy: 0.0909 - Brier score: 0.0269 - tp: 243170.0000 - fp: 7246.0000 - tn: 246552.0000 - fn: 10936.0000 - accuracy: 0.9642 - precision: 0.9711 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/278 [==========================>...] - ETA: 0s - loss: 0.0908 - cross entropy: 0.0908 - Brier score: 0.0269 - tp: 246068.0000 - fp: 7340.0000 - tn: 249594.0000 - fn: 11046.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/278 [==========================>...] - ETA: 0s - loss: 0.0908 - cross entropy: 0.0908 - Brier score: 0.0269 - tp: 248952.0000 - fp: 7437.0000 - tn: 252625.0000 - fn: 11178.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/278 [==========================>...] - ETA: 0s - loss: 0.0908 - cross entropy: 0.0908 - Brier score: 0.0269 - tp: 251916.0000 - fp: 7535.0000 - tn: 255562.0000 - fn: 11323.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "260/278 [===========================>..] - ETA: 0s - loss: 0.0907 - cross entropy: 0.0907 - Brier score: 0.0269 - tp: 254818.0000 - fp: 7629.0000 - tn: 258577.0000 - fn: 11456.0000 - accuracy: 0.9642 - precision: 0.9709 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/278 [===========================>..] - ETA: 0s - loss: 0.0907 - cross entropy: 0.0907 - Brier score: 0.0269 - tp: 257716.0000 - fp: 7708.0000 - tn: 261616.0000 - fn: 11584.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "266/278 [===========================>..] - ETA: 0s - loss: 0.0907 - cross entropy: 0.0907 - Brier score: 0.0269 - tp: 260670.0000 - fp: 7795.0000 - tn: 264577.0000 - fn: 11726.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "269/278 [============================>.] - ETA: 0s - loss: 0.0906 - cross entropy: 0.0906 - Brier score: 0.0268 - tp: 263628.0000 - fp: 7870.0000 - tn: 267576.0000 - fn: 11838.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9570 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "272/278 [============================>.] - ETA: 0s - loss: 0.0905 - cross entropy: 0.0905 - Brier score: 0.0268 - tp: 266657.0000 - fp: 7953.0000 - tn: 270481.0000 - fn: 11965.0000 - accuracy: 0.9642 - precision: 0.9710 - recall: 0.9571 - auc: 0.9957 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "275/278 [============================>.] - ETA: 0s - loss: 0.0905 - cross entropy: 0.0905 - Brier score: 0.0268 - tp: 269642.0000 - fp: 8044.0000 - tn: 273438.0000 - fn: 12076.0000 - accuracy: 0.9643 - precision: 0.9710 - recall: 0.9571 - auc: 0.9958 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - ETA: 0s - loss: 0.0904 - cross entropy: 0.0904 - Brier score: 0.0268 - tp: 272681.0000 - fp: 8122.0000 - tn: 276344.0000 - fn: 12197.0000 - accuracy: 0.9643 - precision: 0.9711 - recall: 0.9572 - auc: 0.9958 - prc: 0.9956"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 6s 20ms/step - loss: 0.0904 - cross entropy: 0.0904 - Brier score: 0.0268 - tp: 272681.0000 - fp: 8122.0000 - tn: 276344.0000 - fn: 12197.0000 - accuracy: 0.9643 - precision: 0.9711 - recall: 0.9572 - auc: 0.9958 - prc: 0.9956 - val_loss: 0.0456 - val_cross entropy: 0.0456 - val_Brier score: 0.0108 - val_tp: 76.0000 - val_fp: 576.0000 - val_tn: 44911.0000 - val_fn: 6.0000 - val_accuracy: 0.9872 - val_precision: 0.1166 - val_recall: 0.9268 - val_auc: 0.9737 - val_prc: 0.7304\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.0914 - cross entropy: 0.0914 - Brier score: 0.0257 - tp: 962.0000 - fp: 28.0000 - tn: 1015.0000 - fn: 43.0000 - accuracy: 0.9653 - precision: 0.9717 - recall: 0.9572 - auc: 0.9958 - prc: 0.9952"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.0882 - cross entropy: 0.0882 - Brier score: 0.0258 - tp: 4914.0000 - fp: 123.0000 - tn: 4985.0000 - fn: 218.0000 - accuracy: 0.9667 - precision: 0.9756 - recall: 0.9575 - auc: 0.9961 - prc: 0.9959"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.0872 - cross entropy: 0.0872 - Brier score: 0.0257 - tp: 7875.0000 - fp: 206.0000 - tn: 7960.0000 - fn: 343.0000 - accuracy: 0.9665 - precision: 0.9745 - recall: 0.9583 - auc: 0.9962 - prc: 0.9960"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.0853 - cross entropy: 0.0853 - Brier score: 0.0252 - tp: 10810.0000 - fp: 282.0000 - tn: 10971.0000 - fn: 465.0000 - accuracy: 0.9668 - precision: 0.9746 - recall: 0.9588 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.0848 - cross entropy: 0.0848 - Brier score: 0.0251 - tp: 13750.0000 - fp: 352.0000 - tn: 13984.0000 - fn: 586.0000 - accuracy: 0.9673 - precision: 0.9750 - recall: 0.9591 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.0855 - cross entropy: 0.0855 - Brier score: 0.0252 - tp: 16663.0000 - fp: 431.0000 - tn: 17009.0000 - fn: 713.0000 - accuracy: 0.9671 - precision: 0.9748 - recall: 0.9590 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.0848 - cross entropy: 0.0848 - Brier score: 0.0250 - tp: 19602.0000 - fp: 503.0000 - tn: 20028.0000 - fn: 827.0000 - accuracy: 0.9675 - precision: 0.9750 - recall: 0.9595 - auc: 0.9964 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.0850 - cross entropy: 0.0850 - Brier score: 0.0252 - tp: 22449.0000 - fp: 596.0000 - tn: 23109.0000 - fn: 950.0000 - accuracy: 0.9672 - precision: 0.9741 - recall: 0.9594 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.0855 - cross entropy: 0.0855 - Brier score: 0.0250 - tp: 25427.0000 - fp: 666.0000 - tn: 26086.0000 - fn: 1069.0000 - accuracy: 0.9674 - precision: 0.9745 - recall: 0.9597 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.0857 - cross entropy: 0.0857 - Brier score: 0.0251 - tp: 28388.0000 - fp: 761.0000 - tn: 29050.0000 - fn: 1193.0000 - accuracy: 0.9671 - precision: 0.9739 - recall: 0.9597 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.0855 - cross entropy: 0.0855 - Brier score: 0.0252 - tp: 31353.0000 - fp: 842.0000 - tn: 32019.0000 - fn: 1322.0000 - accuracy: 0.9670 - precision: 0.9738 - recall: 0.9595 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.0852 - cross entropy: 0.0852 - Brier score: 0.0251 - tp: 34330.0000 - fp: 919.0000 - tn: 34978.0000 - fn: 1453.0000 - accuracy: 0.9669 - precision: 0.9739 - recall: 0.9594 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.0853 - cross entropy: 0.0853 - Brier score: 0.0252 - tp: 37321.0000 - fp: 1013.0000 - tn: 37910.0000 - fn: 1580.0000 - accuracy: 0.9667 - precision: 0.9736 - recall: 0.9594 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.0851 - cross entropy: 0.0851 - Brier score: 0.0252 - tp: 40232.0000 - fp: 1106.0000 - tn: 40939.0000 - fn: 1691.0000 - accuracy: 0.9667 - precision: 0.9732 - recall: 0.9597 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.0856 - cross entropy: 0.0856 - Brier score: 0.0252 - tp: 43163.0000 - fp: 1197.0000 - tn: 43945.0000 - fn: 1807.0000 - accuracy: 0.9667 - precision: 0.9730 - recall: 0.9598 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.0856 - cross entropy: 0.0856 - Brier score: 0.0252 - tp: 46051.0000 - fp: 1277.0000 - tn: 47004.0000 - fn: 1924.0000 - accuracy: 0.9667 - precision: 0.9730 - recall: 0.9599 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.0853 - cross entropy: 0.0853 - Brier score: 0.0252 - tp: 49009.0000 - fp: 1364.0000 - tn: 49986.0000 - fn: 2041.0000 - accuracy: 0.9667 - precision: 0.9729 - recall: 0.9600 - auc: 0.9963 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.0852 - cross entropy: 0.0852 - Brier score: 0.0252 - tp: 51924.0000 - fp: 1445.0000 - tn: 53015.0000 - fn: 2160.0000 - accuracy: 0.9668 - precision: 0.9729 - recall: 0.9601 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.0856 - cross entropy: 0.0856 - Brier score: 0.0252 - tp: 54862.0000 - fp: 1537.0000 - tn: 56013.0000 - fn: 2276.0000 - accuracy: 0.9668 - precision: 0.9727 - recall: 0.9602 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.0858 - cross entropy: 0.0858 - Brier score: 0.0252 - tp: 57820.0000 - fp: 1611.0000 - tn: 59012.0000 - fn: 2389.0000 - accuracy: 0.9669 - precision: 0.9729 - recall: 0.9603 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.0857 - cross entropy: 0.0857 - Brier score: 0.0252 - tp: 60762.0000 - fp: 1695.0000 - tn: 61997.0000 - fn: 2522.0000 - accuracy: 0.9668 - precision: 0.9729 - recall: 0.9601 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.0856 - cross entropy: 0.0856 - Brier score: 0.0252 - tp: 63699.0000 - fp: 1777.0000 - tn: 64999.0000 - fn: 2645.0000 - accuracy: 0.9668 - precision: 0.9729 - recall: 0.9601 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 4s - loss: 0.0855 - cross entropy: 0.0855 - Brier score: 0.0251 - tp: 66638.0000 - fp: 1847.0000 - tn: 68035.0000 - fn: 2744.0000 - accuracy: 0.9670 - precision: 0.9730 - recall: 0.9605 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 4s - loss: 0.0854 - cross entropy: 0.0854 - Brier score: 0.0251 - tp: 69568.0000 - fp: 1931.0000 - tn: 71054.0000 - fn: 2855.0000 - accuracy: 0.9671 - precision: 0.9730 - recall: 0.9606 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.0852 - cross entropy: 0.0852 - Brier score: 0.0251 - tp: 72596.0000 - fp: 2006.0000 - tn: 73980.0000 - fn: 2970.0000 - accuracy: 0.9672 - precision: 0.9731 - recall: 0.9607 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.0850 - cross entropy: 0.0850 - Brier score: 0.0250 - tp: 75542.0000 - fp: 2082.0000 - tn: 76975.0000 - fn: 3097.0000 - accuracy: 0.9672 - precision: 0.9732 - recall: 0.9606 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.0853 - cross entropy: 0.0853 - Brier score: 0.0251 - tp: 78466.0000 - fp: 2175.0000 - tn: 79979.0000 - fn: 3220.0000 - accuracy: 0.9671 - precision: 0.9730 - recall: 0.9606 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.0854 - cross entropy: 0.0854 - Brier score: 0.0252 - tp: 81493.0000 - fp: 2262.0000 - tn: 82883.0000 - fn: 3346.0000 - accuracy: 0.9670 - precision: 0.9730 - recall: 0.9606 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.0857 - cross entropy: 0.0857 - Brier score: 0.0252 - tp: 84430.0000 - fp: 2336.0000 - tn: 85886.0000 - fn: 3476.0000 - accuracy: 0.9670 - precision: 0.9731 - recall: 0.9605 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.0856 - cross entropy: 0.0856 - Brier score: 0.0252 - tp: 87318.0000 - fp: 2417.0000 - tn: 88940.0000 - fn: 3597.0000 - accuracy: 0.9670 - precision: 0.9731 - recall: 0.9604 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.0857 - cross entropy: 0.0857 - Brier score: 0.0252 - tp: 90286.0000 - fp: 2510.0000 - tn: 91890.0000 - fn: 3730.0000 - accuracy: 0.9669 - precision: 0.9730 - recall: 0.9603 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.0854 - cross entropy: 0.0854 - Brier score: 0.0251 - tp: 93228.0000 - fp: 2572.0000 - tn: 94925.0000 - fn: 3835.0000 - accuracy: 0.9671 - precision: 0.9732 - recall: 0.9605 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.0854 - cross entropy: 0.0854 - Brier score: 0.0251 - tp: 96267.0000 - fp: 2654.0000 - tn: 97821.0000 - fn: 3962.0000 - accuracy: 0.9670 - precision: 0.9732 - recall: 0.9605 - auc: 0.9963 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.0853 - cross entropy: 0.0853 - Brier score: 0.0251 - tp: 99251.0000 - fp: 2729.0000 - tn: 100785.0000 - fn: 4083.0000 - accuracy: 0.9671 - precision: 0.9732 - recall: 0.9605 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/278 [==========>...................] - ETA: 3s - loss: 0.0852 - cross entropy: 0.0852 - Brier score: 0.0251 - tp: 102191.0000 - fp: 2812.0000 - tn: 103786.0000 - fn: 4203.0000 - accuracy: 0.9671 - precision: 0.9732 - recall: 0.9605 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/278 [==========>...................] - ETA: 3s - loss: 0.0851 - cross entropy: 0.0851 - Brier score: 0.0251 - tp: 105143.0000 - fp: 2898.0000 - tn: 106780.0000 - fn: 4315.0000 - accuracy: 0.9671 - precision: 0.9732 - recall: 0.9606 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/278 [==========>...................] - ETA: 3s - loss: 0.0849 - cross entropy: 0.0849 - Brier score: 0.0250 - tp: 108137.0000 - fp: 2969.0000 - tn: 109742.0000 - fn: 4432.0000 - accuracy: 0.9671 - precision: 0.9733 - recall: 0.9606 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/278 [===========>..................] - ETA: 3s - loss: 0.0849 - cross entropy: 0.0849 - Brier score: 0.0250 - tp: 111093.0000 - fp: 3043.0000 - tn: 112744.0000 - fn: 4544.0000 - accuracy: 0.9672 - precision: 0.9733 - recall: 0.9607 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/278 [===========>..................] - ETA: 3s - loss: 0.0850 - cross entropy: 0.0850 - Brier score: 0.0250 - tp: 114049.0000 - fp: 3136.0000 - tn: 115724.0000 - fn: 4659.0000 - accuracy: 0.9672 - precision: 0.9732 - recall: 0.9608 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/278 [===========>..................] - ETA: 3s - loss: 0.0850 - cross entropy: 0.0850 - Brier score: 0.0251 - tp: 116965.0000 - fp: 3237.0000 - tn: 118727.0000 - fn: 4783.0000 - accuracy: 0.9671 - precision: 0.9731 - recall: 0.9607 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/278 [============>.................] - ETA: 3s - loss: 0.0850 - cross entropy: 0.0850 - Brier score: 0.0251 - tp: 119937.0000 - fp: 3319.0000 - tn: 121683.0000 - fn: 4917.0000 - accuracy: 0.9670 - precision: 0.9731 - recall: 0.9606 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/278 [============>.................] - ETA: 2s - loss: 0.0850 - cross entropy: 0.0850 - Brier score: 0.0250 - tp: 122932.0000 - fp: 3398.0000 - tn: 124641.0000 - fn: 5029.0000 - accuracy: 0.9671 - precision: 0.9731 - recall: 0.9607 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/278 [============>.................] - ETA: 2s - loss: 0.0848 - cross entropy: 0.0848 - Brier score: 0.0250 - tp: 125920.0000 - fp: 3451.0000 - tn: 127625.0000 - fn: 5148.0000 - accuracy: 0.9672 - precision: 0.9733 - recall: 0.9607 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/278 [=============>................] - ETA: 2s - loss: 0.0847 - cross entropy: 0.0847 - Brier score: 0.0250 - tp: 128827.0000 - fp: 3521.0000 - tn: 130660.0000 - fn: 5280.0000 - accuracy: 0.9672 - precision: 0.9734 - recall: 0.9606 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/278 [=============>................] - ETA: 2s - loss: 0.0845 - cross entropy: 0.0845 - Brier score: 0.0249 - tp: 131777.0000 - fp: 3601.0000 - tn: 133660.0000 - fn: 5394.0000 - accuracy: 0.9672 - precision: 0.9734 - recall: 0.9607 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/278 [=============>................] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0249 - tp: 134695.0000 - fp: 3687.0000 - tn: 136682.0000 - fn: 5512.0000 - accuracy: 0.9672 - precision: 0.9734 - recall: 0.9607 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/278 [==============>...............] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0249 - tp: 137707.0000 - fp: 3768.0000 - tn: 139625.0000 - fn: 5620.0000 - accuracy: 0.9673 - precision: 0.9734 - recall: 0.9608 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/278 [==============>...............] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0249 - tp: 140579.0000 - fp: 3850.0000 - tn: 142706.0000 - fn: 5729.0000 - accuracy: 0.9673 - precision: 0.9733 - recall: 0.9608 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/278 [==============>...............] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0249 - tp: 143518.0000 - fp: 3929.0000 - tn: 145716.0000 - fn: 5845.0000 - accuracy: 0.9673 - precision: 0.9734 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/278 [===============>..............] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0248 - tp: 146449.0000 - fp: 4005.0000 - tn: 148746.0000 - fn: 5952.0000 - accuracy: 0.9674 - precision: 0.9734 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/278 [===============>..............] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0249 - tp: 149384.0000 - fp: 4087.0000 - tn: 151744.0000 - fn: 6081.0000 - accuracy: 0.9673 - precision: 0.9734 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/278 [===============>..............] - ETA: 2s - loss: 0.0846 - cross entropy: 0.0846 - Brier score: 0.0249 - tp: 152421.0000 - fp: 4164.0000 - tn: 154651.0000 - fn: 6204.0000 - accuracy: 0.9673 - precision: 0.9734 - recall: 0.9609 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/278 [================>.............] - ETA: 2s - loss: 0.0845 - cross entropy: 0.0845 - Brier score: 0.0248 - tp: 155378.0000 - fp: 4235.0000 - tn: 157660.0000 - fn: 6311.0000 - accuracy: 0.9674 - precision: 0.9735 - recall: 0.9610 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/278 [================>.............] - ETA: 2s - loss: 0.0845 - cross entropy: 0.0845 - Brier score: 0.0248 - tp: 158369.0000 - fp: 4319.0000 - tn: 160611.0000 - fn: 6429.0000 - accuracy: 0.9674 - precision: 0.9735 - recall: 0.9610 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/278 [================>.............] - ETA: 2s - loss: 0.0845 - cross entropy: 0.0845 - Brier score: 0.0248 - tp: 161244.0000 - fp: 4417.0000 - tn: 163656.0000 - fn: 6555.0000 - accuracy: 0.9673 - precision: 0.9733 - recall: 0.9609 - auc: 0.9964 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/278 [=================>............] - ETA: 2s - loss: 0.0845 - cross entropy: 0.0845 - Brier score: 0.0249 - tp: 164253.0000 - fp: 4500.0000 - tn: 166584.0000 - fn: 6679.0000 - accuracy: 0.9673 - precision: 0.9733 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/278 [=================>............] - ETA: 2s - loss: 0.0843 - cross entropy: 0.0843 - Brier score: 0.0248 - tp: 167262.0000 - fp: 4568.0000 - tn: 169525.0000 - fn: 6805.0000 - accuracy: 0.9673 - precision: 0.9734 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/278 [=================>............] - ETA: 2s - loss: 0.0843 - cross entropy: 0.0843 - Brier score: 0.0248 - tp: 170216.0000 - fp: 4644.0000 - tn: 172521.0000 - fn: 6923.0000 - accuracy: 0.9674 - precision: 0.9734 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/278 [=================>............] - ETA: 1s - loss: 0.0842 - cross entropy: 0.0842 - Brier score: 0.0248 - tp: 173198.0000 - fp: 4716.0000 - tn: 175494.0000 - fn: 7040.0000 - accuracy: 0.9674 - precision: 0.9735 - recall: 0.9609 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/278 [==================>...........] - ETA: 1s - loss: 0.0842 - cross entropy: 0.0842 - Brier score: 0.0248 - tp: 176212.0000 - fp: 4788.0000 - tn: 178446.0000 - fn: 7146.0000 - accuracy: 0.9674 - precision: 0.9735 - recall: 0.9610 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/278 [==================>...........] - ETA: 1s - loss: 0.0841 - cross entropy: 0.0841 - Brier score: 0.0248 - tp: 179165.0000 - fp: 4852.0000 - tn: 181460.0000 - fn: 7259.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9611 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/278 [==================>...........] - ETA: 1s - loss: 0.0840 - cross entropy: 0.0840 - Brier score: 0.0247 - tp: 182176.0000 - fp: 4931.0000 - tn: 184398.0000 - fn: 7375.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9611 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/278 [===================>..........] - ETA: 1s - loss: 0.0840 - cross entropy: 0.0840 - Brier score: 0.0247 - tp: 185185.0000 - fp: 5001.0000 - tn: 187344.0000 - fn: 7494.0000 - accuracy: 0.9675 - precision: 0.9737 - recall: 0.9611 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/278 [===================>..........] - ETA: 1s - loss: 0.0841 - cross entropy: 0.0841 - Brier score: 0.0247 - tp: 188077.0000 - fp: 5091.0000 - tn: 190385.0000 - fn: 7615.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9611 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/278 [===================>..........] - ETA: 1s - loss: 0.0841 - cross entropy: 0.0841 - Brier score: 0.0247 - tp: 191076.0000 - fp: 5175.0000 - tn: 193309.0000 - fn: 7752.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9610 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/278 [====================>.........] - ETA: 1s - loss: 0.0841 - cross entropy: 0.0841 - Brier score: 0.0247 - tp: 194020.0000 - fp: 5254.0000 - tn: 196317.0000 - fn: 7865.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9610 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/278 [====================>.........] - ETA: 1s - loss: 0.0841 - cross entropy: 0.0841 - Brier score: 0.0247 - tp: 196996.0000 - fp: 5332.0000 - tn: 199282.0000 - fn: 7990.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9610 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/278 [====================>.........] - ETA: 1s - loss: 0.0840 - cross entropy: 0.0840 - Brier score: 0.0247 - tp: 199935.0000 - fp: 5410.0000 - tn: 202287.0000 - fn: 8112.0000 - accuracy: 0.9675 - precision: 0.9737 - recall: 0.9610 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/278 [=====================>........] - ETA: 1s - loss: 0.0839 - cross entropy: 0.0839 - Brier score: 0.0247 - tp: 202943.0000 - fp: 5479.0000 - tn: 205235.0000 - fn: 8231.0000 - accuracy: 0.9675 - precision: 0.9737 - recall: 0.9610 - auc: 0.9964 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/278 [=====================>........] - ETA: 1s - loss: 0.0838 - cross entropy: 0.0838 - Brier score: 0.0246 - tp: 205899.0000 - fp: 5554.0000 - tn: 208236.0000 - fn: 8343.0000 - accuracy: 0.9675 - precision: 0.9737 - recall: 0.9611 - auc: 0.9965 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/278 [=====================>........] - ETA: 1s - loss: 0.0838 - cross entropy: 0.0838 - Brier score: 0.0246 - tp: 208904.0000 - fp: 5648.0000 - tn: 211173.0000 - fn: 8451.0000 - accuracy: 0.9675 - precision: 0.9737 - recall: 0.9611 - auc: 0.9965 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "215/278 [======================>.......] - ETA: 1s - loss: 0.0837 - cross entropy: 0.0837 - Brier score: 0.0246 - tp: 211860.0000 - fp: 5733.0000 - tn: 214159.0000 - fn: 8568.0000 - accuracy: 0.9675 - precision: 0.9737 - recall: 0.9611 - auc: 0.9965 - prc: 0.9962"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/278 [======================>.......] - ETA: 1s - loss: 0.0836 - cross entropy: 0.0836 - Brier score: 0.0246 - tp: 214839.0000 - fp: 5812.0000 - tn: 217139.0000 - fn: 8674.0000 - accuracy: 0.9676 - precision: 0.9737 - recall: 0.9612 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/278 [======================>.......] - ETA: 1s - loss: 0.0836 - cross entropy: 0.0836 - Brier score: 0.0246 - tp: 217749.0000 - fp: 5908.0000 - tn: 220147.0000 - fn: 8804.0000 - accuracy: 0.9675 - precision: 0.9736 - recall: 0.9611 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/278 [=======================>......] - ETA: 1s - loss: 0.0835 - cross entropy: 0.0835 - Brier score: 0.0246 - tp: 220669.0000 - fp: 5972.0000 - tn: 223211.0000 - fn: 8900.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9612 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/278 [=======================>......] - ETA: 0s - loss: 0.0835 - cross entropy: 0.0835 - Brier score: 0.0246 - tp: 223576.0000 - fp: 6051.0000 - tn: 226250.0000 - fn: 9019.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9612 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/278 [=======================>......] - ETA: 0s - loss: 0.0835 - cross entropy: 0.0835 - Brier score: 0.0246 - tp: 226550.0000 - fp: 6135.0000 - tn: 229216.0000 - fn: 9139.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9612 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/278 [========================>.....] - ETA: 0s - loss: 0.0834 - cross entropy: 0.0834 - Brier score: 0.0245 - tp: 229502.0000 - fp: 6214.0000 - tn: 232225.0000 - fn: 9243.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9613 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/278 [========================>.....] - ETA: 0s - loss: 0.0833 - cross entropy: 0.0833 - Brier score: 0.0245 - tp: 232490.0000 - fp: 6273.0000 - tn: 235202.0000 - fn: 9363.0000 - accuracy: 0.9676 - precision: 0.9737 - recall: 0.9613 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/278 [========================>.....] - ETA: 0s - loss: 0.0833 - cross entropy: 0.0833 - Brier score: 0.0245 - tp: 235471.0000 - fp: 6363.0000 - tn: 238164.0000 - fn: 9474.0000 - accuracy: 0.9676 - precision: 0.9737 - recall: 0.9613 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/278 [=========================>....] - ETA: 0s - loss: 0.0832 - cross entropy: 0.0832 - Brier score: 0.0245 - tp: 238411.0000 - fp: 6450.0000 - tn: 241162.0000 - fn: 9593.0000 - accuracy: 0.9676 - precision: 0.9737 - recall: 0.9613 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/278 [=========================>....] - ETA: 0s - loss: 0.0831 - cross entropy: 0.0831 - Brier score: 0.0245 - tp: 241354.0000 - fp: 6539.0000 - tn: 244173.0000 - fn: 9694.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/278 [=========================>....] - ETA: 0s - loss: 0.0831 - cross entropy: 0.0831 - Brier score: 0.0245 - tp: 244343.0000 - fp: 6630.0000 - tn: 247120.0000 - fn: 9811.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/278 [==========================>...] - ETA: 0s - loss: 0.0831 - cross entropy: 0.0831 - Brier score: 0.0245 - tp: 247372.0000 - fp: 6723.0000 - tn: 250037.0000 - fn: 9916.0000 - accuracy: 0.9676 - precision: 0.9735 - recall: 0.9615 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/278 [==========================>...] - ETA: 0s - loss: 0.0831 - cross entropy: 0.0831 - Brier score: 0.0245 - tp: 250308.0000 - fp: 6810.0000 - tn: 253034.0000 - fn: 10040.0000 - accuracy: 0.9676 - precision: 0.9735 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/278 [==========================>...] - ETA: 0s - loss: 0.0831 - cross entropy: 0.0831 - Brier score: 0.0245 - tp: 253238.0000 - fp: 6881.0000 - tn: 256057.0000 - fn: 10160.0000 - accuracy: 0.9676 - precision: 0.9735 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "260/278 [===========================>..] - ETA: 0s - loss: 0.0830 - cross entropy: 0.0830 - Brier score: 0.0245 - tp: 256174.0000 - fp: 6957.0000 - tn: 259077.0000 - fn: 10272.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/278 [===========================>..] - ETA: 0s - loss: 0.0830 - cross entropy: 0.0830 - Brier score: 0.0244 - tp: 259101.0000 - fp: 7033.0000 - tn: 262111.0000 - fn: 10379.0000 - accuracy: 0.9677 - precision: 0.9736 - recall: 0.9615 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "266/278 [===========================>..] - ETA: 0s - loss: 0.0830 - cross entropy: 0.0830 - Brier score: 0.0245 - tp: 262048.0000 - fp: 7129.0000 - tn: 265072.0000 - fn: 10519.0000 - accuracy: 0.9676 - precision: 0.9735 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "269/278 [============================>.] - ETA: 0s - loss: 0.0830 - cross entropy: 0.0830 - Brier score: 0.0244 - tp: 264982.0000 - fp: 7208.0000 - tn: 268083.0000 - fn: 10639.0000 - accuracy: 0.9676 - precision: 0.9735 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "272/278 [============================>.] - ETA: 0s - loss: 0.0829 - cross entropy: 0.0829 - Brier score: 0.0244 - tp: 267991.0000 - fp: 7289.0000 - tn: 271018.0000 - fn: 10758.0000 - accuracy: 0.9676 - precision: 0.9735 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "275/278 [============================>.] - ETA: 0s - loss: 0.0828 - cross entropy: 0.0828 - Brier score: 0.0244 - tp: 270974.0000 - fp: 7353.0000 - tn: 273992.0000 - fn: 10881.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - ETA: 0s - loss: 0.0828 - cross entropy: 0.0828 - Brier score: 0.0244 - tp: 273911.0000 - fp: 7426.0000 - tn: 277008.0000 - fn: 10999.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 6s 20ms/step - loss: 0.0828 - cross entropy: 0.0828 - Brier score: 0.0244 - tp: 273911.0000 - fp: 7426.0000 - tn: 277008.0000 - fn: 10999.0000 - accuracy: 0.9676 - precision: 0.9736 - recall: 0.9614 - auc: 0.9965 - prc: 0.9963 - val_loss: 0.0408 - val_cross entropy: 0.0408 - val_Brier score: 0.0099 - val_tp: 77.0000 - val_fp: 546.0000 - val_tn: 44941.0000 - val_fn: 5.0000 - val_accuracy: 0.9879 - val_precision: 0.1236 - val_recall: 0.9390 - val_auc: 0.9752 - val_prc: 0.7232\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.0796 - cross entropy: 0.0796 - Brier score: 0.0241 - tp: 1027.0000 - fp: 25.0000 - tn: 959.0000 - fn: 37.0000 - accuracy: 0.9697 - precision: 0.9762 - recall: 0.9652 - auc: 0.9970 - prc: 0.9972"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0237 - tp: 4981.0000 - fp: 127.0000 - tn: 4928.0000 - fn: 204.0000 - accuracy: 0.9677 - precision: 0.9751 - recall: 0.9607 - auc: 0.9968 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.0773 - cross entropy: 0.0773 - Brier score: 0.0234 - tp: 7960.0000 - fp: 197.0000 - tn: 7905.0000 - fn: 322.0000 - accuracy: 0.9683 - precision: 0.9758 - recall: 0.9611 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.0773 - cross entropy: 0.0773 - Brier score: 0.0234 - tp: 10937.0000 - fp: 266.0000 - tn: 10889.0000 - fn: 436.0000 - accuracy: 0.9688 - precision: 0.9763 - recall: 0.9617 - auc: 0.9970 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0236 - tp: 13934.0000 - fp: 358.0000 - tn: 13817.0000 - fn: 563.0000 - accuracy: 0.9679 - precision: 0.9750 - recall: 0.9612 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.0776 - cross entropy: 0.0776 - Brier score: 0.0235 - tp: 16908.0000 - fp: 431.0000 - tn: 16804.0000 - fn: 673.0000 - accuracy: 0.9683 - precision: 0.9751 - recall: 0.9617 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.0773 - cross entropy: 0.0773 - Brier score: 0.0234 - tp: 19906.0000 - fp: 511.0000 - tn: 19765.0000 - fn: 778.0000 - accuracy: 0.9685 - precision: 0.9750 - recall: 0.9624 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.0772 - cross entropy: 0.0772 - Brier score: 0.0233 - tp: 22830.0000 - fp: 587.0000 - tn: 22801.0000 - fn: 886.0000 - accuracy: 0.9687 - precision: 0.9749 - recall: 0.9626 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.0771 - cross entropy: 0.0771 - Brier score: 0.0233 - tp: 25792.0000 - fp: 654.0000 - tn: 25810.0000 - fn: 992.0000 - accuracy: 0.9691 - precision: 0.9753 - recall: 0.9630 - auc: 0.9970 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.0771 - cross entropy: 0.0771 - Brier score: 0.0233 - tp: 28767.0000 - fp: 729.0000 - tn: 28784.0000 - fn: 1112.0000 - accuracy: 0.9690 - precision: 0.9753 - recall: 0.9628 - auc: 0.9970 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.0771 - cross entropy: 0.0771 - Brier score: 0.0233 - tp: 31694.0000 - fp: 808.0000 - tn: 31805.0000 - fn: 1229.0000 - accuracy: 0.9689 - precision: 0.9751 - recall: 0.9627 - auc: 0.9970 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.0774 - cross entropy: 0.0774 - Brier score: 0.0234 - tp: 34680.0000 - fp: 888.0000 - tn: 34767.0000 - fn: 1345.0000 - accuracy: 0.9688 - precision: 0.9750 - recall: 0.9627 - auc: 0.9970 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0234 - tp: 37685.0000 - fp: 972.0000 - tn: 37709.0000 - fn: 1458.0000 - accuracy: 0.9688 - precision: 0.9749 - recall: 0.9628 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0234 - tp: 40639.0000 - fp: 1049.0000 - tn: 40714.0000 - fn: 1566.0000 - accuracy: 0.9689 - precision: 0.9748 - recall: 0.9629 - auc: 0.9969 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0234 - tp: 43659.0000 - fp: 1127.0000 - tn: 43648.0000 - fn: 1678.0000 - accuracy: 0.9689 - precision: 0.9748 - recall: 0.9630 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.0787 - cross entropy: 0.0787 - Brier score: 0.0234 - tp: 46601.0000 - fp: 1202.0000 - tn: 46654.0000 - fn: 1799.0000 - accuracy: 0.9688 - precision: 0.9749 - recall: 0.9628 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.0787 - cross entropy: 0.0787 - Brier score: 0.0235 - tp: 49547.0000 - fp: 1283.0000 - tn: 49649.0000 - fn: 1921.0000 - accuracy: 0.9687 - precision: 0.9748 - recall: 0.9627 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.0787 - cross entropy: 0.0787 - Brier score: 0.0234 - tp: 52494.0000 - fp: 1358.0000 - tn: 52663.0000 - fn: 2029.0000 - accuracy: 0.9688 - precision: 0.9748 - recall: 0.9628 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.0788 - cross entropy: 0.0788 - Brier score: 0.0235 - tp: 55398.0000 - fp: 1438.0000 - tn: 55703.0000 - fn: 2149.0000 - accuracy: 0.9687 - precision: 0.9747 - recall: 0.9627 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.0787 - cross entropy: 0.0787 - Brier score: 0.0235 - tp: 58377.0000 - fp: 1507.0000 - tn: 58687.0000 - fn: 2261.0000 - accuracy: 0.9688 - precision: 0.9748 - recall: 0.9627 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.0786 - cross entropy: 0.0786 - Brier score: 0.0234 - tp: 61360.0000 - fp: 1590.0000 - tn: 61653.0000 - fn: 2373.0000 - accuracy: 0.9688 - precision: 0.9747 - recall: 0.9628 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 3s - loss: 0.0786 - cross entropy: 0.0786 - Brier score: 0.0234 - tp: 64283.0000 - fp: 1661.0000 - tn: 64704.0000 - fn: 2472.0000 - accuracy: 0.9690 - precision: 0.9748 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 3s - loss: 0.0787 - cross entropy: 0.0787 - Brier score: 0.0234 - tp: 67224.0000 - fp: 1739.0000 - tn: 67704.0000 - fn: 2597.0000 - accuracy: 0.9689 - precision: 0.9748 - recall: 0.9628 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 3s - loss: 0.0786 - cross entropy: 0.0786 - Brier score: 0.0233 - tp: 70222.0000 - fp: 1796.0000 - tn: 70679.0000 - fn: 2711.0000 - accuracy: 0.9690 - precision: 0.9751 - recall: 0.9628 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.0788 - cross entropy: 0.0788 - Brier score: 0.0234 - tp: 73210.0000 - fp: 1875.0000 - tn: 73633.0000 - fn: 2834.0000 - accuracy: 0.9689 - precision: 0.9750 - recall: 0.9627 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.0786 - cross entropy: 0.0786 - Brier score: 0.0233 - tp: 76185.0000 - fp: 1943.0000 - tn: 76626.0000 - fn: 2942.0000 - accuracy: 0.9690 - precision: 0.9751 - recall: 0.9628 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.0785 - cross entropy: 0.0785 - Brier score: 0.0233 - tp: 79177.0000 - fp: 2018.0000 - tn: 79594.0000 - fn: 3051.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9629 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.0783 - cross entropy: 0.0783 - Brier score: 0.0233 - tp: 82115.0000 - fp: 2090.0000 - tn: 82622.0000 - fn: 3157.0000 - accuracy: 0.9691 - precision: 0.9752 - recall: 0.9630 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.0785 - cross entropy: 0.0785 - Brier score: 0.0233 - tp: 85104.0000 - fp: 2178.0000 - tn: 85566.0000 - fn: 3280.0000 - accuracy: 0.9690 - precision: 0.9750 - recall: 0.9629 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0232 - tp: 88095.0000 - fp: 2248.0000 - tn: 88547.0000 - fn: 3382.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9630 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0232 - tp: 91075.0000 - fp: 2321.0000 - tn: 91515.0000 - fn: 3505.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9629 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0232 - tp: 94065.0000 - fp: 2392.0000 - tn: 94494.0000 - fn: 3609.0000 - accuracy: 0.9692 - precision: 0.9752 - recall: 0.9631 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0232 - tp: 97007.0000 - fp: 2468.0000 - tn: 97507.0000 - fn: 3722.0000 - accuracy: 0.9692 - precision: 0.9752 - recall: 0.9630 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0232 - tp: 99928.0000 - fp: 2537.0000 - tn: 100552.0000 - fn: 3831.0000 - accuracy: 0.9692 - precision: 0.9752 - recall: 0.9631 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/278 [==========>...................] - ETA: 3s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0232 - tp: 102906.0000 - fp: 2602.0000 - tn: 103544.0000 - fn: 3940.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9631 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/278 [==========>...................] - ETA: 3s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0232 - tp: 105833.0000 - fp: 2683.0000 - tn: 106576.0000 - fn: 4044.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9632 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/278 [==========>...................] - ETA: 3s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0231 - tp: 108828.0000 - fp: 2758.0000 - tn: 109545.0000 - fn: 4149.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9633 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/278 [===========>..................] - ETA: 3s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0231 - tp: 111763.0000 - fp: 2832.0000 - tn: 112562.0000 - fn: 4267.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9632 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/278 [===========>..................] - ETA: 3s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0231 - tp: 114737.0000 - fp: 2911.0000 - tn: 115528.0000 - fn: 4392.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9631 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/278 [===========>..................] - ETA: 3s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0231 - tp: 117691.0000 - fp: 2978.0000 - tn: 118542.0000 - fn: 4501.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9632 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/278 [============>.................] - ETA: 2s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0231 - tp: 120684.0000 - fp: 3050.0000 - tn: 121503.0000 - fn: 4619.0000 - accuracy: 0.9693 - precision: 0.9754 - recall: 0.9631 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/278 [============>.................] - ETA: 2s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0231 - tp: 123588.0000 - fp: 3122.0000 - tn: 124563.0000 - fn: 4727.0000 - accuracy: 0.9693 - precision: 0.9754 - recall: 0.9632 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/278 [============>.................] - ETA: 2s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0231 - tp: 126476.0000 - fp: 3203.0000 - tn: 127611.0000 - fn: 4854.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9630 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/278 [=============>................] - ETA: 2s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0231 - tp: 129425.0000 - fp: 3282.0000 - tn: 130613.0000 - fn: 4968.0000 - accuracy: 0.9692 - precision: 0.9753 - recall: 0.9630 - auc: 0.9969 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/278 [=============>................] - ETA: 2s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0231 - tp: 132402.0000 - fp: 3361.0000 - tn: 133597.0000 - fn: 5072.0000 - accuracy: 0.9693 - precision: 0.9752 - recall: 0.9631 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/278 [=============>................] - ETA: 2s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0231 - tp: 135370.0000 - fp: 3433.0000 - tn: 136574.0000 - fn: 5199.0000 - accuracy: 0.9692 - precision: 0.9753 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/278 [==============>...............] - ETA: 2s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0231 - tp: 138317.0000 - fp: 3508.0000 - tn: 139570.0000 - fn: 5325.0000 - accuracy: 0.9692 - precision: 0.9753 - recall: 0.9629 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/278 [==============>...............] - ETA: 2s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0231 - tp: 141332.0000 - fp: 3600.0000 - tn: 142499.0000 - fn: 5433.0000 - accuracy: 0.9692 - precision: 0.9752 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/278 [==============>...............] - ETA: 2s - loss: 0.0783 - cross entropy: 0.0783 - Brier score: 0.0232 - tp: 144295.0000 - fp: 3671.0000 - tn: 145497.0000 - fn: 5545.0000 - accuracy: 0.9692 - precision: 0.9752 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/278 [===============>..............] - ETA: 2s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0232 - tp: 147277.0000 - fp: 3757.0000 - tn: 148445.0000 - fn: 5673.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9629 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/278 [===============>..............] - ETA: 2s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0232 - tp: 150276.0000 - fp: 3824.0000 - tn: 151413.0000 - fn: 5783.0000 - accuracy: 0.9691 - precision: 0.9752 - recall: 0.9629 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/278 [===============>..............] - ETA: 2s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0231 - tp: 153174.0000 - fp: 3889.0000 - tn: 154484.0000 - fn: 5893.0000 - accuracy: 0.9692 - precision: 0.9752 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "158/278 [================>.............] - ETA: 2s - loss: 0.0783 - cross entropy: 0.0783 - Brier score: 0.0232 - tp: 156159.0000 - fp: 3980.0000 - tn: 157440.0000 - fn: 6005.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "161/278 [================>.............] - ETA: 2s - loss: 0.0783 - cross entropy: 0.0783 - Brier score: 0.0231 - tp: 159087.0000 - fp: 4064.0000 - tn: 160454.0000 - fn: 6123.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9629 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "164/278 [================>.............] - ETA: 2s - loss: 0.0783 - cross entropy: 0.0783 - Brier score: 0.0232 - tp: 162045.0000 - fp: 4140.0000 - tn: 163458.0000 - fn: 6229.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "167/278 [=================>............] - ETA: 2s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0231 - tp: 165064.0000 - fp: 4216.0000 - tn: 166389.0000 - fn: 6347.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "170/278 [=================>............] - ETA: 2s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0231 - tp: 168017.0000 - fp: 4296.0000 - tn: 169397.0000 - fn: 6450.0000 - accuracy: 0.9691 - precision: 0.9751 - recall: 0.9630 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "173/278 [=================>............] - ETA: 2s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0231 - tp: 170967.0000 - fp: 4372.0000 - tn: 172409.0000 - fn: 6556.0000 - accuracy: 0.9692 - precision: 0.9751 - recall: 0.9631 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "176/278 [=================>............] - ETA: 1s - loss: 0.0785 - cross entropy: 0.0785 - Brier score: 0.0231 - tp: 173919.0000 - fp: 4448.0000 - tn: 175413.0000 - fn: 6668.0000 - accuracy: 0.9692 - precision: 0.9751 - recall: 0.9631 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "179/278 [==================>...........] - ETA: 1s - loss: 0.0784 - cross entropy: 0.0784 - Brier score: 0.0231 - tp: 176943.0000 - fp: 4518.0000 - tn: 178371.0000 - fn: 6760.0000 - accuracy: 0.9692 - precision: 0.9751 - recall: 0.9632 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "182/278 [==================>...........] - ETA: 1s - loss: 0.0783 - cross entropy: 0.0783 - Brier score: 0.0231 - tp: 179940.0000 - fp: 4591.0000 - tn: 181345.0000 - fn: 6860.0000 - accuracy: 0.9693 - precision: 0.9751 - recall: 0.9633 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "185/278 [==================>...........] - ETA: 1s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0231 - tp: 182915.0000 - fp: 4648.0000 - tn: 184347.0000 - fn: 6970.0000 - accuracy: 0.9693 - precision: 0.9752 - recall: 0.9633 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "188/278 [===================>..........] - ETA: 1s - loss: 0.0782 - cross entropy: 0.0782 - Brier score: 0.0231 - tp: 185930.0000 - fp: 4713.0000 - tn: 187293.0000 - fn: 7088.0000 - accuracy: 0.9693 - precision: 0.9753 - recall: 0.9633 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "191/278 [===================>..........] - ETA: 1s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0230 - tp: 188920.0000 - fp: 4777.0000 - tn: 190275.0000 - fn: 7196.0000 - accuracy: 0.9694 - precision: 0.9753 - recall: 0.9633 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "194/278 [===================>..........] - ETA: 1s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0230 - tp: 191851.0000 - fp: 4862.0000 - tn: 193299.0000 - fn: 7300.0000 - accuracy: 0.9694 - precision: 0.9753 - recall: 0.9633 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "197/278 [====================>.........] - ETA: 1s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0230 - tp: 194853.0000 - fp: 4923.0000 - tn: 196271.0000 - fn: 7409.0000 - accuracy: 0.9694 - precision: 0.9754 - recall: 0.9634 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "200/278 [====================>.........] - ETA: 1s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0230 - tp: 197832.0000 - fp: 4998.0000 - tn: 199258.0000 - fn: 7512.0000 - accuracy: 0.9695 - precision: 0.9754 - recall: 0.9634 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "203/278 [====================>.........] - ETA: 1s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0230 - tp: 200788.0000 - fp: 5070.0000 - tn: 202278.0000 - fn: 7608.0000 - accuracy: 0.9695 - precision: 0.9754 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "206/278 [=====================>........] - ETA: 1s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0230 - tp: 203799.0000 - fp: 5153.0000 - tn: 205220.0000 - fn: 7716.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "209/278 [=====================>........] - ETA: 1s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0230 - tp: 206728.0000 - fp: 5228.0000 - tn: 208242.0000 - fn: 7834.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "212/278 [=====================>........] - ETA: 1s - loss: 0.0781 - cross entropy: 0.0781 - Brier score: 0.0230 - tp: 209648.0000 - fp: 5318.0000 - tn: 211260.0000 - fn: 7950.0000 - accuracy: 0.9694 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "215/278 [======================>.......] - ETA: 1s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0230 - tp: 212584.0000 - fp: 5385.0000 - tn: 214291.0000 - fn: 8060.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "218/278 [======================>.......] - ETA: 1s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0230 - tp: 215580.0000 - fp: 5455.0000 - tn: 217261.0000 - fn: 8168.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "221/278 [======================>.......] - ETA: 1s - loss: 0.0780 - cross entropy: 0.0780 - Brier score: 0.0230 - tp: 218480.0000 - fp: 5535.0000 - tn: 220301.0000 - fn: 8292.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9634 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "224/278 [=======================>......] - ETA: 1s - loss: 0.0779 - cross entropy: 0.0779 - Brier score: 0.0230 - tp: 221445.0000 - fp: 5597.0000 - tn: 223313.0000 - fn: 8397.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "227/278 [=======================>......] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0230 - tp: 224360.0000 - fp: 5669.0000 - tn: 226364.0000 - fn: 8503.0000 - accuracy: 0.9695 - precision: 0.9754 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "230/278 [=======================>......] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0230 - tp: 227369.0000 - fp: 5752.0000 - tn: 229308.0000 - fn: 8611.0000 - accuracy: 0.9695 - precision: 0.9753 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "233/278 [========================>.....] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0230 - tp: 230376.0000 - fp: 5820.0000 - tn: 232267.0000 - fn: 8721.0000 - accuracy: 0.9695 - precision: 0.9754 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "236/278 [========================>.....] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0230 - tp: 233332.0000 - fp: 5896.0000 - tn: 235273.0000 - fn: 8827.0000 - accuracy: 0.9695 - precision: 0.9754 - recall: 0.9635 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "239/278 [========================>.....] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0230 - tp: 236279.0000 - fp: 5969.0000 - tn: 238289.0000 - fn: 8935.0000 - accuracy: 0.9696 - precision: 0.9754 - recall: 0.9636 - auc: 0.9969 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "242/278 [=========================>....] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0229 - tp: 239239.0000 - fp: 6038.0000 - tn: 241301.0000 - fn: 9038.0000 - accuracy: 0.9696 - precision: 0.9754 - recall: 0.9636 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "245/278 [=========================>....] - ETA: 0s - loss: 0.0778 - cross entropy: 0.0778 - Brier score: 0.0229 - tp: 242201.0000 - fp: 6117.0000 - tn: 244303.0000 - fn: 9139.0000 - accuracy: 0.9696 - precision: 0.9754 - recall: 0.9636 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "248/278 [=========================>....] - ETA: 0s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0229 - tp: 245147.0000 - fp: 6184.0000 - tn: 247316.0000 - fn: 9257.0000 - accuracy: 0.9696 - precision: 0.9754 - recall: 0.9636 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "251/278 [==========================>...] - ETA: 0s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0229 - tp: 248136.0000 - fp: 6244.0000 - tn: 250302.0000 - fn: 9366.0000 - accuracy: 0.9696 - precision: 0.9755 - recall: 0.9636 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "254/278 [==========================>...] - ETA: 0s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0229 - tp: 251119.0000 - fp: 6332.0000 - tn: 253275.0000 - fn: 9466.0000 - accuracy: 0.9696 - precision: 0.9754 - recall: 0.9637 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "257/278 [==========================>...] - ETA: 0s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0229 - tp: 254125.0000 - fp: 6396.0000 - tn: 256239.0000 - fn: 9576.0000 - accuracy: 0.9697 - precision: 0.9754 - recall: 0.9637 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "260/278 [===========================>..] - ETA: 0s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0229 - tp: 257066.0000 - fp: 6463.0000 - tn: 259274.0000 - fn: 9677.0000 - accuracy: 0.9697 - precision: 0.9755 - recall: 0.9637 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "263/278 [===========================>..] - ETA: 0s - loss: 0.0777 - cross entropy: 0.0777 - Brier score: 0.0229 - tp: 260134.0000 - fp: 6528.0000 - tn: 262169.0000 - fn: 9793.0000 - accuracy: 0.9697 - precision: 0.9755 - recall: 0.9637 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "266/278 [===========================>..] - ETA: 0s - loss: 0.0776 - cross entropy: 0.0776 - Brier score: 0.0229 - tp: 263079.0000 - fp: 6596.0000 - tn: 265200.0000 - fn: 9893.0000 - accuracy: 0.9697 - precision: 0.9755 - recall: 0.9638 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "269/278 [============================>.] - ETA: 0s - loss: 0.0775 - cross entropy: 0.0775 - Brier score: 0.0228 - tp: 266083.0000 - fp: 6663.0000 - tn: 268174.0000 - fn: 9992.0000 - accuracy: 0.9698 - precision: 0.9756 - recall: 0.9638 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "272/278 [============================>.] - ETA: 0s - loss: 0.0775 - cross entropy: 0.0775 - Brier score: 0.0228 - tp: 269080.0000 - fp: 6738.0000 - tn: 271138.0000 - fn: 10100.0000 - accuracy: 0.9698 - precision: 0.9756 - recall: 0.9638 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "275/278 [============================>.] - ETA: 0s - loss: 0.0775 - cross entropy: 0.0775 - Brier score: 0.0228 - tp: 272055.0000 - fp: 6816.0000 - tn: 274120.0000 - fn: 10209.0000 - accuracy: 0.9698 - precision: 0.9756 - recall: 0.9638 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - ETA: 0s - loss: 0.0775 - cross entropy: 0.0775 - Brier score: 0.0228 - tp: 274985.0000 - fp: 6904.0000 - tn: 277146.0000 - fn: 10309.0000 - accuracy: 0.9698 - precision: 0.9755 - recall: 0.9639 - auc: 0.9970 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 5s 20ms/step - loss: 0.0775 - cross entropy: 0.0775 - Brier score: 0.0228 - tp: 274985.0000 - fp: 6904.0000 - tn: 277146.0000 - fn: 10309.0000 - accuracy: 0.9698 - precision: 0.9755 - recall: 0.9639 - auc: 0.9970 - prc: 0.9968 - val_loss: 0.0387 - val_cross entropy: 0.0387 - val_Brier score: 0.0096 - val_tp: 77.0000 - val_fp: 568.0000 - val_tn: 44919.0000 - val_fn: 5.0000 - val_accuracy: 0.9874 - val_precision: 0.1194 - val_recall: 0.9390 - val_auc: 0.9761 - val_prc: 0.7145\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.0680 - cross entropy: 0.0680 - Brier score: 0.0205 - tp: 983.0000 - fp: 22.0000 - tn: 1008.0000 - fn: 35.0000 - accuracy: 0.9722 - precision: 0.9781 - recall: 0.9656 - auc: 0.9977 - prc: 0.9978"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.0709 - cross entropy: 0.0709 - Brier score: 0.0209 - tp: 4840.0000 - fp: 125.0000 - tn: 5108.0000 - fn: 167.0000 - accuracy: 0.9715 - precision: 0.9748 - recall: 0.9666 - auc: 0.9975 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 7809.0000 - fp: 211.0000 - tn: 8093.0000 - fn: 271.0000 - accuracy: 0.9706 - precision: 0.9737 - recall: 0.9665 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.0745 - cross entropy: 0.0745 - Brier score: 0.0219 - tp: 10782.0000 - fp: 280.0000 - tn: 11087.0000 - fn: 379.0000 - accuracy: 0.9707 - precision: 0.9747 - recall: 0.9660 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.0752 - cross entropy: 0.0752 - Brier score: 0.0221 - tp: 13799.0000 - fp: 364.0000 - tn: 14034.0000 - fn: 475.0000 - accuracy: 0.9707 - precision: 0.9743 - recall: 0.9667 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.0764 - cross entropy: 0.0764 - Brier score: 0.0222 - tp: 16683.0000 - fp: 449.0000 - tn: 17113.0000 - fn: 571.0000 - accuracy: 0.9707 - precision: 0.9738 - recall: 0.9669 - auc: 0.9969 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.0763 - cross entropy: 0.0763 - Brier score: 0.0222 - tp: 19607.0000 - fp: 524.0000 - tn: 20153.0000 - fn: 676.0000 - accuracy: 0.9707 - precision: 0.9740 - recall: 0.9667 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.0757 - cross entropy: 0.0757 - Brier score: 0.0220 - tp: 22509.0000 - fp: 591.0000 - tn: 23228.0000 - fn: 776.0000 - accuracy: 0.9710 - precision: 0.9744 - recall: 0.9667 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.0764 - cross entropy: 0.0764 - Brier score: 0.0223 - tp: 25419.0000 - fp: 681.0000 - tn: 26269.0000 - fn: 879.0000 - accuracy: 0.9707 - precision: 0.9739 - recall: 0.9666 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.0759 - cross entropy: 0.0759 - Brier score: 0.0222 - tp: 28347.0000 - fp: 743.0000 - tn: 29326.0000 - fn: 976.0000 - accuracy: 0.9711 - precision: 0.9745 - recall: 0.9667 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 31/278 [==>...........................] - ETA: 4s - loss: 0.0761 - cross entropy: 0.0761 - Brier score: 0.0222 - tp: 30298.0000 - fp: 790.0000 - tn: 31357.0000 - fn: 1043.0000 - accuracy: 0.9711 - precision: 0.9746 - recall: 0.9667 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 34/278 [==>...........................] - ETA: 4s - loss: 0.0760 - cross entropy: 0.0760 - Brier score: 0.0222 - tp: 33277.0000 - fp: 851.0000 - tn: 34350.0000 - fn: 1154.0000 - accuracy: 0.9712 - precision: 0.9751 - recall: 0.9665 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 37/278 [==>...........................] - ETA: 4s - loss: 0.0760 - cross entropy: 0.0760 - Brier score: 0.0222 - tp: 36129.0000 - fp: 922.0000 - tn: 37466.0000 - fn: 1259.0000 - accuracy: 0.9712 - precision: 0.9751 - recall: 0.9663 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 40/278 [===>..........................] - ETA: 4s - loss: 0.0769 - cross entropy: 0.0769 - Brier score: 0.0223 - tp: 39087.0000 - fp: 998.0000 - tn: 40462.0000 - fn: 1373.0000 - accuracy: 0.9711 - precision: 0.9751 - recall: 0.9661 - auc: 0.9970 - prc: 0.9964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 43/278 [===>..........................] - ETA: 4s - loss: 0.0766 - cross entropy: 0.0766 - Brier score: 0.0222 - tp: 42080.0000 - fp: 1071.0000 - tn: 43424.0000 - fn: 1489.0000 - accuracy: 0.9709 - precision: 0.9752 - recall: 0.9658 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 46/278 [===>..........................] - ETA: 4s - loss: 0.0763 - cross entropy: 0.0763 - Brier score: 0.0222 - tp: 45079.0000 - fp: 1136.0000 - tn: 46394.0000 - fn: 1599.0000 - accuracy: 0.9710 - precision: 0.9754 - recall: 0.9657 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 49/278 [====>.........................] - ETA: 4s - loss: 0.0766 - cross entropy: 0.0766 - Brier score: 0.0223 - tp: 48068.0000 - fp: 1212.0000 - tn: 49349.0000 - fn: 1723.0000 - accuracy: 0.9708 - precision: 0.9754 - recall: 0.9654 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 52/278 [====>.........................] - ETA: 4s - loss: 0.0764 - cross entropy: 0.0764 - Brier score: 0.0222 - tp: 51028.0000 - fp: 1280.0000 - tn: 52360.0000 - fn: 1828.0000 - accuracy: 0.9708 - precision: 0.9755 - recall: 0.9654 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 55/278 [====>.........................] - ETA: 4s - loss: 0.0764 - cross entropy: 0.0764 - Brier score: 0.0223 - tp: 53937.0000 - fp: 1348.0000 - tn: 55409.0000 - fn: 1946.0000 - accuracy: 0.9708 - precision: 0.9756 - recall: 0.9652 - auc: 0.9970 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 58/278 [=====>........................] - ETA: 4s - loss: 0.0761 - cross entropy: 0.0761 - Brier score: 0.0222 - tp: 56960.0000 - fp: 1418.0000 - tn: 58368.0000 - fn: 2038.0000 - accuracy: 0.9709 - precision: 0.9757 - recall: 0.9655 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 61/278 [=====>........................] - ETA: 4s - loss: 0.0759 - cross entropy: 0.0759 - Brier score: 0.0222 - tp: 59890.0000 - fp: 1479.0000 - tn: 61406.0000 - fn: 2153.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9653 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 64/278 [=====>........................] - ETA: 4s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 62831.0000 - fp: 1534.0000 - tn: 64432.0000 - fn: 2275.0000 - accuracy: 0.9709 - precision: 0.9762 - recall: 0.9651 - auc: 0.9971 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 67/278 [======>.......................] - ETA: 4s - loss: 0.0757 - cross entropy: 0.0757 - Brier score: 0.0222 - tp: 65757.0000 - fp: 1622.0000 - tn: 67467.0000 - fn: 2370.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9652 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 70/278 [======>.......................] - ETA: 3s - loss: 0.0757 - cross entropy: 0.0757 - Brier score: 0.0222 - tp: 68666.0000 - fp: 1703.0000 - tn: 70520.0000 - fn: 2471.0000 - accuracy: 0.9709 - precision: 0.9758 - recall: 0.9653 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 73/278 [======>.......................] - ETA: 3s - loss: 0.0759 - cross entropy: 0.0759 - Brier score: 0.0223 - tp: 71644.0000 - fp: 1779.0000 - tn: 73500.0000 - fn: 2581.0000 - accuracy: 0.9708 - precision: 0.9758 - recall: 0.9652 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 75/278 [=======>......................] - ETA: 3s - loss: 0.0759 - cross entropy: 0.0759 - Brier score: 0.0223 - tp: 73630.0000 - fp: 1831.0000 - tn: 75483.0000 - fn: 2656.0000 - accuracy: 0.9708 - precision: 0.9757 - recall: 0.9652 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 78/278 [=======>......................] - ETA: 3s - loss: 0.0759 - cross entropy: 0.0759 - Brier score: 0.0223 - tp: 76564.0000 - fp: 1903.0000 - tn: 78502.0000 - fn: 2775.0000 - accuracy: 0.9707 - precision: 0.9757 - recall: 0.9650 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 81/278 [=======>......................] - ETA: 3s - loss: 0.0760 - cross entropy: 0.0760 - Brier score: 0.0223 - tp: 79535.0000 - fp: 1982.0000 - tn: 81489.0000 - fn: 2882.0000 - accuracy: 0.9707 - precision: 0.9757 - recall: 0.9650 - auc: 0.9970 - prc: 0.9966"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 84/278 [========>.....................] - ETA: 3s - loss: 0.0759 - cross entropy: 0.0759 - Brier score: 0.0223 - tp: 82505.0000 - fp: 2033.0000 - tn: 84505.0000 - fn: 2989.0000 - accuracy: 0.9708 - precision: 0.9760 - recall: 0.9650 - auc: 0.9970 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 87/278 [========>.....................] - ETA: 3s - loss: 0.0758 - cross entropy: 0.0758 - Brier score: 0.0222 - tp: 85521.0000 - fp: 2100.0000 - tn: 87460.0000 - fn: 3095.0000 - accuracy: 0.9708 - precision: 0.9760 - recall: 0.9651 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 90/278 [========>.....................] - ETA: 3s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 88394.0000 - fp: 2160.0000 - tn: 90548.0000 - fn: 3218.0000 - accuracy: 0.9708 - precision: 0.9761 - recall: 0.9649 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 93/278 [=========>....................] - ETA: 3s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 91342.0000 - fp: 2228.0000 - tn: 93556.0000 - fn: 3338.0000 - accuracy: 0.9708 - precision: 0.9762 - recall: 0.9647 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 96/278 [=========>....................] - ETA: 3s - loss: 0.0754 - cross entropy: 0.0754 - Brier score: 0.0222 - tp: 94233.0000 - fp: 2305.0000 - tn: 96653.0000 - fn: 3417.0000 - accuracy: 0.9709 - precision: 0.9761 - recall: 0.9650 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 99/278 [=========>....................] - ETA: 3s - loss: 0.0754 - cross entropy: 0.0754 - Brier score: 0.0221 - tp: 97186.0000 - fp: 2376.0000 - tn: 99675.0000 - fn: 3515.0000 - accuracy: 0.9709 - precision: 0.9761 - recall: 0.9651 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "102/278 [==========>...................] - ETA: 3s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 100182.0000 - fp: 2464.0000 - tn: 102633.0000 - fn: 3617.0000 - accuracy: 0.9709 - precision: 0.9760 - recall: 0.9652 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "105/278 [==========>...................] - ETA: 3s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 103102.0000 - fp: 2534.0000 - tn: 105686.0000 - fn: 3718.0000 - accuracy: 0.9709 - precision: 0.9760 - recall: 0.9652 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "108/278 [==========>...................] - ETA: 3s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 106103.0000 - fp: 2614.0000 - tn: 108640.0000 - fn: 3827.0000 - accuracy: 0.9709 - precision: 0.9760 - recall: 0.9652 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "111/278 [==========>...................] - ETA: 3s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 109083.0000 - fp: 2691.0000 - tn: 111630.0000 - fn: 3924.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9653 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "114/278 [===========>..................] - ETA: 3s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 112124.0000 - fp: 2769.0000 - tn: 114562.0000 - fn: 4017.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9654 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "117/278 [===========>..................] - ETA: 3s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 115033.0000 - fp: 2836.0000 - tn: 117626.0000 - fn: 4121.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9654 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "120/278 [===========>..................] - ETA: 3s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 118020.0000 - fp: 2908.0000 - tn: 120606.0000 - fn: 4226.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9654 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "123/278 [============>.................] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 121001.0000 - fp: 2979.0000 - tn: 123586.0000 - fn: 4338.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9654 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "126/278 [============>.................] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 124033.0000 - fp: 3041.0000 - tn: 126536.0000 - fn: 4438.0000 - accuracy: 0.9710 - precision: 0.9761 - recall: 0.9655 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "129/278 [============>.................] - ETA: 2s - loss: 0.0753 - cross entropy: 0.0753 - Brier score: 0.0221 - tp: 126993.0000 - fp: 3105.0000 - tn: 129558.0000 - fn: 4536.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "132/278 [=============>................] - ETA: 2s - loss: 0.0753 - cross entropy: 0.0753 - Brier score: 0.0221 - tp: 129978.0000 - fp: 3194.0000 - tn: 132531.0000 - fn: 4633.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "135/278 [=============>................] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0221 - tp: 133006.0000 - fp: 3278.0000 - tn: 135469.0000 - fn: 4727.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9657 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "138/278 [=============>................] - ETA: 2s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 135982.0000 - fp: 3361.0000 - tn: 138456.0000 - fn: 4825.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9657 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "141/278 [==============>...............] - ETA: 2s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0221 - tp: 138905.0000 - fp: 3432.0000 - tn: 141508.0000 - fn: 4923.0000 - accuracy: 0.9711 - precision: 0.9759 - recall: 0.9658 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "144/278 [==============>...............] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0221 - tp: 141862.0000 - fp: 3495.0000 - tn: 144510.0000 - fn: 5045.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9657 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "147/278 [==============>...............] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0221 - tp: 144749.0000 - fp: 3576.0000 - tn: 147585.0000 - fn: 5146.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9657 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "150/278 [===============>..............] - ETA: 2s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 147724.0000 - fp: 3655.0000 - tn: 150567.0000 - fn: 5254.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9657 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "153/278 [===============>..............] - ETA: 2s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 150671.0000 - fp: 3719.0000 - tn: 153600.0000 - fn: 5354.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9657 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "156/278 [===============>..............] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 153582.0000 - fp: 3795.0000 - tn: 156647.0000 - fn: 5464.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9656 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "159/278 [================>.............] - ETA: 2s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 156557.0000 - fp: 3880.0000 - tn: 159614.0000 - fn: 5581.0000 - accuracy: 0.9709 - precision: 0.9758 - recall: 0.9656 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "162/278 [================>.............] - ETA: 2s - loss: 0.0756 - cross entropy: 0.0756 - Brier score: 0.0222 - tp: 159529.0000 - fp: 3958.0000 - tn: 162595.0000 - fn: 5694.0000 - accuracy: 0.9709 - precision: 0.9758 - recall: 0.9655 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "165/278 [================>.............] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 162507.0000 - fp: 4011.0000 - tn: 165601.0000 - fn: 5801.0000 - accuracy: 0.9710 - precision: 0.9759 - recall: 0.9655 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "168/278 [=================>............] - ETA: 2s - loss: 0.0755 - cross entropy: 0.0755 - Brier score: 0.0222 - tp: 165468.0000 - fp: 4086.0000 - tn: 168597.0000 - fn: 5913.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9655 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "171/278 [=================>............] - ETA: 2s - loss: 0.0754 - cross entropy: 0.0754 - Brier score: 0.0222 - tp: 168406.0000 - fp: 4162.0000 - tn: 171621.0000 - fn: 6019.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/278 [=================>............] - ETA: 1s - loss: 0.0753 - cross entropy: 0.0753 - Brier score: 0.0222 - tp: 171346.0000 - fp: 4228.0000 - tn: 174650.0000 - fn: 6128.0000 - accuracy: 0.9709 - precision: 0.9759 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/278 [==================>...........] - ETA: 1s - loss: 0.0752 - cross entropy: 0.0752 - Brier score: 0.0221 - tp: 174373.0000 - fp: 4278.0000 - tn: 177608.0000 - fn: 6237.0000 - accuracy: 0.9710 - precision: 0.9761 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/278 [==================>...........] - ETA: 1s - loss: 0.0752 - cross entropy: 0.0752 - Brier score: 0.0221 - tp: 177396.0000 - fp: 4358.0000 - tn: 180536.0000 - fn: 6350.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9654 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "183/278 [==================>...........] - ETA: 1s - loss: 0.0753 - cross entropy: 0.0753 - Brier score: 0.0221 - tp: 180343.0000 - fp: 4436.0000 - tn: 183545.0000 - fn: 6460.0000 - accuracy: 0.9709 - precision: 0.9760 - recall: 0.9654 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "186/278 [===================>..........] - ETA: 1s - loss: 0.0752 - cross entropy: 0.0752 - Brier score: 0.0221 - tp: 183356.0000 - fp: 4498.0000 - tn: 186507.0000 - fn: 6567.0000 - accuracy: 0.9710 - precision: 0.9761 - recall: 0.9654 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "189/278 [===================>..........] - ETA: 1s - loss: 0.0751 - cross entropy: 0.0751 - Brier score: 0.0221 - tp: 186375.0000 - fp: 4558.0000 - tn: 189469.0000 - fn: 6670.0000 - accuracy: 0.9710 - precision: 0.9761 - recall: 0.9654 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/278 [===================>..........] - ETA: 1s - loss: 0.0750 - cross entropy: 0.0750 - Brier score: 0.0221 - tp: 189307.0000 - fp: 4626.0000 - tn: 192517.0000 - fn: 6766.0000 - accuracy: 0.9710 - precision: 0.9761 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/278 [====================>.........] - ETA: 1s - loss: 0.0750 - cross entropy: 0.0750 - Brier score: 0.0221 - tp: 192182.0000 - fp: 4701.0000 - tn: 195609.0000 - fn: 6868.0000 - accuracy: 0.9710 - precision: 0.9761 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "198/278 [====================>.........] - ETA: 1s - loss: 0.0751 - cross entropy: 0.0751 - Brier score: 0.0221 - tp: 195135.0000 - fp: 4799.0000 - tn: 198597.0000 - fn: 6973.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9655 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/278 [====================>.........] - ETA: 1s - loss: 0.0750 - cross entropy: 0.0750 - Brier score: 0.0221 - tp: 198103.0000 - fp: 4869.0000 - tn: 201618.0000 - fn: 7058.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "204/278 [=====================>........] - ETA: 1s - loss: 0.0749 - cross entropy: 0.0749 - Brier score: 0.0221 - tp: 201060.0000 - fp: 4941.0000 - tn: 204631.0000 - fn: 7160.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/278 [=====================>........] - ETA: 1s - loss: 0.0748 - cross entropy: 0.0748 - Brier score: 0.0221 - tp: 204012.0000 - fp: 5014.0000 - tn: 207640.0000 - fn: 7270.0000 - accuracy: 0.9710 - precision: 0.9760 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/278 [=====================>........] - ETA: 1s - loss: 0.0748 - cross entropy: 0.0748 - Brier score: 0.0220 - tp: 206992.0000 - fp: 5083.0000 - tn: 210649.0000 - fn: 7356.0000 - accuracy: 0.9711 - precision: 0.9760 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/278 [=====================>........] - ETA: 1s - loss: 0.0747 - cross entropy: 0.0747 - Brier score: 0.0220 - tp: 209959.0000 - fp: 5155.0000 - tn: 213649.0000 - fn: 7461.0000 - accuracy: 0.9711 - precision: 0.9760 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "216/278 [======================>.......] - ETA: 1s - loss: 0.0747 - cross entropy: 0.0747 - Brier score: 0.0220 - tp: 212924.0000 - fp: 5210.0000 - tn: 216670.0000 - fn: 7564.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "219/278 [======================>.......] - ETA: 1s - loss: 0.0747 - cross entropy: 0.0747 - Brier score: 0.0220 - tp: 215867.0000 - fp: 5286.0000 - tn: 219682.0000 - fn: 7677.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/278 [======================>.......] - ETA: 1s - loss: 0.0746 - cross entropy: 0.0746 - Brier score: 0.0220 - tp: 218809.0000 - fp: 5367.0000 - tn: 222696.0000 - fn: 7784.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/278 [=======================>......] - ETA: 1s - loss: 0.0747 - cross entropy: 0.0747 - Brier score: 0.0220 - tp: 221774.0000 - fp: 5451.0000 - tn: 225686.0000 - fn: 7889.0000 - accuracy: 0.9711 - precision: 0.9760 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "228/278 [=======================>......] - ETA: 0s - loss: 0.0746 - cross entropy: 0.0746 - Brier score: 0.0220 - tp: 224721.0000 - fp: 5519.0000 - tn: 228713.0000 - fn: 7991.0000 - accuracy: 0.9711 - precision: 0.9760 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "231/278 [=======================>......] - ETA: 0s - loss: 0.0746 - cross entropy: 0.0746 - Brier score: 0.0220 - tp: 227685.0000 - fp: 5585.0000 - tn: 231715.0000 - fn: 8103.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9656 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "234/278 [========================>.....] - ETA: 0s - loss: 0.0745 - cross entropy: 0.0745 - Brier score: 0.0220 - tp: 230725.0000 - fp: 5650.0000 - tn: 234661.0000 - fn: 8196.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/278 [========================>.....] - ETA: 0s - loss: 0.0745 - cross entropy: 0.0745 - Brier score: 0.0220 - tp: 233678.0000 - fp: 5722.0000 - tn: 237676.0000 - fn: 8300.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/278 [========================>.....] - ETA: 0s - loss: 0.0744 - cross entropy: 0.0744 - Brier score: 0.0219 - tp: 236619.0000 - fp: 5771.0000 - tn: 240720.0000 - fn: 8410.0000 - accuracy: 0.9711 - precision: 0.9762 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/278 [=========================>....] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 239596.0000 - fp: 5858.0000 - tn: 243711.0000 - fn: 8499.0000 - accuracy: 0.9712 - precision: 0.9761 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/278 [=========================>....] - ETA: 0s - loss: 0.0744 - cross entropy: 0.0744 - Brier score: 0.0220 - tp: 242575.0000 - fp: 5935.0000 - tn: 246685.0000 - fn: 8613.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/278 [=========================>....] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 245501.0000 - fp: 5997.0000 - tn: 249744.0000 - fn: 8710.0000 - accuracy: 0.9712 - precision: 0.9762 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/278 [==========================>...] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 248486.0000 - fp: 6065.0000 - tn: 252739.0000 - fn: 8806.0000 - accuracy: 0.9712 - precision: 0.9762 - recall: 0.9658 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/278 [==========================>...] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 251409.0000 - fp: 6144.0000 - tn: 255770.0000 - fn: 8917.0000 - accuracy: 0.9712 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "258/278 [==========================>...] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 254316.0000 - fp: 6209.0000 - tn: 258827.0000 - fn: 9032.0000 - accuracy: 0.9712 - precision: 0.9762 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "261/278 [===========================>..] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 257307.0000 - fp: 6290.0000 - tn: 261797.0000 - fn: 9134.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "264/278 [===========================>..] - ETA: 0s - loss: 0.0742 - cross entropy: 0.0742 - Brier score: 0.0219 - tp: 260293.0000 - fp: 6348.0000 - tn: 264797.0000 - fn: 9234.0000 - accuracy: 0.9712 - precision: 0.9762 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/278 [===========================>..] - ETA: 0s - loss: 0.0742 - cross entropy: 0.0742 - Brier score: 0.0219 - tp: 263219.0000 - fp: 6418.0000 - tn: 267829.0000 - fn: 9350.0000 - accuracy: 0.9712 - precision: 0.9762 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/278 [============================>.] - ETA: 0s - loss: 0.0742 - cross entropy: 0.0742 - Brier score: 0.0219 - tp: 266151.0000 - fp: 6498.0000 - tn: 270850.0000 - fn: 9461.0000 - accuracy: 0.9711 - precision: 0.9762 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "273/278 [============================>.] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 269126.0000 - fp: 6575.0000 - tn: 273840.0000 - fn: 9563.0000 - accuracy: 0.9711 - precision: 0.9762 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "276/278 [============================>.] - ETA: 0s - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 272118.0000 - fp: 6650.0000 - tn: 276822.0000 - fn: 9658.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 5s 20ms/step - loss: 0.0743 - cross entropy: 0.0743 - Brier score: 0.0219 - tp: 274086.0000 - fp: 6704.0000 - tn: 278828.0000 - fn: 9726.0000 - accuracy: 0.9711 - precision: 0.9761 - recall: 0.9657 - auc: 0.9971 - prc: 0.9969 - val_loss: 0.0344 - val_cross entropy: 0.0344 - val_Brier score: 0.0085 - val_tp: 76.0000 - val_fp: 492.0000 - val_tn: 44995.0000 - val_fn: 6.0000 - val_accuracy: 0.9891 - val_precision: 0.1338 - val_recall: 0.9268 - val_auc: 0.9767 - val_prc: 0.7147\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.0669 - cross entropy: 0.0669 - Brier score: 0.0202 - tp: 993.0000 - fp: 19.0000 - tn: 998.0000 - fn: 38.0000 - accuracy: 0.9722 - precision: 0.9812 - recall: 0.9631 - auc: 0.9979 - prc: 0.9979"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.0723 - cross entropy: 0.0723 - Brier score: 0.0220 - tp: 4913.0000 - fp: 115.0000 - tn: 5024.0000 - fn: 188.0000 - accuracy: 0.9704 - precision: 0.9771 - recall: 0.9631 - auc: 0.9972 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.0708 - cross entropy: 0.0708 - Brier score: 0.0213 - tp: 7886.0000 - fp: 175.0000 - tn: 8036.0000 - fn: 287.0000 - accuracy: 0.9718 - precision: 0.9783 - recall: 0.9649 - auc: 0.9974 - prc: 0.9973"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.0707 - cross entropy: 0.0707 - Brier score: 0.0212 - tp: 10846.0000 - fp: 234.0000 - tn: 11046.0000 - fn: 402.0000 - accuracy: 0.9718 - precision: 0.9789 - recall: 0.9643 - auc: 0.9974 - prc: 0.9972"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.0737 - cross entropy: 0.0737 - Brier score: 0.0216 - tp: 13824.0000 - fp: 315.0000 - tn: 14026.0000 - fn: 507.0000 - accuracy: 0.9713 - precision: 0.9777 - recall: 0.9646 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.0737 - cross entropy: 0.0737 - Brier score: 0.0215 - tp: 16823.0000 - fp: 383.0000 - tn: 16993.0000 - fn: 617.0000 - accuracy: 0.9713 - precision: 0.9777 - recall: 0.9646 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.0740 - cross entropy: 0.0740 - Brier score: 0.0216 - tp: 19779.0000 - fp: 453.0000 - tn: 19999.0000 - fn: 729.0000 - accuracy: 0.9711 - precision: 0.9776 - recall: 0.9645 - auc: 0.9971 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.0732 - cross entropy: 0.0732 - Brier score: 0.0214 - tp: 22759.0000 - fp: 516.0000 - tn: 22993.0000 - fn: 836.0000 - accuracy: 0.9713 - precision: 0.9778 - recall: 0.9646 - auc: 0.9972 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.0733 - cross entropy: 0.0733 - Brier score: 0.0215 - tp: 25783.0000 - fp: 587.0000 - tn: 25929.0000 - fn: 949.0000 - accuracy: 0.9712 - precision: 0.9777 - recall: 0.9645 - auc: 0.9971 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.0725 - cross entropy: 0.0725 - Brier score: 0.0212 - tp: 28762.0000 - fp: 638.0000 - tn: 28962.0000 - fn: 1030.0000 - accuracy: 0.9719 - precision: 0.9783 - recall: 0.9654 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.0723 - cross entropy: 0.0723 - Brier score: 0.0212 - tp: 31708.0000 - fp: 709.0000 - tn: 31983.0000 - fn: 1136.0000 - accuracy: 0.9718 - precision: 0.9781 - recall: 0.9654 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.0721 - cross entropy: 0.0721 - Brier score: 0.0211 - tp: 34689.0000 - fp: 767.0000 - tn: 34981.0000 - fn: 1243.0000 - accuracy: 0.9720 - precision: 0.9784 - recall: 0.9654 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.0716 - cross entropy: 0.0716 - Brier score: 0.0210 - tp: 37689.0000 - fp: 821.0000 - tn: 37961.0000 - fn: 1353.0000 - accuracy: 0.9721 - precision: 0.9787 - recall: 0.9653 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.0717 - cross entropy: 0.0717 - Brier score: 0.0211 - tp: 40611.0000 - fp: 892.0000 - tn: 40988.0000 - fn: 1477.0000 - accuracy: 0.9718 - precision: 0.9785 - recall: 0.9649 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.0716 - cross entropy: 0.0716 - Brier score: 0.0211 - tp: 43557.0000 - fp: 948.0000 - tn: 44020.0000 - fn: 1587.0000 - accuracy: 0.9719 - precision: 0.9787 - recall: 0.9648 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0211 - tp: 46481.0000 - fp: 1019.0000 - tn: 47060.0000 - fn: 1696.0000 - accuracy: 0.9718 - precision: 0.9785 - recall: 0.9648 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0211 - tp: 49440.0000 - fp: 1086.0000 - tn: 50082.0000 - fn: 1792.0000 - accuracy: 0.9719 - precision: 0.9785 - recall: 0.9650 - auc: 0.9973 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.0710 - cross entropy: 0.0710 - Brier score: 0.0210 - tp: 52300.0000 - fp: 1145.0000 - tn: 53209.0000 - fn: 1890.0000 - accuracy: 0.9720 - precision: 0.9786 - recall: 0.9651 - auc: 0.9973 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0210 - tp: 55281.0000 - fp: 1224.0000 - tn: 56196.0000 - fn: 1987.0000 - accuracy: 0.9720 - precision: 0.9783 - recall: 0.9653 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0211 - tp: 58239.0000 - fp: 1308.0000 - tn: 59191.0000 - fn: 2094.0000 - accuracy: 0.9718 - precision: 0.9780 - recall: 0.9653 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.0717 - cross entropy: 0.0717 - Brier score: 0.0211 - tp: 61175.0000 - fp: 1376.0000 - tn: 62236.0000 - fn: 2189.0000 - accuracy: 0.9719 - precision: 0.9780 - recall: 0.9655 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.0718 - cross entropy: 0.0718 - Brier score: 0.0212 - tp: 64164.0000 - fp: 1453.0000 - tn: 65204.0000 - fn: 2299.0000 - accuracy: 0.9718 - precision: 0.9779 - recall: 0.9654 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 3s - loss: 0.0720 - cross entropy: 0.0720 - Brier score: 0.0212 - tp: 67128.0000 - fp: 1539.0000 - tn: 68196.0000 - fn: 2401.0000 - accuracy: 0.9717 - precision: 0.9776 - recall: 0.9655 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 3s - loss: 0.0717 - cross entropy: 0.0717 - Brier score: 0.0212 - tp: 70147.0000 - fp: 1596.0000 - tn: 71168.0000 - fn: 2497.0000 - accuracy: 0.9719 - precision: 0.9778 - recall: 0.9656 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.0717 - cross entropy: 0.0717 - Brier score: 0.0212 - tp: 73113.0000 - fp: 1663.0000 - tn: 74172.0000 - fn: 2604.0000 - accuracy: 0.9718 - precision: 0.9778 - recall: 0.9656 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.0716 - cross entropy: 0.0716 - Brier score: 0.0212 - tp: 76099.0000 - fp: 1734.0000 - tn: 77164.0000 - fn: 2699.0000 - accuracy: 0.9719 - precision: 0.9777 - recall: 0.9657 - auc: 0.9972 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.0717 - cross entropy: 0.0717 - Brier score: 0.0212 - tp: 79073.0000 - fp: 1802.0000 - tn: 80161.0000 - fn: 2804.0000 - accuracy: 0.9719 - precision: 0.9777 - recall: 0.9658 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0211 - tp: 82110.0000 - fp: 1865.0000 - tn: 83122.0000 - fn: 2887.0000 - accuracy: 0.9720 - precision: 0.9778 - recall: 0.9660 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 86/278 [========>.....................] - ETA: 3s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0211 - tp: 85084.0000 - fp: 1943.0000 - tn: 86111.0000 - fn: 2990.0000 - accuracy: 0.9720 - precision: 0.9777 - recall: 0.9661 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 89/278 [========>.....................] - ETA: 3s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0211 - tp: 88058.0000 - fp: 2015.0000 - tn: 89104.0000 - fn: 3095.0000 - accuracy: 0.9720 - precision: 0.9776 - recall: 0.9660 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 92/278 [========>.....................] - ETA: 3s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0211 - tp: 91030.0000 - fp: 2097.0000 - tn: 92089.0000 - fn: 3200.0000 - accuracy: 0.9719 - precision: 0.9775 - recall: 0.9660 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 95/278 [=========>....................] - ETA: 3s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0211 - tp: 94004.0000 - fp: 2167.0000 - tn: 95101.0000 - fn: 3288.0000 - accuracy: 0.9720 - precision: 0.9775 - recall: 0.9662 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 98/278 [=========>....................] - ETA: 3s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0211 - tp: 96953.0000 - fp: 2223.0000 - tn: 98140.0000 - fn: 3388.0000 - accuracy: 0.9720 - precision: 0.9776 - recall: 0.9662 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "101/278 [=========>....................] - ETA: 3s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0211 - tp: 99985.0000 - fp: 2293.0000 - tn: 101092.0000 - fn: 3478.0000 - accuracy: 0.9721 - precision: 0.9776 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "104/278 [==========>...................] - ETA: 3s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 102889.0000 - fp: 2352.0000 - tn: 104176.0000 - fn: 3575.0000 - accuracy: 0.9722 - precision: 0.9777 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "107/278 [==========>...................] - ETA: 3s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 105930.0000 - fp: 2429.0000 - tn: 107089.0000 - fn: 3688.0000 - accuracy: 0.9721 - precision: 0.9776 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "110/278 [==========>...................] - ETA: 3s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0211 - tp: 108886.0000 - fp: 2499.0000 - tn: 110103.0000 - fn: 3792.0000 - accuracy: 0.9721 - precision: 0.9776 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "113/278 [===========>..................] - ETA: 3s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 111878.0000 - fp: 2565.0000 - tn: 113076.0000 - fn: 3905.0000 - accuracy: 0.9720 - precision: 0.9776 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "116/278 [===========>..................] - ETA: 3s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 114869.0000 - fp: 2634.0000 - tn: 116065.0000 - fn: 4000.0000 - accuracy: 0.9721 - precision: 0.9776 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "119/278 [===========>..................] - ETA: 3s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 117891.0000 - fp: 2708.0000 - tn: 119014.0000 - fn: 4099.0000 - accuracy: 0.9721 - precision: 0.9775 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "122/278 [============>.................] - ETA: 2s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 120893.0000 - fp: 2781.0000 - tn: 121980.0000 - fn: 4202.0000 - accuracy: 0.9721 - precision: 0.9775 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "125/278 [============>.................] - ETA: 2s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0211 - tp: 123830.0000 - fp: 2847.0000 - tn: 125011.0000 - fn: 4312.0000 - accuracy: 0.9720 - precision: 0.9775 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "128/278 [============>.................] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0211 - tp: 126872.0000 - fp: 2926.0000 - tn: 127919.0000 - fn: 4427.0000 - accuracy: 0.9720 - precision: 0.9775 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "131/278 [=============>................] - ETA: 2s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 129847.0000 - fp: 3004.0000 - tn: 130908.0000 - fn: 4529.0000 - accuracy: 0.9719 - precision: 0.9774 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "134/278 [=============>................] - ETA: 2s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 132778.0000 - fp: 3082.0000 - tn: 133941.0000 - fn: 4631.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "137/278 [=============>................] - ETA: 2s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 135755.0000 - fp: 3158.0000 - tn: 136931.0000 - fn: 4732.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "140/278 [==============>...............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 138755.0000 - fp: 3217.0000 - tn: 139917.0000 - fn: 4831.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "143/278 [==============>...............] - ETA: 2s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0211 - tp: 141710.0000 - fp: 3276.0000 - tn: 142947.0000 - fn: 4931.0000 - accuracy: 0.9720 - precision: 0.9774 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "146/278 [==============>...............] - ETA: 2s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 144675.0000 - fp: 3346.0000 - tn: 145962.0000 - fn: 5025.0000 - accuracy: 0.9720 - precision: 0.9774 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "149/278 [===============>..............] - ETA: 2s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 147589.0000 - fp: 3419.0000 - tn: 149019.0000 - fn: 5125.0000 - accuracy: 0.9720 - precision: 0.9774 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "152/278 [===============>..............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 150524.0000 - fp: 3488.0000 - tn: 152034.0000 - fn: 5250.0000 - accuracy: 0.9719 - precision: 0.9774 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "155/278 [===============>..............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 153471.0000 - fp: 3561.0000 - tn: 155051.0000 - fn: 5357.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/278 [===============>..............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 155493.0000 - fp: 3597.0000 - tn: 157031.0000 - fn: 5415.0000 - accuracy: 0.9720 - precision: 0.9774 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/278 [================>.............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 158469.0000 - fp: 3669.0000 - tn: 160038.0000 - fn: 5504.0000 - accuracy: 0.9720 - precision: 0.9774 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/278 [================>.............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 161439.0000 - fp: 3747.0000 - tn: 163026.0000 - fn: 5612.0000 - accuracy: 0.9720 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "166/278 [================>.............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 164381.0000 - fp: 3816.0000 - tn: 166048.0000 - fn: 5723.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/278 [=================>............] - ETA: 2s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 167368.0000 - fp: 3892.0000 - tn: 169022.0000 - fn: 5830.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "172/278 [=================>............] - ETA: 2s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 170328.0000 - fp: 3978.0000 - tn: 172006.0000 - fn: 5944.0000 - accuracy: 0.9718 - precision: 0.9772 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "175/278 [=================>............] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 173306.0000 - fp: 4044.0000 - tn: 175003.0000 - fn: 6047.0000 - accuracy: 0.9718 - precision: 0.9772 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "178/278 [==================>...........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 176370.0000 - fp: 4111.0000 - tn: 177913.0000 - fn: 6150.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "181/278 [==================>...........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 179353.0000 - fp: 4175.0000 - tn: 180899.0000 - fn: 6261.0000 - accuracy: 0.9718 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "184/278 [==================>...........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 182245.0000 - fp: 4244.0000 - tn: 183978.0000 - fn: 6365.0000 - accuracy: 0.9718 - precision: 0.9772 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "187/278 [===================>..........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 185184.0000 - fp: 4307.0000 - tn: 187015.0000 - fn: 6470.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9662 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "190/278 [===================>..........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 188198.0000 - fp: 4380.0000 - tn: 189961.0000 - fn: 6581.0000 - accuracy: 0.9718 - precision: 0.9773 - recall: 0.9662 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "193/278 [===================>..........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 191139.0000 - fp: 4453.0000 - tn: 192990.0000 - fn: 6682.0000 - accuracy: 0.9718 - precision: 0.9772 - recall: 0.9662 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "196/278 [====================>.........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 194194.0000 - fp: 4516.0000 - tn: 195925.0000 - fn: 6773.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "199/278 [====================>.........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 197167.0000 - fp: 4590.0000 - tn: 198933.0000 - fn: 6862.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "202/278 [====================>.........] - ETA: 1s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0212 - tp: 200167.0000 - fp: 4661.0000 - tn: 201890.0000 - fn: 6978.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "205/278 [=====================>........] - ETA: 1s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 203148.0000 - fp: 4720.0000 - tn: 204902.0000 - fn: 7070.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "208/278 [=====================>........] - ETA: 1s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 206125.0000 - fp: 4802.0000 - tn: 207887.0000 - fn: 7170.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "211/278 [=====================>........] - ETA: 1s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 209027.0000 - fp: 4865.0000 - tn: 210957.0000 - fn: 7279.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "214/278 [======================>.......] - ETA: 1s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 211982.0000 - fp: 4933.0000 - tn: 213966.0000 - fn: 7391.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "217/278 [======================>.......] - ETA: 1s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 214949.0000 - fp: 4990.0000 - tn: 217002.0000 - fn: 7475.0000 - accuracy: 0.9720 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "220/278 [======================>.......] - ETA: 1s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 217865.0000 - fp: 5060.0000 - tn: 220058.0000 - fn: 7577.0000 - accuracy: 0.9720 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "223/278 [=======================>......] - ETA: 1s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 220797.0000 - fp: 5122.0000 - tn: 223096.0000 - fn: 7689.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "226/278 [=======================>......] - ETA: 1s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0212 - tp: 223786.0000 - fp: 5200.0000 - tn: 226072.0000 - fn: 7790.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "229/278 [=======================>......] - ETA: 0s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 226751.0000 - fp: 5262.0000 - tn: 229088.0000 - fn: 7891.0000 - accuracy: 0.9720 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "232/278 [========================>.....] - ETA: 0s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 229706.0000 - fp: 5335.0000 - tn: 232096.0000 - fn: 7999.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9663 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "235/278 [========================>.....] - ETA: 0s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 232695.0000 - fp: 5407.0000 - tn: 235080.0000 - fn: 8098.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "238/278 [========================>.....] - ETA: 0s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 235660.0000 - fp: 5480.0000 - tn: 238091.0000 - fn: 8193.0000 - accuracy: 0.9719 - precision: 0.9773 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "241/278 [=========================>....] - ETA: 0s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 238614.0000 - fp: 5565.0000 - tn: 241088.0000 - fn: 8301.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "244/278 [=========================>....] - ETA: 0s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 241615.0000 - fp: 5642.0000 - tn: 244050.0000 - fn: 8405.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "247/278 [=========================>....] - ETA: 0s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 244556.0000 - fp: 5704.0000 - tn: 247088.0000 - fn: 8508.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "250/278 [=========================>....] - ETA: 0s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 247544.0000 - fp: 5765.0000 - tn: 250087.0000 - fn: 8604.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "253/278 [==========================>...] - ETA: 0s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 250406.0000 - fp: 5853.0000 - tn: 253185.0000 - fn: 8700.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9664 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "256/278 [==========================>...] - ETA: 0s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 253435.0000 - fp: 5915.0000 - tn: 256148.0000 - fn: 8790.0000 - accuracy: 0.9720 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "259/278 [==========================>...] - ETA: 0s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 256406.0000 - fp: 5994.0000 - tn: 259153.0000 - fn: 8879.0000 - accuracy: 0.9720 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "262/278 [===========================>..] - ETA: 0s - loss: 0.0714 - cross entropy: 0.0714 - Brier score: 0.0212 - tp: 259350.0000 - fp: 6061.0000 - tn: 262169.0000 - fn: 8996.0000 - accuracy: 0.9719 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "265/278 [===========================>..] - ETA: 0s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 262332.0000 - fp: 6127.0000 - tn: 265177.0000 - fn: 9084.0000 - accuracy: 0.9720 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "268/278 [===========================>..] - ETA: 0s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 265346.0000 - fp: 6189.0000 - tn: 268139.0000 - fn: 9190.0000 - accuracy: 0.9720 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "271/278 [============================>.] - ETA: 0s - loss: 0.0713 - cross entropy: 0.0713 - Brier score: 0.0212 - tp: 268280.0000 - fp: 6259.0000 - tn: 271180.0000 - fn: 9289.0000 - accuracy: 0.9720 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "274/278 [============================>.] - ETA: 0s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0212 - tp: 271255.0000 - fp: 6316.0000 - tn: 274185.0000 - fn: 9396.0000 - accuracy: 0.9720 - precision: 0.9772 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "277/278 [============================>.] - ETA: 0s - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 274226.0000 - fp: 6377.0000 - tn: 277198.0000 - fn: 9495.0000 - accuracy: 0.9720 - precision: 0.9773 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 5s 20ms/step - loss: 0.0712 - cross entropy: 0.0712 - Brier score: 0.0211 - tp: 275221.0000 - fp: 6399.0000 - tn: 278199.0000 - fn: 9525.0000 - accuracy: 0.9720 - precision: 0.9773 - recall: 0.9665 - auc: 0.9973 - prc: 0.9970 - val_loss: 0.0311 - val_cross entropy: 0.0311 - val_Brier score: 0.0077 - val_tp: 76.0000 - val_fp: 434.0000 - val_tn: 45053.0000 - val_fn: 6.0000 - val_accuracy: 0.9903 - val_precision: 0.1490 - val_recall: 0.9268 - val_auc: 0.9772 - val_prc: 0.7140\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "  1/278 [..............................] - ETA: 1s - loss: 0.0786 - cross entropy: 0.0786 - Brier score: 0.0216 - tp: 997.0000 - fp: 19.0000 - tn: 1003.0000 - fn: 29.0000 - accuracy: 0.9766 - precision: 0.9813 - recall: 0.9717 - auc: 0.9970 - prc: 0.9964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  5/278 [..............................] - ETA: 4s - loss: 0.0745 - cross entropy: 0.0745 - Brier score: 0.0211 - tp: 4860.0000 - fp: 107.0000 - tn: 5112.0000 - fn: 161.0000 - accuracy: 0.9738 - precision: 0.9785 - recall: 0.9679 - auc: 0.9970 - prc: 0.9961"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "  8/278 [..............................] - ETA: 4s - loss: 0.0722 - cross entropy: 0.0722 - Brier score: 0.0209 - tp: 7832.0000 - fp: 172.0000 - tn: 8119.0000 - fn: 261.0000 - accuracy: 0.9736 - precision: 0.9785 - recall: 0.9677 - auc: 0.9972 - prc: 0.9965"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 11/278 [>.............................] - ETA: 4s - loss: 0.0719 - cross entropy: 0.0719 - Brier score: 0.0207 - tp: 10787.0000 - fp: 234.0000 - tn: 11144.0000 - fn: 363.0000 - accuracy: 0.9735 - precision: 0.9788 - recall: 0.9674 - auc: 0.9973 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 14/278 [>.............................] - ETA: 4s - loss: 0.0718 - cross entropy: 0.0718 - Brier score: 0.0208 - tp: 13778.0000 - fp: 321.0000 - tn: 14119.0000 - fn: 454.0000 - accuracy: 0.9730 - precision: 0.9772 - recall: 0.9681 - auc: 0.9972 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 17/278 [>.............................] - ETA: 4s - loss: 0.0718 - cross entropy: 0.0718 - Brier score: 0.0208 - tp: 16663.0000 - fp: 394.0000 - tn: 17203.0000 - fn: 556.0000 - accuracy: 0.9727 - precision: 0.9769 - recall: 0.9677 - auc: 0.9972 - prc: 0.9967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 20/278 [=>............................] - ETA: 4s - loss: 0.0716 - cross entropy: 0.0716 - Brier score: 0.0208 - tp: 19646.0000 - fp: 458.0000 - tn: 20192.0000 - fn: 664.0000 - accuracy: 0.9726 - precision: 0.9772 - recall: 0.9673 - auc: 0.9973 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 23/278 [=>............................] - ETA: 4s - loss: 0.0715 - cross entropy: 0.0715 - Brier score: 0.0208 - tp: 22700.0000 - fp: 517.0000 - tn: 23129.0000 - fn: 758.0000 - accuracy: 0.9729 - precision: 0.9777 - recall: 0.9677 - auc: 0.9973 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 26/278 [=>............................] - ETA: 4s - loss: 0.0721 - cross entropy: 0.0721 - Brier score: 0.0208 - tp: 25696.0000 - fp: 590.0000 - tn: 26121.0000 - fn: 841.0000 - accuracy: 0.9731 - precision: 0.9776 - recall: 0.9683 - auc: 0.9972 - prc: 0.9968"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 29/278 [==>...........................] - ETA: 4s - loss: 0.0717 - cross entropy: 0.0717 - Brier score: 0.0208 - tp: 28618.0000 - fp: 660.0000 - tn: 29180.0000 - fn: 934.0000 - accuracy: 0.9732 - precision: 0.9775 - recall: 0.9684 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 32/278 [==>...........................] - ETA: 4s - loss: 0.0710 - cross entropy: 0.0710 - Brier score: 0.0206 - tp: 31599.0000 - fp: 716.0000 - tn: 32189.0000 - fn: 1032.0000 - accuracy: 0.9733 - precision: 0.9778 - recall: 0.9684 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 35/278 [==>...........................] - ETA: 4s - loss: 0.0709 - cross entropy: 0.0709 - Brier score: 0.0206 - tp: 34608.0000 - fp: 780.0000 - tn: 35169.0000 - fn: 1123.0000 - accuracy: 0.9735 - precision: 0.9780 - recall: 0.9686 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 38/278 [===>..........................] - ETA: 4s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0205 - tp: 37630.0000 - fp: 845.0000 - tn: 38131.0000 - fn: 1218.0000 - accuracy: 0.9735 - precision: 0.9780 - recall: 0.9686 - auc: 0.9974 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 41/278 [===>..........................] - ETA: 4s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0206 - tp: 40571.0000 - fp: 914.0000 - tn: 41163.0000 - fn: 1320.0000 - accuracy: 0.9734 - precision: 0.9780 - recall: 0.9685 - auc: 0.9974 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 44/278 [===>..........................] - ETA: 4s - loss: 0.0700 - cross entropy: 0.0700 - Brier score: 0.0205 - tp: 43540.0000 - fp: 974.0000 - tn: 44186.0000 - fn: 1412.0000 - accuracy: 0.9735 - precision: 0.9781 - recall: 0.9686 - auc: 0.9974 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 47/278 [====>.........................] - ETA: 4s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0205 - tp: 46502.0000 - fp: 1048.0000 - tn: 47195.0000 - fn: 1511.0000 - accuracy: 0.9734 - precision: 0.9780 - recall: 0.9685 - auc: 0.9974 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 50/278 [====>.........................] - ETA: 4s - loss: 0.0701 - cross entropy: 0.0701 - Brier score: 0.0205 - tp: 49472.0000 - fp: 1111.0000 - tn: 50208.0000 - fn: 1609.0000 - accuracy: 0.9734 - precision: 0.9780 - recall: 0.9685 - auc: 0.9974 - prc: 0.9971"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 53/278 [====>.........................] - ETA: 4s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0206 - tp: 52481.0000 - fp: 1189.0000 - tn: 53148.0000 - fn: 1726.0000 - accuracy: 0.9731 - precision: 0.9778 - recall: 0.9682 - auc: 0.9974 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 56/278 [=====>........................] - ETA: 4s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0207 - tp: 55506.0000 - fp: 1270.0000 - tn: 56080.0000 - fn: 1832.0000 - accuracy: 0.9730 - precision: 0.9776 - recall: 0.9680 - auc: 0.9974 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 59/278 [=====>........................] - ETA: 4s - loss: 0.0708 - cross entropy: 0.0708 - Brier score: 0.0207 - tp: 58416.0000 - fp: 1345.0000 - tn: 59138.0000 - fn: 1933.0000 - accuracy: 0.9729 - precision: 0.9775 - recall: 0.9680 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 62/278 [=====>........................] - ETA: 4s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0206 - tp: 61409.0000 - fp: 1405.0000 - tn: 62129.0000 - fn: 2033.0000 - accuracy: 0.9729 - precision: 0.9776 - recall: 0.9680 - auc: 0.9974 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 65/278 [======>.......................] - ETA: 4s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0206 - tp: 64424.0000 - fp: 1481.0000 - tn: 65087.0000 - fn: 2128.0000 - accuracy: 0.9729 - precision: 0.9775 - recall: 0.9680 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 68/278 [======>.......................] - ETA: 4s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0206 - tp: 67418.0000 - fp: 1556.0000 - tn: 68067.0000 - fn: 2223.0000 - accuracy: 0.9729 - precision: 0.9774 - recall: 0.9681 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 71/278 [======>.......................] - ETA: 3s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0206 - tp: 70361.0000 - fp: 1625.0000 - tn: 71085.0000 - fn: 2337.0000 - accuracy: 0.9728 - precision: 0.9774 - recall: 0.9679 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 74/278 [======>.......................] - ETA: 3s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0206 - tp: 73399.0000 - fp: 1701.0000 - tn: 74028.0000 - fn: 2424.0000 - accuracy: 0.9728 - precision: 0.9774 - recall: 0.9680 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 77/278 [=======>......................] - ETA: 3s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0206 - tp: 76300.0000 - fp: 1776.0000 - tn: 77097.0000 - fn: 2523.0000 - accuracy: 0.9727 - precision: 0.9773 - recall: 0.9680 - auc: 0.9974 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 80/278 [=======>......................] - ETA: 3s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0207 - tp: 79299.0000 - fp: 1854.0000 - tn: 80060.0000 - fn: 2627.0000 - accuracy: 0.9727 - precision: 0.9772 - recall: 0.9679 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 83/278 [=======>......................] - ETA: 3s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0207 - tp: 82273.0000 - fp: 1931.0000 - tn: 83057.0000 - fn: 2723.0000 - accuracy: 0.9726 - precision: 0.9771 - recall: 0.9680 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 85/278 [========>.....................] - ETA: 3s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0207 - tp: 84237.0000 - fp: 1978.0000 - tn: 85081.0000 - fn: 2784.0000 - accuracy: 0.9726 - precision: 0.9771 - recall: 0.9680 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 88/278 [========>.....................] - ETA: 3s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0207 - tp: 87261.0000 - fp: 2042.0000 - tn: 88034.0000 - fn: 2887.0000 - accuracy: 0.9727 - precision: 0.9771 - recall: 0.9680 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 91/278 [========>.....................] - ETA: 3s - loss: 0.0708 - cross entropy: 0.0708 - Brier score: 0.0208 - tp: 90181.0000 - fp: 2124.0000 - tn: 91061.0000 - fn: 3002.0000 - accuracy: 0.9725 - precision: 0.9770 - recall: 0.9678 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 94/278 [=========>....................] - ETA: 3s - loss: 0.0707 - cross entropy: 0.0707 - Brier score: 0.0208 - tp: 93111.0000 - fp: 2194.0000 - tn: 94096.0000 - fn: 3111.0000 - accuracy: 0.9724 - precision: 0.9770 - recall: 0.9677 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 97/278 [=========>....................] - ETA: 3s - loss: 0.0707 - cross entropy: 0.0707 - Brier score: 0.0208 - tp: 96028.0000 - fp: 2264.0000 - tn: 97143.0000 - fn: 3221.0000 - accuracy: 0.9724 - precision: 0.9770 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "100/278 [=========>....................] - ETA: 3s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0208 - tp: 99058.0000 - fp: 2336.0000 - tn: 100077.0000 - fn: 3329.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "103/278 [==========>...................] - ETA: 3s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0208 - tp: 102018.0000 - fp: 2411.0000 - tn: 103073.0000 - fn: 3442.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "106/278 [==========>...................] - ETA: 3s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0208 - tp: 105011.0000 - fp: 2471.0000 - tn: 106071.0000 - fn: 3535.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "109/278 [==========>...................] - ETA: 3s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0208 - tp: 107921.0000 - fp: 2549.0000 - tn: 109125.0000 - fn: 3637.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "112/278 [===========>..................] - ETA: 3s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0208 - tp: 110944.0000 - fp: 2611.0000 - tn: 112092.0000 - fn: 3729.0000 - accuracy: 0.9724 - precision: 0.9770 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "115/278 [===========>..................] - ETA: 3s - loss: 0.0703 - cross entropy: 0.0703 - Brier score: 0.0208 - tp: 113900.0000 - fp: 2681.0000 - tn: 115103.0000 - fn: 3836.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "118/278 [===========>..................] - ETA: 3s - loss: 0.0703 - cross entropy: 0.0703 - Brier score: 0.0208 - tp: 116863.0000 - fp: 2753.0000 - tn: 118099.0000 - fn: 3949.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9673 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "121/278 [============>.................] - ETA: 3s - loss: 0.0703 - cross entropy: 0.0703 - Brier score: 0.0208 - tp: 119791.0000 - fp: 2819.0000 - tn: 121155.0000 - fn: 4043.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "124/278 [============>.................] - ETA: 2s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0208 - tp: 122766.0000 - fp: 2900.0000 - tn: 124151.0000 - fn: 4135.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "127/278 [============>.................] - ETA: 2s - loss: 0.0703 - cross entropy: 0.0703 - Brier score: 0.0208 - tp: 125732.0000 - fp: 2974.0000 - tn: 127158.0000 - fn: 4232.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "130/278 [=============>................] - ETA: 2s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0208 - tp: 128703.0000 - fp: 3037.0000 - tn: 130161.0000 - fn: 4339.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "133/278 [=============>................] - ETA: 2s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0208 - tp: 131698.0000 - fp: 3116.0000 - tn: 133130.0000 - fn: 4440.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "136/278 [=============>................] - ETA: 2s - loss: 0.0703 - cross entropy: 0.0703 - Brier score: 0.0208 - tp: 134624.0000 - fp: 3184.0000 - tn: 136174.0000 - fn: 4546.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9673 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "139/278 [==============>...............] - ETA: 2s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0208 - tp: 137579.0000 - fp: 3252.0000 - tn: 139193.0000 - fn: 4648.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9673 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "142/278 [==============>...............] - ETA: 2s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0208 - tp: 140541.0000 - fp: 3327.0000 - tn: 142198.0000 - fn: 4750.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9673 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "145/278 [==============>...............] - ETA: 2s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0208 - tp: 143521.0000 - fp: 3390.0000 - tn: 145186.0000 - fn: 4863.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9672 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "148/278 [==============>...............] - ETA: 2s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0208 - tp: 146494.0000 - fp: 3457.0000 - tn: 148198.0000 - fn: 4955.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9673 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "151/278 [===============>..............] - ETA: 2s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0208 - tp: 149497.0000 - fp: 3534.0000 - tn: 151155.0000 - fn: 5062.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9672 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "154/278 [===============>..............] - ETA: 2s - loss: 0.0707 - cross entropy: 0.0707 - Brier score: 0.0208 - tp: 152456.0000 - fp: 3602.0000 - tn: 154181.0000 - fn: 5153.0000 - accuracy: 0.9722 - precision: 0.9769 - recall: 0.9673 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "157/278 [===============>..............] - ETA: 2s - loss: 0.0707 - cross entropy: 0.0707 - Brier score: 0.0208 - tp: 155434.0000 - fp: 3675.0000 - tn: 157180.0000 - fn: 5247.0000 - accuracy: 0.9723 - precision: 0.9769 - recall: 0.9673 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "160/278 [================>.............] - ETA: 2s - loss: 0.0706 - cross entropy: 0.0706 - Brier score: 0.0208 - tp: 158404.0000 - fp: 3725.0000 - tn: 160204.0000 - fn: 5347.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9673 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "163/278 [================>.............] - ETA: 2s - loss: 0.0705 - cross entropy: 0.0705 - Brier score: 0.0208 - tp: 161455.0000 - fp: 3793.0000 - tn: 163128.0000 - fn: 5448.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9674 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "166/278 [================>.............] - ETA: 2s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0208 - tp: 164386.0000 - fp: 3862.0000 - tn: 166178.0000 - fn: 5542.0000 - accuracy: 0.9723 - precision: 0.9770 - recall: 0.9674 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "169/278 [=================>............] - ETA: 2s - loss: 0.0704 - cross entropy: 0.0704 - Brier score: 0.0208 - tp: 167355.0000 - fp: 3930.0000 - tn: 169194.0000 - fn: 5633.0000 - accuracy: 0.9724 - precision: 0.9771 - recall: 0.9674 - auc: 0.9973 - prc: 0.9969"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "171/278 [=================>............] - ETA: 2s - loss: 0.0703 - cross entropy: 0.0703 - Brier score: 0.0208 - tp: 169413.0000 - fp: 3971.0000 - tn: 171122.0000 - fn: 5702.0000 - accuracy: 0.9724 - precision: 0.9771 - recall: 0.9674 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "174/278 [=================>............] - ETA: 2s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0207 - tp: 172435.0000 - fp: 4020.0000 - tn: 174096.0000 - fn: 5801.0000 - accuracy: 0.9724 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "177/278 [==================>...........] - ETA: 1s - loss: 0.0701 - cross entropy: 0.0701 - Brier score: 0.0207 - tp: 175469.0000 - fp: 4092.0000 - tn: 177047.0000 - fn: 5888.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "180/278 [==================>...........] - ETA: 1s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0207 - tp: 178504.0000 - fp: 4161.0000 - tn: 179976.0000 - fn: 5999.0000 - accuracy: 0.9724 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "183/278 [==================>...........] - ETA: 1s - loss: 0.0702 - cross entropy: 0.0702 - Brier score: 0.0207 - tp: 181493.0000 - fp: 4238.0000 - tn: 182956.0000 - fn: 6097.0000 - accuracy: 0.9724 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "186/278 [===================>..........] - ETA: 1s - loss: 0.0701 - cross entropy: 0.0701 - Brier score: 0.0207 - tp: 184501.0000 - fp: 4300.0000 - tn: 185926.0000 - fn: 6201.0000 - accuracy: 0.9724 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "189/278 [===================>..........] - ETA: 1s - loss: 0.0701 - cross entropy: 0.0701 - Brier score: 0.0207 - tp: 187479.0000 - fp: 4381.0000 - tn: 188910.0000 - fn: 6302.0000 - accuracy: 0.9724 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "192/278 [===================>..........] - ETA: 1s - loss: 0.0701 - cross entropy: 0.0701 - Brier score: 0.0207 - tp: 190464.0000 - fp: 4453.0000 - tn: 191920.0000 - fn: 6379.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "195/278 [====================>.........] - ETA: 1s - loss: 0.0700 - cross entropy: 0.0700 - Brier score: 0.0207 - tp: 193478.0000 - fp: 4517.0000 - tn: 194888.0000 - fn: 6477.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "198/278 [====================>.........] - ETA: 1s - loss: 0.0699 - cross entropy: 0.0699 - Brier score: 0.0207 - tp: 196427.0000 - fp: 4577.0000 - tn: 197926.0000 - fn: 6574.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "201/278 [====================>.........] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0207 - tp: 199416.0000 - fp: 4644.0000 - tn: 200912.0000 - fn: 6676.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "204/278 [=====================>........] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0207 - tp: 202326.0000 - fp: 4713.0000 - tn: 203974.0000 - fn: 6779.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "207/278 [=====================>........] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0207 - tp: 205256.0000 - fp: 4778.0000 - tn: 207019.0000 - fn: 6883.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "210/278 [=====================>........] - ETA: 1s - loss: 0.0697 - cross entropy: 0.0697 - Brier score: 0.0207 - tp: 208264.0000 - fp: 4848.0000 - tn: 209980.0000 - fn: 6988.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "213/278 [=====================>........] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0206 - tp: 211244.0000 - fp: 4906.0000 - tn: 212988.0000 - fn: 7086.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "216/278 [======================>.......] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0207 - tp: 214207.0000 - fp: 4987.0000 - tn: 215998.0000 - fn: 7176.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "219/278 [======================>.......] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0207 - tp: 217179.0000 - fp: 5057.0000 - tn: 218991.0000 - fn: 7285.0000 - accuracy: 0.9725 - precision: 0.9772 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "222/278 [======================>.......] - ETA: 1s - loss: 0.0697 - cross entropy: 0.0697 - Brier score: 0.0207 - tp: 220210.0000 - fp: 5125.0000 - tn: 221936.0000 - fn: 7385.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "225/278 [=======================>......] - ETA: 1s - loss: 0.0698 - cross entropy: 0.0698 - Brier score: 0.0207 - tp: 223203.0000 - fp: 5189.0000 - tn: 224913.0000 - fn: 7495.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "228/278 [=======================>......] - ETA: 0s - loss: 0.0697 - cross entropy: 0.0697 - Brier score: 0.0206 - tp: 226208.0000 - fp: 5257.0000 - tn: 227897.0000 - fn: 7582.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9676 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "231/278 [=======================>......] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 229153.0000 - fp: 5319.0000 - tn: 230929.0000 - fn: 7687.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "234/278 [========================>.....] - ETA: 0s - loss: 0.0697 - cross entropy: 0.0697 - Brier score: 0.0206 - tp: 232183.0000 - fp: 5395.0000 - tn: 233858.0000 - fn: 7796.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "237/278 [========================>.....] - ETA: 0s - loss: 0.0697 - cross entropy: 0.0697 - Brier score: 0.0206 - tp: 235124.0000 - fp: 5468.0000 - tn: 236890.0000 - fn: 7894.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "240/278 [========================>.....] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 238076.0000 - fp: 5530.0000 - tn: 239929.0000 - fn: 7985.0000 - accuracy: 0.9725 - precision: 0.9773 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "243/278 [=========================>....] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 241054.0000 - fp: 5583.0000 - tn: 242938.0000 - fn: 8089.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "246/278 [=========================>....] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 244053.0000 - fp: 5650.0000 - tn: 245907.0000 - fn: 8198.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "249/278 [=========================>....] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 246989.0000 - fp: 5711.0000 - tn: 248958.0000 - fn: 8294.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "252/278 [==========================>...] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 249954.0000 - fp: 5776.0000 - tn: 251963.0000 - fn: 8403.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "255/278 [==========================>...] - ETA: 0s - loss: 0.0696 - cross entropy: 0.0696 - Brier score: 0.0206 - tp: 252925.0000 - fp: 5847.0000 - tn: 254967.0000 - fn: 8501.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "258/278 [==========================>...] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 255935.0000 - fp: 5910.0000 - tn: 257944.0000 - fn: 8595.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "261/278 [===========================>..] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 258877.0000 - fp: 5976.0000 - tn: 260973.0000 - fn: 8702.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "264/278 [===========================>..] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 261801.0000 - fp: 6044.0000 - tn: 264027.0000 - fn: 8800.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "267/278 [===========================>..] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 264875.0000 - fp: 6128.0000 - tn: 266921.0000 - fn: 8892.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "270/278 [============================>.] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 267839.0000 - fp: 6189.0000 - tn: 269933.0000 - fn: 8999.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "273/278 [============================>.] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 270874.0000 - fp: 6263.0000 - tn: 272873.0000 - fn: 9094.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "276/278 [============================>.] - ETA: 0s - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 273841.0000 - fp: 6329.0000 - tn: 275888.0000 - fn: 9190.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restoring model weights from the end of the best epoch: 1.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "278/278 [==============================] - 5s 20ms/step - loss: 0.0695 - cross entropy: 0.0695 - Brier score: 0.0206 - tp: 275842.0000 - fp: 6384.0000 - tn: 277849.0000 - fn: 9269.0000 - accuracy: 0.9725 - precision: 0.9774 - recall: 0.9675 - auc: 0.9973 - prc: 0.9970 - val_loss: 0.0302 - val_cross entropy: 0.0302 - val_Brier score: 0.0075 - val_tp: 76.0000 - val_fp: 433.0000 - val_tn: 45054.0000 - val_fn: 6.0000 - val_accuracy: 0.9904 - val_precision: 0.1493 - val_recall: 0.9268 - val_auc: 0.9775 - val_prc: 0.7154\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11: early stopping\n"
     ]
    }
   ],
   "source": [
    "resampled_model = make_model()\n",
    "resampled_model.load_weights(initial_weights)\n",
    "\n",
    "# Reset the bias to zero, since this dataset is balanced.\n",
    "output_layer = resampled_model.layers[-1] \n",
    "output_layer.bias.assign([0])\n",
    "\n",
    "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n",
    "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2) \n",
    "\n",
    "resampled_history = resampled_model.fit(\n",
    "    resampled_ds,\n",
    "    epochs=EPOCHS,\n",
    "    steps_per_epoch=resampled_steps_per_epoch,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=val_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "avALvzUp3T_c"
   },
   "source": [
    "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n",
    "\n",
    "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n",
    "\n",
    "This smoother gradient signal makes it easier to train the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "klHZ0HV76VC5"
   },
   "source": [
    "### Check training history\n",
    "\n",
    "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:22:45.567663Z",
     "iopub.status.busy": "2024-01-17T02:22:45.567415Z",
     "iopub.status.idle": "2024-01-17T02:22:46.178060Z",
     "shell.execute_reply": "2024-01-17T02:22:46.177365Z"
    },
    "id": "YoUGfr1vuivl"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P4/V6/Rar+76dO3dq3759Sk9PP656j9eIpnnyX3F5PQAAAADgGFl++7n8/Hw988wzevHFF7Vu3Tpde+21qq6u1rRp0yRJU6ZM0YwZM3ztZ8+erUWLFmnLli1at26dHnvsMf3tb3/TFVdcIUmqqqrS//7v/2rp0qXatm2bCgoKdNFFF6lfv37Ky8uz5DM2G5mVIIl58gAAAACAY2f5HPlJkyZp7969mjlzpoqKijR8+HAtXLjQtwBeYWGhbLaD3zdUV1frd7/7nXbu3KnIyEgNHDhQf//73zVp0iRJkt1u1+rVq/Xiiy+qvLxcGRkZGj9+vO69994Onwd/NKN6NY7Ir95ZIXeDV44wy79HAQAAAAAEGcuDvCRdf/31uv7661t8bfHixX7P77vvPt13331HPFZkZKTef//99iyv3fROjlZiVLj219Tr2z0uDc9MsLokAAAAAECQYUi4ExmG4Zsnv3I7l9cDAAAAANqOIN/JmCcPAKHhzDPP1PTp060uAwAAdJBA7usJ8p1sZK/mlevLrS0EALqwCy+8UBMmTGjxtU8++USGYWj16tWdXBUAAGgvod7XE+Q72bCeCbIZ0q7yAyqqqLW6HADokq666iotWrRIO3fuPOy1559/XqNHj9bQoUMtqAwAALSHUO/rCfKdLNoZpoFpcZK4vB5AaKtxNxzxUVvvafe2bfHjH/9Y3bt31wsvvOC3vaqqSm+88YYmTpyoyy+/XD169FBUVJSGDBmiV1999ZjOAwAAoYq+3joBsWp9VzOyV4K+3ePSyu37df6QdKvLAYAOceLMI99B5KwB3fX8tLG+56Pu/VAHvteJN8vpnaT514zzPT/toY9UVu0+rN22By9odW1hYWGaMmWKXnjhBd1+++0yDEOS9MYbb8jj8eiKK67QG2+8oVtuuUVxcXF699139ctf/lJ9+/bV2LFjj3J0AAC6Bvp66zAib4Hm+8kzIg8A1vnVr36l7777Th9//LFv2/PPP69LLrlEvXr10s0336zhw4erT58+uuGGGzRhwgS9/vrrFlYMAADaIpT7ekbkLTCy6RZ0a3e5VNfgkTPMbnFFAND+vr0n74iv2Zq+FW+24s7cVrf99Jazjq+wJgMHDtQpp5yi5557TmeeeaY2b96sTz75RPfcc488Ho8eeOABvf7669q1a5fcbrfq6uoUFRXVLu8NAEAooK+3DkHeAllJUeoW7dC+arfW7nL5RugBIJREOVrfxXRU26O56qqrdMMNN2ju3Ll6/vnn1bdvX51xxhl66KGH9Mc//lFz5szRkCFDFB0drenTp8vtPvwyPwAAuir6eutwab0FDMPQiKzm29BxeT0AWOXnP/+5bDabXnnlFb300kv61a9+JcMw9Nlnn+miiy7SFVdcoWHDhqlPnz7auHGj1eUCAIA2CtW+niBvkZG9EiQxTx4ArBQTE6NJkyZpxowZ2rNnj6688kpJUv/+/bVo0SJ9/vnnWrduna655hoVFxdbWywAAGizUO3rCfIWGdU0Ir9i+36ZpmlxNQDQdV111VXav3+/8vLylJGRIUm64447NHLkSOXl5enMM89UWlqaJk6caG2hAADgmIRiX88ceYsM7ZmgMJuhYleddlfUqkdCpNUlAUCXNG7cuMO+UE1KStKCBQt+cL/Fixd3XFEAAKDdhGJfz4i8RSIddg1Kj5MkrdzO5fUAAAAAgNYhyFtoZFaCJObJAwAAAABajyBvoZFNt51jRB4AAAAA0FoEeQuNbFrw7pvdLtXWeyyuBgAAAAAQDAjyFuqZGKnusU41eE2t2VVhdTkAcMy4+0b74DwCAAIZ/dTxa69zSJC3kGEYB+fJc3k9gCAUHh4uSaqpqbG4ktDQfB6bzysAAIHAbrdLktxut8WVBL/26uu5/ZzFRvVK1PvfFGsFQR5AELLb7UpISFBJSYkkKSoqSoZhWFxV8DFNUzU1NSopKVFCQoLvH0wAAASCsLAwRUVFae/evQoPD5fNxnhwW7V3X0+Qt1jzPPmVheUyTZN/AAMIOmlpaZLkC/M4dgkJCb7zCQBAoDAMQ+np6dq6dau2b99udTlBrb36eoK8xQb3iFe43VBpVZ127j+gzKQoq0sCgDZp7txTUlJUX19vdTlBKzw8nJF4AEDAcjgc6t+/P5fXH4f27OsJ8haLCLfrxIx4fb2jXCsL9xPkAQQtu91OEAUAIITZbDZFRERYXQbEYncBYVTT5fXMkwcAAAAAHA1BPgCM7JUgSVpZSJAHAAAAAPwwgnwAaF7wbt2eStW4GyyuBgAAAAAQyAjyASAjIVJpcRHyeE2t3llhdTkAAAAAgABGkA8Qo3oxTx4AAAAAcHQE+QAxIitBkvQV8+QBAAAAAD+AIB8gRjaNyK8sLJdpmhZXAwAAAAAIVAT5AHFSRpwcdpvKqt3avq/G6nIAAAAAAAGKIB8gnGF2DekZL4l58gAAAACAIyPIB5CRTfPkuZ88AAAAAOBICPIBpPl+8isLy60tBACATjZ37lxlZ2crIiJCOTk5Wr58+Q+2nzNnjgYMGKDIyEhlZmbqpptuUm1tbSdVCwCAtQjyAaR5wbsNRS5V1TVYXA0AAJ1j/vz5ys/P16xZs7Ry5UoNGzZMeXl5KikpabH9K6+8oltvvVWzZs3SunXr9Ne//lXz58/Xbbfd1smVAwBgjYAI8m35Fv7NN9/U6NGjlZCQoOjoaA0fPlx/+9vf/NqYpqmZM2cqPT1dkZGRys3N1aZNmzr6Yxy31LgI9UiIlNeUvt5RbnU5AAB0iscff1y/+c1vNG3aNJ144omaN2+eoqKi9Nxzz7XY/vPPP9epp56qX/ziF8rOztb48eN1+eWXH3UUHwCAUGF5kG/rt/BJSUm6/fbbtWTJEq1evVrTpk3TtGnT9P777/vaPPzww3riiSc0b948LVu2TNHR0crLywuKS+58t6FjwTsAQBfgdru1YsUK5ebm+rbZbDbl5uZqyZIlLe5zyimnaMWKFb7gvmXLFr333ns6//zzj/g+dXV1crlcfg8AAIKV5UG+rd/Cn3nmmbr44os1aNAg9e3bVzfeeKOGDh2qTz/9VFLjaPycOXN0xx136KKLLtLQoUP10ksvaffu3VqwYEEnfrJjw4J3AICupLS0VB6PR6mpqX7bU1NTVVRU1OI+v/jFL3TPPffotNNOU3h4uPr27aszzzzzBy+tnz17tuLj432PzMzMdv0cAAB0JkuD/LF8C38o0zRVUFCgDRs26Ec/+pEkaevWrSoqKvI7Znx8vHJyco54zED6lr55wbuvdpTL6zUtqwMAgEC1ePFiPfDAA/rzn/+slStX6s0339S7776re++994j7zJgxQxUVFb7Hjh072rWmLXurtHBtkXbur5Fp0n8DADpWmJVv/kPfwq9fv/6I+1VUVKhHjx6qq6uT3W7Xn//8Z5177rmS5Pv2vi3f7M+ePVt333338XyUdnNiRpwiwm0qr6nXltJq9UuJsbokAAA6THJysux2u4qLi/22FxcXKy0trcV97rzzTv3yl7/Ur3/9a0nSkCFDVF1drauvvlq33367bLbDxymcTqecTmf7f4Am/15bpEfe3yBJSowK1+Ae8Y2PjHgN6RGvzKRIGYbRYe8PAOhaLL+0/ljExsZq1apV+uKLL3T//fcrPz9fixcvPubjdfS39G0RbrdpaI8ESVxeDwAIfQ6HQ6NGjVJBQYFvm9frVUFBgcaNG9fiPjU1NYeFdbvdLkmWjYbHRYRpUHqcwmyG9tfU65NNpXpq8Xe67pWV+tEjH+nbPQev9ttaWq0te6u48g4AcMwsHZE/lm/hpcbL7/v16ydJGj58uNatW6fZs2frzDPP9O1XXFys9PR0v2MOHz68xeN19Lf0bTWiV4KWbyvTV4X79fPRzOEDAIS2/Px8TZ06VaNHj9bYsWM1Z84cVVdXa9q0aZKkKVOmqEePHpo9e7Yk6cILL9Tjjz+uESNGKCcnR5s3b9add96pCy+80BfoO9svx2Xrl+OyVVvv0cbiSq3ZVaG1u1xau6tC20qrdUJqrK/tnz/arDdW7FSMM0wnZsRpSI94De7R+LN3cozsNkbuAQA/zNIgf+i38BMnTpR08Fv466+/vtXH8Xq9qqurkyT17t1baWlpKigo8AV3l8ulZcuW6dprr23vj9AhmufJr9xebm0hAAB0gkmTJmnv3r2aOXOmioqKNHz4cC1cuNA3Ta6wsNBvBP6OO+6QYRi64447tGvXLnXv3l0XXnih7r//fqs+gk9EuF1DeyZoaM8E3zaP1/QL5x6vKUeYTVV1DVq+tUzLt5b5XotxhunLO3IVEd74hURpVZ0SIsMVZg/KiygBAB3EMC1ekWX+/PmaOnWq/vKXv/i+hX/99de1fv16paamHvYt/OzZszV69Gj17dtXdXV1eu+993Trrbfqqaee8s2Ve+ihh/Tggw/qxRdfVO/evXXnnXdq9erV+vbbbxUREXHUmlwul+Lj41VRUaG4uLgO/fwt2VtZpzH3fyjDkL6eNV5xEeGdXgMAILBY3TeFGqvPZ73Hq80lVVq7q6Lxsdulb3e7lB4fof/cfKav3c/nLdHqXeUalN40cp/ROPe+f2qMwgn3ABBS2tI3WToiL7X9W/jq6mr97ne/086dOxUZGamBAwfq73//uyZNmuRr84c//MG36E15eblOO+00LVy4sFUhPhB0j3UqKylKhWU1WlVYrh+d0N3qkgAAQDsKt9s0KD1Og9Lj9LOmaXQer6nSqjpfG9M0tb2sWrX1Xn1VWK6vCst9rznCbDqtX7Keu3KMb5vXa8rGZfkA0CVYPiIfiKz+ll6Spr/2lRas2q3puf01PfcES2oAAASOQOibQkmwnE+v19TWfdUHR+53ubR2d4Uqaxt01oDuen7aWF/bUx/8jxKiwjWkR7xO6tG4Wv7AtFjfZfoAgMAWVCPyaNnIXolasGq3Vh7y7TsAAOhabDZDfbvHqG/3GF00vIekxpH6wrIa1dZ7fe1KKmu1q/yAdpUf0De7XdIXjXfgsdsM9eoWpQuHZuimcw8ODGwuqVLPxEhCPgAEKYJ8gGpe8O6rwv1cKgcAAHwMw1CvbtF+27rHOPXpLWf5Ru3XNI3g76t2a8veapXXuH1tK2rqlfv4x5KktLgIZXWLUq+kKGUlRSmrW5ROyohXv5SYTv1MAIC2IcgHqIFpsYpy2FVZ26DNe6v8blsDAABwKMMw1DMxSj0TozRhcOPtd03TVJGrVlv2VqtbjMPXtqSyVjHOMFXVNajIVasiV63fyvm/PLmX7p04WJJUWVuvW/7famUlRatXc+DvFqX0+EhukwcAFiLIB6gwu01De8Zr6ZYyrdy+nyAPAADaxDAMpcdHKj0+0m97/9RYrblrvMqq3dpeVqMdZTXavq/xUVhWrZMyDs7L3L6vRu+tKTrs2A67TT0TI3XlqdmaMi5bklTX4NH2fTXKSorikn0A6GAE+QA2MiuxMcgX7tdlY7OsLgcAAIQIwzDULcapbjFO33S+lnSPdeqOCwapsKw56Ndo5/4auT1ebSmtVt0h8/Q3l1Tpgic+lSSlxjnVKyn64GX73aI0IjNRWd2iOvyzAUBXQJAPYKN6NXasK7bvt7gSAADQFaXGRejXp/fx2+bxmtpTcUCF+2qUmXQwmJfX1Cs2IkyVtQ0qdtWp2FWn5dsOXrI/47yBuuaMvpKkLXur9NiijRqZlaix2UkalB6rMLtNAIDWIcgHsBFN35B/17RITUKU4yh7AAAAdCy77eB8/EOd2i9Zq2eNV3lNvbaXNY7eF+6rbrxsv6xGA9IOThPcWFyld1fv0bur90iSoh12jeyVqDHZSRqTnaQRWQlcng8AP4AgH8CSoh3qnRytraXV+mpHuc4akGJ1SQAAAEdkGIYSox1KjHZoeGbCEdsNSo/VHyYM0Jfb9uuLbWWqrG3QJ5tK9cmmUknSHy8b7rvdXvOK+wxoAMBBBPkANyIroTHIb99PkAcAACGhV7do/e7MfpIkr9fUhuJKfbGtTMu3lumLbWUa2zvJ1/aV5YV6eOEGDUiN1ZjejaP2Y3snHbaIHwB0JQT5ADeqV6LeXLlLKwqZJw8AAEKPzWZoUHqcBqXHacq4bJmmKcM4eGu7HWU1kqQNxZXaUFypvy8tlCT1TIzU2Owk3fHjE5UUzWg9gK6FIB/gmleSXVVYLo/X5J6tAAAgpB0a4iVp9k+H6vfjB+jLbWVavrXxUvxvdldo5/4DKq3aowcvGepr+/Ky7Trg9mhMdpJOyohjAT0AIYsgH+BOSI1VjDNMVXUN2lhcqUHpcUffCQAAIIQkxzg1YXC6JgxOlyRV1TXoq8L92l1+QI6wg2H9hc+2aVNJlSQpymHXyKzmBfQSNSIrUZEOFtADEBoI8gHObjM0LDNen23ep5WF+wnyAACgy4txhun0/t39tpmmqUtH9fTNs3fVNujTzaX6dHPjAnr9UmL0Yf4ZvvY17gZFOfinMIDgxN9eQWBUVqI+27xPK7bv1+ScXlaXAwAAEHAMw9A1Z/TVNWf0lddramNJpb7Ytl9fNAX7EYesou/xmsq5v0Bp8REa0ztJY7OTNKZ3knoksIAegOBAkA8CI3o1zpP/qrDc2kIAAACCgM1maGBanAamxemXJ/eSaZqqa/D6Xt9cUqXKugZVllRpU0mVXlnWuIBej4RIjclO1CWjevpG/A+4PVpX5FJCZLgSohyKiwgL+bn3pmnKVdsgw5DiIsIlSaVVdfrHip0qq3Yf9iivcevynCzNOG+QJKmytl4z//mN4iPDFRcZrvimR1xEmOIjw9UzKYovTYDjRJAPAiMzG4P81tJqlVW7WZkVAACgDQzDUET4wfnxA9JiteKO3MYR+21lTQvoubSr/IB2rTqg/qmxviC/tbRaP/3z537Hi3WGKT4qXAlR4frF2F76RU6WJKmipl6vfVGohKhwxUc6FB/Z2Kbxebgiw+2HLebXGeoaPNpfXa+yarcSosKV0RSi91Qc0J8/+s4/mNe4tb/arQavqRvO7qffjx8gSXIdqNeD/15/xPewHfK5yqrdeuurXUds+4ucLD1w8RBJUnmNW+c89rEv9B8M/o2hf2RWos4ZlCqp8VaF3+5xNb4eFa4YR5hsLASNLoogHwTio8LVt3u0vttbra8K9/v+MgMAAMCx6Rbj1ITBaZowOE2SVF3XoK8Ky7Vi+36NaroaUpLqPV71TIxURU29KusaJKlxNL+uQTv3H9D+Grev7a7yA5r9A2H3mjP6+EatSyprNeP/rVF8U8hPiHT4An98VLj6JEerV7fow47h9Zpy1dZrX3Vj4C6rdis7OVonpMZKkrbvq9Zdb39zSCivV1VT3ZL8wnltvVd/W7r9iPVW1h7cr3usU5eM7Kmk6HAlRjvULdqhxCiHkqIdTV9WHBxoio0I123nD1TFgfqmR4MqDtTL1fQ4dDS+4kDjZ9lX7VZLJudk+f7t66qt14//9KnvNZvR+F7NI/55J6Xq+rP7S2qcPvH0f7coPjJcSdHhSop2NtYe5VBClIM7QSHoEeSDxKheifpub7VWbCfIAwAAtLdoZ5hO65+s0/on+20flpmgT285W5LU4PHKVdug8hq3yptCavYhYTvSYddPR/TwvVZe4276Wa8Gr+m7TF2S9lbWqWB9yRHrueZHfTTj/MbQv6v8gCb833/lCLOp/EC9PF7Tr+31Z/XTzXmN4dw0pY827D3seHabocQoh8JsB6cFpMQ69T/n9FdSVLiSYpxKinIoMTpc3aKdSogK97uKITYiXI/9fNhRz6MkJUU7dPWP+raqbVp8hBZOP10VNfWHBP/GwF9xoF5jeif52ta4Peoe61TFgXq5G7zymvK1l6ShPeN9bStr6/XQwpa/VDEM6ZKRPfXozxo/j9drasaba5QY7Tgs9CdFO9QtxqkYJ7EJgYU/kUFiZFaiXv9yp1YW7re6FAAAgC4pzG5TUrTjiNMceydH6/FJww/bbpqmatweHXpVfVpchB786ZDGoN8U9isOHAz+PZOifG3La9yNVwPUHdw/xhmmpGiHEqMd6h7r9G1PjYvQw5cMbQqlBx9xEWGHXdYf7QxT/rknHNvJaCfOMLsGprXurkwZCZH64vZcSVJtvccX9psfqXERfu0vHdVT5TWNVy3sr2mcWlBxoF6mKUWEH/xCo+JAveZ/ueOI73vB0HTN/cVISY2h/4q/LlNi05ceSU1hv/l890yMUu/kw6+kANobQT5IjGy6xOvrHRVq8HhDfpEVAACAUGEYhqK/N6LbLcapy8ZmtWr/fikxKvj9GXI3eH2XsjvD7C22jXTY9fMxmcddc6CLCLcrItyulO+F92YJUQ7fiPuhGjxe7a+p16FX1tvthn5/7gm+9QHKaup90xbKqt1KOmTaQMWBen3+3b4j1vXjoel6sin0e7ymznz0IyVENgX9qIOj/fFRDvXtHq1T+ib72r66vFC+ay3Mxt+an2cmRemsASm+93lpyTbflRlNTX1teyRE+qaMSNILn21VvceUefDovn3S4iN00fAevu0vfr5NNW6PbEbjlQs2w5BhGDIkdYtx+LV9++vdqqpt8LU1DKOxvaTYiDCNP+lgDZ9s2qvKpraSIZvv2I3/X57a7+CVMKt3lquqtqHxfQ3JkHxrIdgMw2/qy8rC/dpX5ZbHa8prmmrwmvJ6TXm8pgxD+unInr62i74t1vZ91fJ4TXlMUx5P409v0/Obxw/wfdn1+hc7tHpXuTxe+V73NB3XY5p67GfDfFesLPq2WOee2PlXTBPkg0S/7jGKjQhTZW2D1hdVanCP+KPvBAAAgKDnDLOrb/cYq8sICWF2m98VDFLjyvw3nNP/iPscOpUhItyuP142vHGU/5B1CBpH/d1+Uy0qDtRrR9kB7dCBFo87cXiGL8g3eL26Y8HaI9aQd1KqX5C/+1/fHjbFotnp/ZP9gvyjH2z0WyfhUKN7JfqF8yc/2qy9lXUttj0pI86v7WMfbND2fTUttu2dHO0X5O9/d53WF1W22DYtLkJLbzvH93zW298c8W5dCVHhWjVzvO/5Iws3aMmWlr9YcYTZ/IL8a8sLf3A6S/65A2Rv+oLnv5v26p3Ve47Y9sGfDvEF+V37Wz4HHY0gHyRsNkMjshL13417tbJwP0EeAAAA6ASHLowX6bD7hdkfEuMM01u/O8U3sr+/xq2y6nqVVdepvKbe79/zNsPQhEOC76GzIAxDGtYzwe/YFwxJl9c0fSPIxiFtB6TF+rX98dB01TV4fW2afzFkqHdylF/bC4dmyFXbOP3ANBvH8L2mKa8p9Uz0v2Xgaf2SdUJqXWM782A7U1Lq974sGdwjXnER4TLV1MY8+LNbjH/brKQoVdc1+I5pNh3Ta/qvMyFJ/VNjdKDeI7vNaHwYjT9tNkOO713BfHKfboqNaLzTgd0wFGZvvILAbmv8aZqm7+ScNzhdfbvH+I7b2E6+9o6wg8f+/roancUwTbPlr3K6MJfLpfj4eFVUVCgurnVzdjrDnA83as6HmzRxeIbmXDbC6nIAAJ0oUPumYMX5BAAEmrb0TUy0DiIjsxrng6w8wqUmAAAAAIDQR5APIsOzEmQYUmFZzRHnrgAAAAAAQhtBPojERYTrhJTGOS/chg4AAAAAuiaCfJAZ2StBEkEeAAAAALoqgnyQGdE0T/6r7eXWFgIAAAAAsARBPsg0L3j39c5y1Xu8FlcDAAAAAOhsBPkg0yc5WglR4apr8Orb3S6rywEAAAAAdDKCfJCx2QyNyEyQxDx5AAAAAOiKCPJBiPvJAwAAAEDXRZAPQiN7NQX57YzIAwAAAEBXQ5APQsMyE2QzpF3lB1TsqrW6HAAAAABAJyLIB6EYZ5gGpMVJYlQeAAAAALqagAjyc+fOVXZ2tiIiIpSTk6Ply5cfse0zzzyj008/XYmJiUpMTFRubu5h7a+88koZhuH3mDBhQkd/jE41MitBEgveAQAAAEBXY3mQnz9/vvLz8zVr1iytXLlSw4YNU15enkpKSlpsv3jxYl1++eX66KOPtGTJEmVmZmr8+PHatWuXX7sJEyZoz549vserr77aGR+n07DgHQAAAAB0TZYH+ccff1y/+c1vNG3aNJ144omaN2+eoqKi9Nxzz7XY/uWXX9bvfvc7DR8+XAMHDtSzzz4rr9ergoICv3ZOp1NpaWm+R2JiYmd8nE4zqmnBuzU7K1TX4LG4GgAAAABAZ7E0yLvdbq1YsUK5ubm+bTabTbm5uVqyZEmrjlFTU6P6+nolJSX5bV+8eLFSUlI0YMAAXXvttdq3b98Rj1FXVyeXy+X3CHS9ukUpKdoht8erb3YHfr0AAAAAgPZhaZAvLS2Vx+NRamqq3/bU1FQVFRW16hi33HKLMjIy/L4MmDBhgl566SUVFBTooYce0scff6zzzjtPHk/LI9ezZ89WfHy875GZmXnsH6qTGIZxcJ48C94BAAAAQJcRZnUBx+PBBx/Ua6+9psWLFysiIsK3/bLLLvP9PmTIEA0dOlR9+/bV4sWLdc455xx2nBkzZig/P9/33OVyBUWYH5GVqA/Xlegr5skDAAAAQJdh6Yh8cnKy7Ha7iouL/bYXFxcrLS3tB/d99NFH9eCDD+qDDz7Q0KFDf7Btnz59lJycrM2bN7f4utPpVFxcnN8jGDTPk1/BiDwAAAAAdBmWBnmHw6FRo0b5LVTXvHDduHHjjrjfww8/rHvvvVcLFy7U6NGjj/o+O3fu1L59+5Sent4udQeKoT3jZbcZKnLVanf5AavLAQAAAAB0AstXrc/Pz9czzzyjF198UevWrdO1116r6upqTZs2TZI0ZcoUzZgxw9f+oYce0p133qnnnntO2dnZKioqUlFRkaqqqiRJVVVV+t///V8tXbpU27ZtU0FBgS666CL169dPeXl5lnzGjhLlCNOg9FhJ3E8eAAAAALoKy4P8pEmT9Oijj2rmzJkaPny4Vq1apYULF/oWwCssLNSePXt87Z966im53W5deumlSk9P9z0effRRSZLdbtfq1av1k5/8RCeccIKuuuoqjRo1Sp988omcTqcln7Ej+e4nv73c2kIAAAAAAJ3CME3TtLqIQONyuRQfH6+KioqAny//z1W7dONrqzQsM0H/vO5Uq8sBAHSQYOqbggHnEwAQaNrSN1k+Io/j0zwi/+3uCtXWt3x7PQAAAABA6CDIB7meiZFKjnGq3mNq7a4Kq8sBAAAAAHQwgnyQMwxDI7MSJLHgHQAAAAB0BQT5EMD95AEAAACg6yDIh4CRTUF+ZWG5WLsQAAAAAEIbQT4EDOkRrzCbob2Vddq5/4DV5QAAAAAAOhBBPgREhNt1Ukbj7QmYJw8AAAAAoY0gHyJ8l9czTx4AAAAAQhpBPkQ0309+ZWG5tYUAAAAAADoUQT5ENI/Ir9vj0gG3x+JqAAAAAAAdhSAfIjLiI5Qa51SD19TqneVWlwMAAAAA6CAE+RBhGMbB+8mz4B0AIMjMnTtX2dnZioiIUE5OjpYvX/6D7cvLy3XdddcpPT1dTqdTJ5xwgt57771OqhYAAGsR5EOIb5789nJrCwEAoA3mz5+v/Px8zZo1SytXrtSwYcOUl5enkpKSFtu73W6de+652rZtm/7xj39ow4YNeuaZZ9SjR49OrhwAAGuEWV0A2s+IpiD/VeF+maYpwzAsrggAgKN7/PHH9Zvf/EbTpk2TJM2bN0/vvvuunnvuOd16662HtX/uuedUVlamzz//XOHh4ZKk7OzsziwZAABLMSIfQgb3iJPDbtO+arcKy2qsLgcAgKNyu91asWKFcnNzfdtsNptyc3O1ZMmSFvd5++23NW7cOF133XVKTU3V4MGD9cADD8jjOfJir3V1dXK5XH4PAACCFUE+hDjD7BrcI06StIL7yQMAgkBpaak8Ho9SU1P9tqempqqoqKjFfbZs2aJ//OMf8ng8eu+993TnnXfqscce03333XfE95k9e7bi4+N9j8zMzHb9HAAAdCaCfIg5eD95gjwAIDR5vV6lpKTo6aef1qhRozRp0iTdfvvtmjdv3hH3mTFjhioqKnyPHTt2dGLFAAC0L+bIh5iRvRKlT7ey4B0AICgkJyfLbreruLjYb3txcbHS0tJa3Cc9PV3h4eGy2+2+bYMGDVJRUZHcbrccDsdh+zidTjmdzvYtHgAAizAiH2KaR+TXF7lUXddgcTUAAPwwh8OhUaNGqaCgwLfN6/WqoKBA48aNa3GfU089VZs3b5bX6/Vt27hxo9LT01sM8QAAhBqCfIhJi49QRnyEvKb09Y5yq8sBAOCo8vPz9cwzz+jFF1/UunXrdO2116q6utq3iv2UKVM0Y8YMX/trr71WZWVluvHGG7Vx40a9++67euCBB3TddddZ9REAAOhUXFofgkb2StTu1Xu0snC/TumXbHU5AAD8oEmTJmnv3r2aOXOmioqKNHz4cC1cuNC3AF5hYaFstoNjD5mZmXr//fd10003aejQoerRo4duvPFG3XLLLVZ9BAAAOpVhmqZpdRGBxuVyKT4+XhUVFYqLi7O6nDZ77tOtuuedb3X2wBQ9d+UYq8sBALSDYO+bAg3nEwAQaNrSN3FpfQga2evgyvV8TwMAAAAAoYUgH4JOTI+TM8ym8pp6bSmttrocAAAAAEA7IsiHIEeYTUN7xkuSVm7nfvIAAAAAEEoI8iGq+TZ0KwvLrS0EAAAAANCuCPIhakRTkP+qkBF5AAAAAAglBPkQNbJXgiRpQ3GlXLX11hYDAAAAAGg3BPkQlRIbocykSJmm9PWOcqvLAQAAAAC0E4J8CPPNk99ebm0hAAAAAIB2Q5APYQcXvGOePAAAAACECoJ8CDs0yHu9psXVAAAAAADaA0E+hA1Mj1VkuF2VtQ36bm+V1eUAAAAAANoBQT6EhdttGtozXhKX1wMAAABAqCDIh7iRvVjwDgAAAABCCUE+xDXPk1/BiDwAAAAAhISACPJz585Vdna2IiIilJOTo+XLlx+x7TPPPKPTTz9diYmJSkxMVG5u7mHtTdPUzJkzlZ6ersjISOXm5mrTpk0d/TEC0sisBEnS5pIqVdTUW1sMAAAAAOC4WR7k58+fr/z8fM2aNUsrV67UsGHDlJeXp5KSkhbbL168WJdffrk++ugjLVmyRJmZmRo/frx27drla/Pwww/riSee0Lx587Rs2TJFR0crLy9PtbW1nfWxfL7ZXaE//ONrvf317k5/b0nqFuNUdrcoSdJXOxiVBwAAAIBgZ3mQf/zxx/Wb3/xG06ZN04knnqh58+YpKipKzz33XIvtX375Zf3ud7/T8OHDNXDgQD377LPyer0qKCiQ1DgaP2fOHN1xxx266KKLNHToUL300kvavXu3FixY0OIx6+rq5HK5/B7tZfGGvXr9y516/rOt7XbMtjp4G7pyy2oAAAAAALSPYwryO3bs0M6dO33Ply9frunTp+vpp59u03HcbrdWrFih3NzcgwXZbMrNzdWSJUtadYyamhrV19crKSlJkrR161YVFRX5HTM+Pl45OTlHPObs2bMVHx/ve2RmZrbpc/yQn4/OVJjN0FeF5fpmd0W7HbctRvgWvGNEHgAAAACC3TEF+V/84hf66KOPJElFRUU699xztXz5ct1+++265557Wn2c0tJSeTwepaam+m1PTU1VUVFRq45xyy23KCMjwxfcm/dryzFnzJihiooK32PHjh2t/gxH0z3WqbzBaZKkV5YVtttx22JU04j8qh3l8nhNS2oAAAAAALSPYwrya9eu1dixYyVJr7/+ugYPHqzPP/9cL7/8sl544YX2rO8HPfjgg3rttdf01ltvKSIi4piP43Q6FRcX5/doT5NzsiRJC77apaq6hnY9dmsMSItVtMOuqroGbSqp7PT3BwAAAAC0n2MK8vX19XI6nZKkDz/8UD/5yU8kSQMHDtSePXtafZzk5GTZ7XYVFxf7bS8uLlZaWtoP7vvoo4/qwQcf1AcffKChQ4f6tjfvdyzH7Cjj+nRTn+7RqnZ7tOCrXUffoZ3ZbYaGZSZI4n7yAAAAABDsjinIn3TSSZo3b54++eQTLVq0SBMmTJAk7d69W926dWv1cRwOh0aNGuVbqE6Sb+G6cePGHXG/hx9+WPfee68WLlyo0aNH+73Wu3dvpaWl+R3T5XJp2bJlP3jMjmQYhibn9JIk/X3pdplm51/e7rufPPPkAQDt5IsvvtCyZcsO275s2TJ9+eWXFlQEAEDXcExB/qGHHtJf/vIXnXnmmbr88ss1bNgwSdLbb7/tu+S+tfLz8/XMM8/oxRdf1Lp163Tttdequrpa06ZNkyRNmTJFM2bM8HvvO++8U88995yys7NVVFSkoqIiVVVVSWoMzdOnT9d9992nt99+W2vWrNGUKVOUkZGhiRMnHsvHbReXjuypPsnROn9IuhosmKc+qmnBu68KCfIAgPZx3XXXtbiuzK5du3TddddZUBEAAF1D2LHsdOaZZ6q0tFQul0uJiYm+7VdffbWioqLadKxJkyZp7969mjlzpoqKijR8+HAtXLjQt1hdYWGhbLaD3zc89dRTcrvduvTSS/2OM2vWLN11112SpD/84Q+qrq7W1VdfrfLycp122mlauHDhcc2jP17xUeEq+P0ZMgzDkvcfkZUgSdpSWq391W4lRjssqQMAEDq+/fZbjRw58rDtI0aM0LfffmtBRQAAdA2GeQzXeR84cECmafpC+/bt2/XWW29p0KBBysvLa/ciO5vL5VJ8fLwqKirafeE7K5392GJt2Vut564crbMHph59BwBAwAjEvqlbt2565513Dpu69vnnn+uCCy7Q/v2BexVYIJ5PAEDX1pa+6Zgurb/ooov00ksvSZLKy8uVk5Ojxx57TBMnTtRTTz11LIfsMuo9Xi1cu0cL17Z+UcD2wjx5AEB7Gj9+vO8Wrs3Ky8t122236dxzz7WwMgAAQtsxBfmVK1fq9NNPlyT94x//UGpqqrZv366XXnpJTzzxRLsWGGreWrlLv/37Sj20cEOnL3rXPE+elesBAO3hkUce0Y4dO9SrVy+dddZZOuuss9S7d28VFRXpscces7o8AABC1jEF+ZqaGsXGxkqSPvjgA/30pz+VzWbTySefrO3bt7drgaHm/KHpinGGaWtptT7/bl+nvnfziPzXO8vV4PF26nsDAEJPz549tXr1aj388MM68cQTNWrUKP3xj3/UmjVrlJmZaXV5AACErGNa7K5fv35asGCBLr74Yr3//vu66aabJEklJSXMMzuKGGeYJo7I0N+XFurlZdt1ar/kTnvv/ikxinWGqbKuQRuKK3VSRnynvTcAILTU19dr4MCBeuedd3T11VdbXQ4AAF3KMY3Iz5w5UzfffLOys7M1duxY3yI3H3zwgUaMGNGuBYai5nvKf/BNsUpctZ32vjaboeFNq9evZJ48AOA4hIeHq7a28/owAABw0DEF+UsvvVSFhYX68ssv9f777/u2n3POOfq///u/disuVA1Kj9OoXolq8Jqa/8Xh99/tSM2X168sLO/U9wUAhJ7rrrtODz30kBoaGqwuBQCALuWYLq2XpLS0NKWlpWnnzp2SGufJjR07tt0KC3VXnJylFdv369XlhfrdWf1kt3XO/eVHNi94V8iIPADg+HzxxRcqKCjQBx98oCFDhig6Otrv9TfffNOiygAACG3HNCLv9Xp1zz33KD4+Xr169VKvXr2UkJCge++9V14vi6i1xnmD05UQFa6UuAjtrazrtPcdnpkgSdq+r0alVZ33vgCA0JOQkKBLLrlEeXl5ysjIUHx8vN8DAAB0jGMakb/99tv117/+VQ8++KBOPfVUSdKnn36qu+66S7W1tbr//vvbtchQFBFu16KbzlD3WGenvm98ZLj6p8RoU0mVVm7fr/EnpXXq+wMAgp/X69UjjzyijRs3yu126+yzz9Zdd92lyMhIq0sDAKBLOKYR+RdffFHPPvusrr32Wg0dOlRDhw7V7373Oz3zzDN64YUX2rnE0NXZIb6Z737yzJMHAByD+++/X7fddptiYmLUo0cPPfHEE7ruuuusLgsAgC7jmIJ8WVmZBg4ceNj2gQMHqqys7LiL6moqauq1YnvnnbeDC94xTx4A0HYvvfSS/vznP+v999/XggUL9K9//Usvv/wy0+sAAOgkxxTkhw0bpieffPKw7U8++aSGDh163EV1JWt2VmjsAx/qmr+tkLuhc/4BNLJXgiRp9c5y1Xv4RxcAoG0KCwt1/vnn+57n5ubKMAzt3r3bwqoAAOg6jmmO/MMPP6wLLrhAH374oe8e8kuWLNGOHTv03nvvtWuBoW5geqziI8NVUlmnD74t0o+HZnT4e/ZJjlFcRJhctQ1at8eloT0TOvw9AQCho6GhQREREX7bwsPDVV9fb1FFAAB0LccU5M844wxt3LhRc+fO1fr16yVJP/3pT3X11Vfrvvvu0+mnn96uRYaycLtNl43J1BP/2ay/L93eKUHeZjM0sleiFm/Yq5Xb9xPkAQBtYpqmrrzySjmdB9d6qa2t1W9/+1u/W9Bx+zkAADrGMd9HPiMj47DV6b/++mv99a9/1dNPP33chXUll43N0pMfbdbSLWXaXFKlfikxHf6eI7Mag/zbX+/WpDFZinTYO/w9AQChYerUqYdtu+KKKyyoBACArumYgzzaT0ZCpM4emKoP1xXrlWWFmnnhiR3+nhcMTddTi7/TysJyTXthuf46dYyinfxxAAAc3fPPP291CQAAdGnHtNgd2t/kk7MkSf9YsUMH3J4Of7++3WP00lVjFeMM09ItZfrlX5fJVcvcRgAAAAAIdAT5APGj/t3VMzFSNW6Pvuqk28KNyU7Sy7/OUXxkuFYWlmvyM8u0v9rdKe8NAAAAADg2bbqW+qc//ekPvl5eXn48tXRpdpuhOZOGKyspSilxEUffoZ0My0zQq785WVf8dZnW7KrQ5c8s1d+uylH3WOfRdwYAAAAAdLo2jcjHx8f/4KNXr16aMmVKR9Ua8kZnJ3VqiG92Ykac5l99slJinVpfVKlJTy9RUUVtp9cBAAAAADg6wzRN0+oiAo3L5VJ8fLwqKioUFxdnSQ37q91KjHZ06ntuK63W5GeXaVf5AWUlRenlX+coMymqU2sAALQsEPqmUML5BAAEmrb0TcyRDzDlNW5NfnapTnvoP6rs5MXnspOjNf+ak5WVFKXCshpN+ssSbS2t7tQaAAAAAAA/jCAfYOIjw1XsqlO126MFX+3q9PfvmRil168Zp77do7W7olY//8sSbSqu7PQ6AAAAAAAtI8gHGMMwNDmn8VZ0Ly8rlBUzH9LiIzT/mnEamBarvZV1mvT0Un2zu6LT6wAAAAAAHI4gH4B+OqKnIsJtWl9UqZWddCu670uOceq1q0/W0J7xKqt26/Knl2rVjnJLagEAAAAAHESQD0DxUeG6cGiGJOnvSwstqyMhyqG//zpHo3olylXboCueXablW8ssqwcAAAAAQJAPWFec3EuS9O6aPSqrdltWR1xEuF761ViN69NNVXUNmvrccn26qdSyegAAAACgqyPIB6ihPeM1uEec3A1evblyp6W1RDvD9Py0MTrjhO46UO/Rr178Qv9ZX2xpTQAAAADQVRHkA5RhGLop9wQ99rNhvtF5K0WE2/X0lFEaf2Kq3A1eXfO3Ffr3mj1WlwUAAAAAXQ5BPoCdMyhVl4zqqYhwu9WlSJKcYXbNnTxSFw7LUL3H1PWvfmXJLfIAAAAAoCsjyKNNwu02zZk0XJeO6imP19RNr6/S/C+sW5APAAAAALoagnyAM01Tz36yReP/72MVu2qtLkeSZLcZeviSobri5CyZpnTL/1ujFz/fZnVZAAAAANAlEOQDnGEYev+bIm0srtL8L3ZYXY6PzWbo3osG69en9ZYkzXr7G/3l4+8srgoAAAAAQh9BPghMzmlc7O7V5YVq8HgtruYgwzB0+wWDdMPZ/SRJs/+9XnM+3CjTNC2uDAAAAABCF0E+CJw3JE1J0Q7tqajVRxv2Wl2OH8Mw9PvxA3Tz+BMkSXM+3KSHFm4gzAMA2mzu3LnKzs5WRESEcnJytHz58lbt99prr8kwDE2cOLFjCwQAIEBYHuTb0ml/8803uuSSS5SdnS3DMDRnzpzD2tx1110yDMPvMXDgwA78BB3PGWbXz0b1lCS9vGy7xdW07Pqz++uOCwZJkuZ9/J3u/te38noJ8wCA1pk/f77y8/M1a9YsrVy5UsOGDVNeXp5KSkp+cL9t27bp5ptv1umnn95JlQIAYD1Lg3xbO+2amhr16dNHDz74oNLS0o543JNOOkl79uzxPT799NOO+gid5vKxWZKkjzfu1Y6yGouradmvT++j+yYOliS98Pk23fbWGnkI8wCAVnj88cf1m9/8RtOmTdOJJ56oefPmKSoqSs8999wR9/F4PJo8ebLuvvtu9enTpxOrBQDAWpYG+bZ22mPGjNEjjzyiyy67TE6n84jHDQsLU1pamu+RnJzcUR+h02QnR+v0/skyTemV5YF7u7crTu6lR382TDZDeu2LHbr5ja8Dal4/ACDwuN1urVixQrm5ub5tNptNubm5WrJkyRH3u+eee5SSkqKrrrrqqO9RV1cnl8vl9wAAIFhZFuSPtdNujU2bNikjI0N9+vTR5MmTVVj4w8E3WDr3KeOydcGQdJ0zMMXqUn7QpaN66o+XjZDdZuitr3bphle/kruBMA8AaFlpaak8Ho9SU1P9tqempqqoqKjFfT799FP99a9/1TPPPNOq95g9e7bi4+N9j8zMzOOuGwAAq1gW5I+l026NnJwcvfDCC1q4cKGeeuopbd26VaeffroqKyuPuE+wdO7nnpiquZNHanR2ktWlHNWFwzL01OSRctht+vfaIl379xWqrfdYXRYAIARUVlbql7/8pZ555plWX3U3Y8YMVVRU+B47dgTOLV0BAGirMKsLaG/nnXee7/ehQ4cqJydHvXr10uuvv37ES+9mzJih/Px833OXyxWwYT6YjD8pTU9PGaVr/rZCBetL9OsXv9TTU0YpyhFyf+wAAMchOTlZdrtdxcXFftuLi4tbXBPnu+++07Zt23ThhRf6tnm9jVd+hYWFacOGDerbt6/fPk6n8wen5QEAEEwsG5Fva6d9rBISEnTCCSdo8+bNR2zjdDoVFxfn9whk3+2t0r3vfKvNJUe+yiBQnDkgRc9PG6Moh12fbi7Vlc99oaq6BqvLAgAEEIfDoVGjRqmgoMC3zev1qqCgQOPGjTus/cCBA7VmzRqtWrXK9/jJT36is846S6tWreLLeABAyLMsyLe10z5WVVVV+u6775Sent5ux7Tag/9er79+ulV/Xxq4i94d6pS+yfrbVWMV6wzT8m1luuLZZaqoqbe6LABAAMnPz9czzzyjF198UevWrdO1116r6upqTZs2TZI0ZcoUzZgxQ5IUERGhwYMH+z0SEhIUGxurwYMHy+FwWPlRAADocJauWt+WTltqXCCv+Zt3t9utXbt2adWqVX6j7TfffLM+/vhjbdu2TZ9//rkuvvhi2e12XX755Z3++TrKFSf3kiT9v5U7dcAdHPPOR/VK0iu/OVkJUeFataNclz+zVPuq6qwuCwAQICZNmqRHH31UM2fO1PDhw7Vq1SotXLjQt5ZOYWGh9uzZY3GVAAAEBsM0TUtv9P3kk0/qkUceUVFRkYYPH64nnnhCOTk5kqQzzzxT2dnZeuGFFyRJ27ZtU+/evQ87xhlnnKHFixdLki677DL997//1b59+9S9e3eddtppuv/++w+bK/dDXC6X4uPjVVFREZCX2Xu9ps549CPtKDughy8dqp+PDp5LCNftcemXf12m0iq3+qfE6OVf5yglLsLqsgAg4AV63xRsOJ8AgEDTlr7J8iAfiIKhc39q8Xd6aOF6DesZr39ef5rV5bTJ5pIqTX52qYpddeqdHK2Xf52jjIRIq8sCgIAWDH1TMOF8AgACTVv6Jksvrcex+/nongq3G/p6Z4XW7Kywupw26ZcSo9evGaceCZHaWlqtn/9liQr31VhdFgAAAAAEBYJ8kOoW49R5gxsX8Ht52XaLq2m7Xt2i9fpvxym7W5R27j+gn/9lib7bW2V1WQAAAAAQ8AjyQeyKk3spISpcKbHBeV/cHgmRev2aceqfEqMiV60m/WWJ1he5rC4LAAAAAAIaQT6IjclO1NIZ5yh//ACrSzlmKXEReu3qkzUoPU6lVW5d9vRSrd0VXFMFAAAAAKAzEeSDmGEYigi3W13GcesW49RrvzlZwzITVF5Tr8ufWaoV2/dbXRYAAAAABCSCfAgwTVOfbS7VhqJKq0s5ZvFR4fr7VWM1JjtRlbUNuvzppZr1z7Uqqqi1ujQAAAAACCgE+RDw0MINmvzsMj21eLPVpRyX2IhwvfirsTpnYIrcHq9eXLJdP3r4I83851rtqThgdXkAAAAAEBAI8iHg/CFpkqT31hSprNptcTXHJ8oRpmenjtYrv8nR2OwkuT1evbRku854eDGBHgAAAABEkA8JQ3smaEiPeLk9Xv1jxQ6ryzluhmHolL7Jmn/NyY2BvjeBHgAAAACaEeRDxBUnZ0mSXl5WKK/XtLia9uEL9FcT6AEAAACgGUE+RFw4LEOxEWHavq9Gn31XanU57ao50L9+zTi9+puTDwv0dy5Yq93lBHoAAAAAXQNBPkREOcJ0yciekqSXlxZaXE3HGde3my/Q5zQF+r8t3a4zHyHQAwAAAOgaCPIh5Bc5jZfXby2tlrvBa3E1HWtc326aT6AHAAAA0AUZpmmGxoTqduRyuRQfH6+KigrFxcVZXU6brNlZocE94mQYhtWldKol3+3THws2aumWMkmSw27TpDGZuvbMvspIiLS4OgA4fsHcNwUizicAINC0pW9iRD7EDOkZ3+VCvNQ4Qv/a1Y0j9Cf38R+hv2PBGkboAQAAAIQMgnyIOuD2aFcXDK8tBfq/Ly3UGY98RKAHAAAAEBII8iGoYF2xch74UDPeXGN1KZZpDvSvXX2yxvXppnqPSaAHAAAAEBII8iGof0qsXLUN+u/GvSrcV2N1OZY6uU83vXr1yQR6AAAAACGDIB+CsrpF6UcndJckvbI8dG9F1xY/FOhvf2tNl5yGAAAAACA4EeRD1OSmW9G98eUO1TV4LK4mcDQH+vlXn6xT+jYG+peXFepMAj0AAACAIEGQD1HnDExRapxT+6rdWri2yOpyAk5On2565TcEegAAAADBhyAfosLsNl02pnFU/uVlXF5/JAR6AAAAAMGGIB/CLh+bJbvN0BfbyrRzf9de9O5omgP969eM06n9/AP9bW+t4fwBAAAACBiGaZqm1UUEGpfLpfj4eFVUVCguLs7qco7LG1/u0JjsJGUnR1tdSlBZvrVMfyzYqM8275MkhdsN/Wx0pn53Zl/1TIyyuDoAXVEo9U2BgPMJAAg0bembCPItoHNHs5YC/dkDU3TR8B46e2CKIsLtFlcIoKugb2pfnE8AQKBpS98U1kk1IQA0eLwKszOboi3G9k7Sy78+2S/Qv/9Nsd7/plgxzjCNPylVFw3voVP7duPcAgAAAOgUBPkuYEdZje5/d51KKmv15u9OtbqcoNQc6Nftcentr3fr7VW7tav8gN5cuUtvrtylbtEOXTA0XRcNz9DIrEQZhmF1yQAAAABCFEG+C4hy2PWf9SVye7xas7NCQ3rGW11S0BqUHqdB6XH63/EDtLJwv/65arfeXbNH+6rdemnJdr20ZLt6JETqJ8MzdNHwDA1M43JNAAAAAO2LOfItCMV5cze+9pX+uWq3LhuTqQcvGWp1OSGl3uPVZ5tL9fbXu/X+2iJVuz2+105IjdFFw3voJ8MylJnEInkAjl0o9k1W4nwCAAINi90dp1Ds3L/YVqafzVuiyHC7lt1+juIiwq0uKSTV1ntUsK5Eb3+9Sx+t3yu3x+t7bURWgi4alqELhmaoe6zTwioBBKNQ7JusxPkEAAQaFrvDYUb3StQJqTHaWFylt1bu0tRTsq0uKSRFhNt1wdB0XTA0XRUH6vX+2iL98+tdWvLdPn1VWK6vCst1zzvf6tR+yfrJsAzlDU7jSxUAAAAAbcKIfAtC9Vv6Fz/fpllvf6MTUmP0/vQfsSBbJypx1eqd1Xv0z6936+sd5b7tjjCbzh6QoouGZ+gsbmcH4AeEat9kFc4nACDQcGn9cQrVzt1VW6+c+wt0oN6jN347TmOyk6wuqUvaVlqtf329W//8erc2l1T5tsc6wzT+pDRdNDxDp3A7OwDfE6p9k1U4nwCAQEOQP06h3Ln/qWCTYiPCdPHInoqP5JJuK5mmqXV7KvXPr3fpX6t2a3dFre+15BiHLhiSrp8M76GRWQlcPQEgpPsmK3A+AQCBhiB/nOjc0dm8XlMrCvfrn6t26d3Ve7S/pt73Ws/ESP1kWIYuGt5DA9JiLawSgJXom9oX5xMAEGja0jdZfu3u3LlzlZ2drYiICOXk5Gj58uVHbPvNN9/okksuUXZ2tgzD0Jw5c477mEAgsNkMjclO0n0Th2j57bl6ftoYXTyih6Icdu3cf0B/Xvyd8ub8VxPm/FdzP9qsHWU1VpcMAAAAwCKWBvn58+crPz9fs2bN0sqVKzVs2DDl5eWppKSkxfY1NTXq06ePHnzwQaWlpbXLMbui2nqPXv9ih377txXyerkgI9CE2206a0CK/m/ScK2441w9+YsROvfEVDnsNq0vqtQj72/Q6Q9/pEue+lwvfr5NpVV1VpcMAAAAoBNZeml9Tk6OxowZoyeffFKS5PV6lZmZqRtuuEG33nrrD+6bnZ2t6dOna/r06e12zGahfrldjbtBOQ8UqLK2QS/9aqx+dEJ3q0tCK1TU1GvhN3v0z1W7tWTLPjX/l2u3GTqlbzdNGJymk/t0U5/kaObUAyEo1Pumzsb5BAAEmqC4j7zb7daKFSs0Y8YM3zabzabc3FwtWbKkU49ZV1enurqDo5oul+uY3j9YRDnCdMnInnrh822a8eYaPXLpUJ3SL9nqsnAU8VHhmjQmS5PGZKm46XZ2b6/apa93VuiTTaX6ZFOpJKl7rFNjeyfp5N5JyunTTf1TYgj2AAAAQAixLMiXlpbK4/EoNTXVb3tqaqrWr1/fqcecPXu27r777mN6z2B1zRl9VLC+WDvKDugXzy7TL0/upVvPG6hop2V/JNAGqXERuuq03rrqtN7aWlqtd77erU83l+qrHeXaW1mnd1fv0bur90iSkqIdGpudpJw+Scrp3U0D02JlsxHsAQAAgGBFapM0Y8YM5efn+567XC5lZmZaWFHHS4+P1MIbf6QH/71ef1u6XX9bul2LN5ZozqQRGtUr0ery0Aa9k6N1wzn9dcM5/VVb79HXO8q1bGuZlm3dpxXb96us2q2F3xRp4TdFkqS4iDCN7d0Y6nP6JOnE9DjuWQ8AAAAEEcuCfHJysux2u4qLi/22FxcXH3Ehu446ptPplNPpPKb3DGbRzjDdO3GwJgxO0x/+sVq7y2sVxkhtUIsItyunTzfl9Okmqb/cDV6t2VWupVvKtGxrmVZsK5OrtkEfrivRh+saF4CMcYZpdHaiL9gP6RGvcII9AAAAELAsC/IOh0OjRo1SQUGBJk6cKKlxYbqCggJdf/31AXPMruDUfslaOP10ff7dPg3LTPBtL3HVKiUuwrrCcNwcYTaN6pWkUb2SdN1ZUoPHq7W7XVq2ZZ+WbS3TF9vKVFnboMUb9mrxhr2SpCiHXaN6JSqnaY790J7xcobZLf4kAAAAAJpZeml9fn6+pk6dqtGjR2vs2LGaM2eOqqurNW3aNEnSlClT1KNHD82ePVtS42J23377re/3Xbt2adWqVYqJiVG/fv1adUy0LDYiXHknHbxqYWNxpS7806eanNNL/5s3QJEOglwoCLPbNDwzQcMzE3TNGX3l8Zpat8fVeCn+ln1avq1M5TX1fovnOcNsGpGV4BuxH5mVqIhw/jwAAAAAVrE0yE+aNEl79+7VzJkzVVRUpOHDh2vhwoW+xeoKCwtlsx28xHf37t0aMWKE7/mjjz6qRx99VGeccYYWL17cqmOidQrWlaiuwavnPtuqjzaU6JFLh2p0dpLVZaGd2W2GBveI1+Ae8brqtN7yek1tLKnUsi2Nc+yXbSnTvmq3lm4p09ItZVKB5LDbNCwz3hfsR/VKVJSD5TYAAACAzmLpfeQDFfeWbfTRhhLN+H9rVOSqlWFIvz6tt34/fgCjsV2IaZr6bm+Vb479si37VFJZ59cmzGZoSM+DwX50r0TFRoRbVDEQuuib2hfnEwAQaNrSNxHkW0DnflDFgXrd+863+seKnZKkPt2j9cilw1jZvosyTVPb9tX45tgv27JPuytq/drYDGlwj/imW95109jsJMVHEeyB40Xf1L44nwCAQEOQP0507of7z/pi3fr/1qiksk63njdQvz2jr9UlIUDsKKvxhfplW8tUWFZzWJvMpEgNTIvToPQ4nZgeq4FpccpKiuJ+9kAb0De1L84nACDQEOSPE517yypq6vXC59t0/dn9ZG8KYHUNHlY0h589FQf85thvKa1usV20w64BabEamH4w4A9Ii1OMk/n2QEvom9oX5xMAEGgI8seJzr11aus9uujJz3T2oBRNz+1PoEeL9le7ta7IpfV7KrVuj0vrilzaWFwld4O3xfZZSVEa1DRq3xjw49QzMZLRe3R59E3ti/MJAAg0bembGPrCMXv/myJtKK7UhuJKFawr1qM/G6ahPROsLgsBJjHaoVP6JuuUvsm+bQ0er7aWVuvbPS6tL2oK+HtcKnbVqbCsRoVlNXr/m2Jf+xhnmAakxfoF/IFpsYpm9B4AAABdECPyLeBb+tZbuLZIdyxYo9Iqt+w2Q9ee0Vc3nNOP0Xkck7Jqt9bvcfkF/E3FVXJ7Wh6979UtSoPS4jQwPdZv9N4wGL1H6KFval+cTwBAoOHS+uNE5942ZdVuzXr7G/3r692SpIFpsXr0Z8M0uEe8xZUhFNQ3jd43jtofHL3//m3wmsU4wzQwrTHYNwf8gWmx3OseQY++qX1xPgEAgYYgf5zo3I/Ne2v26I4Fa1VW7dYZJ3TXi78aa3VJCGH7quoOuSy/8efmkpZH7w1D6pUUpUHpBy/LH5Qepx4JzL1H8KBval+cTwBAoGGOPCxx/pB05fRO0v3vrVP+uSdYXQ5CXLcYp07t59Sp/Q7Ova/3eLVlb7VvUb3mgL+3sk7b9tVo274a/Xttka+9M8ymnomRykyKUmZilDKTIpt+Nj6Pjwq34qMBAAAAP4ggj3bVLcapx38+3G/brH+uVVK0U787q6/C7TZrCkOXEG63aUBarAakxWqievi2l1bV+a2av25PpTaXVKquwavv9lbru70t3yIvLiLMP+Qf8nvPxChFhLMWBAAAADofQR4dau2uCr24ZLsk6YNvi/TYz4dpYBqXMKJzJcc4dVp/p07r7z96v6e8Vjv212hHWU3TzwO+56VVbrlqG/TNbpe+2e1q8bgpsc6mcH8w5PdMilRWUpTS4yNl57J9AAAAdADmyLeAeXPtxzRNvf31bs16+xuV19Qr3G7oxnP667dn9FUYo/MIYDXuBu3cf6Ax5JfVaEfT74VlNdq5/4Cq6hp+cP8wm6GMhEi/y/V7JjaG/MykKHWLdrC6PtqEvql9cT4BAIGGxe6OE517+yuprNXtb63Vom8b7w0+pEe8Hv3ZMA1Ii7W4MqDtTNNUeU39YaP4zWF/1/4DR7xlXrPIcPthIT8zKUpZSVHKSIhUXEQYQR9+ukLfNHfuXD3yyCMqKirSsGHD9Kc//Uljx7a8cOozzzyjl156SWvXrpUkjRo1Sg888MAR239fVzifAIDgQpA/TnTuHcM0TS1YtUt3vf2tKg7UKy0uQv/9w1lyhDEyj9Di9ZoqrqxtDPktXLZf5KrV0f7mDbMZSohyKCk6XIlRjsZHtP/zpGiHEqLClRTd+Fqsk/AfykK9b5o/f76mTJmiefPmKScnR3PmzNEbb7yhDRs2KCUl5bD2kydP1qmnnqpTTjlFEREReuihh/TWW2/pm2++UY8ePVp4B3+hfj4BAMGHIH+c6Nw7VrGrVre9uUYXj+yhHw/NsLocoNPVNXi0u7z2sJC/s+nS/f019cd03EPDf0KUQ0lN4T+xOexHOZTY9EVA45cADkb+g0io9005OTkaM2aMnnzySUmS1+tVZmambrjhBt16661H3d/j8SgxMVFPPvmkpkyZctT2oX4+AQDBh9vPIaClxkXo2amj/cLDwrV7tH1fjX59eh8WCEPIc4bZ1Ts5Wr2To1t8vbbeo/KaepVVu7W/pulR7VZZdb3vue+1pm01bo8avKZKq+pUWlXX6lqaw39iVHjjiH8LYd/vqoAoh2IjwmTjv1O0I7fbrRUrVmjGjBm+bTabTbm5uVqyZEmrjlFTU6P6+nolJSW1+HpdXZ3q6g7+t+FytbyIJQAAwYAgD0scGuIraup121trVVbt1sJvivTIpcPULyXGwuoAa0WE25UWb1dafESr92lt+D+0zbGGf7vNUEJkuBKiGgP+oV8EJESFK6mFbYlRDm4/iSMqLS2Vx+NRamqq3/bU1FStX7++Vce45ZZblJGRodzc3BZfnz17tu6+++7jrhUAgEBAkIfl4iLDdOuEgbr3nW/1VWG5zn/iE11/Vj/9dGQP9UyMsro8ICh0Zvj3eE3tq3ZrX7VbUnWr3y/GGeab0+8L+lGOQ74QODgFoHlblMPOpf84qgcffFCvvfaaFi9erIiIlv8bmDFjhvLz833PXS6XMjMzO6tEAADaFUEeljMMQz8fk6nT+ifrlv+3Wp9sKtXjizbq8UUbdVJGnO6bOFgjshKtLhMIOcca/isO1PuF/P01TT+r3dpfU6/ypi8BymvqVVbjVsWBepmmVFXXoKq6xtv6tZbDbjss6Lf0JUBcZLiiHHZFhNsV6bArMtyuKIddzjAbXwQEgeTkZNntdhUXF/ttLy4uVlpa2g/u++ijj+rBBx/Uhx9+qKFDhx6xndPplNPpbJd6AQCwGkEeASMjIVIv/Wqs3ly5S/O/3KEvt5Xpm90uJccc/IfXysL9qm/wanR2EnPpAQtEhDeG5dS41od/j9eU60DzKH9z0D8Y+Muq/cN/czt3g1duj1cllXUqqWz9pf/fF3lIuI8It/l+j3SEKTLc5ns9Irxpe3N7x8HnEYf8/v0vDCLC7fx9dJwcDodGjRqlgoICTZw4UVLjYncFBQW6/vrrj7jfww8/rPvvv1/vv/++Ro8e3UnVAgBgPYI8AophGLpkVE9dMqqn9lXVafnWMmUmHby8fu5/NqtgfYmSoh06Z2CKxp+UptP7Jysi3G5h1QB+iN1mNK6eH+1o9T6maepAvUf7fSP9h4T/6uarAA5uc9U2qLbeowP1HtW4PXI3eH3HOtC0vSM5wmy+kN8c7puDfu6gFF15au8Off9QkJ+fr6lTp2r06NEaO3as5syZo+rqak2bNk2SNGXKFPXo0UOzZ8+WJD300EOaOXOmXnnlFWVnZ6uoqEiSFBMTo5gY1lkBAIQ2gjwCVrcYp84bku63LSUuQvGR4SqrduuNFTv1xoqdigy36/T+yTpvSJouHtHTomoBtCfDMBTlCFOUI0w9EiLbvL/Ha/qC/QG3R7VNAb851Nce8vsBd9Oj+bX6g89rmvY9eBxv0/YG1dYf/LLA3eCVu8GrigOH3zqwb/eW704Af5MmTdLevXs1c+ZMFRUVafjw4Vq4cKFvAbzCwkLZbAcXTHzqqafkdrt16aWX+h1n1qxZuuuuuzqzdAAAOh33kW8B95YNbA0er77Ytl8ffFukD74p1q7yxvm2Ob2TNP+acb52JZW1Solt/eW/ANAWXq+pugav3xcCfl8YND3v1S2qXdb5oG9qX5xPAECg4T7yCGlhdpvG9e2mcX27aeaPT9S3e1z64Jti9Tlk1Ku0qk4nP1CggWlxGn9Sqs49MVUnpsex6BWAdmOzGb659AAAAJ2JII+gZhiGTsqI10kZ8X7bv95RLkn6do9L3+5xac6Hm9QjIVLjT0rV+BPTNCY7UWHc0xoAAABAECLJICSdMyhVX95xrh792TCNPzFVEeE27So/oOc/26bLn1mqN7/aZXWJAAAAAHBMGJFHyEqKdujSUT116aieOuD26JNNe/XBt8VavKFE5wxM8bX7+9Lt+njjXo0/MVXnDEpVUhtW1gYAAACAzkaQR5cQ6bBr/ElpGn9SmrxeU7ZD7vn8r693a9nWMi36tlg2QxqdnaTxJzZegp/VLeoHjgoAAAAAnY9L69HlHBriJemun5ykm3JP0EkZcfKa0vKtZbrv3XX60SMf6aK5n8nr5cYOAAAAAAIHI/Lo8galx2lQepxuzO2vnftrtOjbYn3wTbGWbytTcrTDL/jP/WizMhIi1D8lVv1SYhQRzmrVAAAAADoXQR44RM/EKE07tbemndpb+6vdqjhQ73tt5/4aPfL+Bt9zmyH16hat/ikxGpAWq1P7JevkPt2sKBsAAABAF0KQB44gMdqhxEMWvvN4Tf3y5F7aUFypjcWVKq+p19bSam0trdYH3xartt7jC/L7q926Y8FanZAaqxNSY3RCWqx6JUVxyzsAAAAAx40gD7RSr27RunfiYEmSaZraW1WnjUVV2lhcqU0llTqlb7Kv7YbiSr27Zo/eXbPHt81ht6lP92gNSIvVJSN76kcndO/0zwAAAAAg+BHkgWNgGIZSYiOUEhuh0/onH/Z6z8RI3X7+IG1sGr3fVFKlGrdH64sqtb6oUmOyk3xtV+8s121vrWkavW8awU+NVY+ESBmGcdixAQAAAHRtBHmgA/RMjNJvftTH99zrNbWr/EBTsK/ym0u/bo9La3c1Pg4V7bCrf2qsfj/+BJ3ev3H03uM1ZTNEwAcAAAC6MII80AlsNkOZSVHKTIrSOYNS/V47a2CK5l0xSpuKK7WhuFKbiqu0pbRK1W6PVu0olyH/e97P/OdaDUiLVf/UWA1IjVX/1BgNSI1VtxhnZ38sAAAAABYIiCA/d+5cPfLIIyoqKtKwYcP0pz/9SWPHjj1i+zfeeEN33nmntm3bpv79++uhhx7S+eef73v9yiuv1Isvvui3T15enhYuXNhhnwE4VimxEZowOE0TBqf5ttV7vNpWWq2NxVUa0jPet31jcaVctQ36Ytt+fbFtv99xukU79PSU0RrVK1GSVFbtlmmaBHwAAAAgxFge5OfPn6/8/HzNmzdPOTk5mjNnjvLy8rRhwwalpKQc1v7zzz/X5ZdfrtmzZ+vHP/6xXnnlFU2cOFErV67U4MGDfe0mTJig559/3vfc6STMIHiE223qn9o46n6o/zmnv348NEObSiq1oajxMv2NxZXasb9G+6rdSok9+Of8pSXbNOfDTeoW7VD/pnn3/VNjdUJK4++HrsgPAAAAIHgYpmmaVhaQk5OjMWPG6Mknn5Qkeb1eZWZm6oYbbtCtt956WPtJkyapurpa77zzjm/bySefrOHDh2vevHmSGkfky8vLtWDBglbVUFdXp7q6Ot9zl8ulzMxMVVRUKC4u7jg+HdA5atwN+q6kWidlxMlma7wU/84Fa/W3pduPuM9/fn+G+nSPkSR9s7tC1XUenZAao4QoAj4QiFwul+Lj4+mb2gnnEwAQaNrSN1k6Iu92u7VixQrNmDHDt81msyk3N1dLlixpcZ8lS5YoPz/fb1teXt5hoX3x4sVKSUlRYmKizj77bN13333q1q2bWjJ79mzdfffdx/dhAAtFOcL8LsGXpHsnDtaM8wdqc0mVNhZXaVPTCvobi6u0t6pOWUlRvrZ//WSr3vxqlySpe6xT/VOaR/Abf47ITFCY3dapnwkAAABAyywN8qWlpfJ4PEpN9V/8KzU1VevXr29xn6KiohbbFxUV+Z5PmDBBP/3pT9W7d2999913uu2223TeeedpyZIlstvthx1zxowZfl8ONI/IA8EuyhGmoT0TNLRngt/22nqPXzCPjwpXj4RI7So/oL2VddpbWafPv9snSbIZ0rf3TFBY0386b3+9W1W1DY0hPyVW8VHhnfVxAAAAACgA5sh3hMsuu8z3+5AhQzR06FD17dtXixcv1jnnnHNYe6fTyRx6dCkR4f5faM268CTNuvAkVdU1NI3gV2pTcaU2lVSprt7r1/6Fz7ZqZWG573lKrNNv9P6yMZncHg8AAADoQJYG+eTkZNntdhUXF/ttLy4uVlpaWov7pKWltam9JPXp00fJycnavHlzi0EeQKMYZ5iGZyZoeGbCEduc3r+7YiPCtam4UrsralVSWaeSyjp9urlUPRIidfnYLF/bB95bJ3eDVz0TI9UjIVIZTY9u0Q7fXH4AAAAAbWNpkHc4HBo1apQKCgo0ceJESY2L3RUUFOj6669vcZ9x48apoKBA06dP921btGiRxo0bd8T32blzp/bt26f09PT2LB/okm469wTf75W19dpcUqVNTavnRzn8R/r/34qd2lftPuwYjjCbxmQn6uVfn+zb9v43RYoMtzeF/QhFOULygiEAAADguFn+L+X8/HxNnTpVo0eP1tixYzVnzhxVV1dr2rRpkqQpU6aoR48emj17tiTpxhtv1BlnnKHHHntMF1xwgV577TV9+eWXevrppyVJVVVVuvvuu3XJJZcoLS1N3333nf7whz+oX79+ysvLs+xzAqEoNiJcI7ISNSIr8bDXvF5Tf5gwQFv2VmtX+QHtLj+g3eW1Kq6slbvBq+/fL+OOBWu1t/Lg3SMSo8J9I/hDe8TrhnP6+17bV1WnhCiH7IzqAwAAoAuyPMhPmjRJe/fu1cyZM1VUVKThw4dr4cKFvgXtCgsLZbMdXJTrlFNO0SuvvKI77rhDt912m/r3768FCxb47iFvt9u1evVqvfjiiyovL1dGRobGjx+ve++9l3nwQCey2QxNGpN12PZ6j1dFFbVye7y+bV6vqSE94rVr/wHtKj+gqroG7a+p1/6aen2z26Wq2ga/IJ835xOV17iVFh+hjITmy/Ybf+/XPUY5fVq+QwUAAAAQCiy/j3wg4t6ygLVctfVNI/gHtKu8VklRDl0wtHFqjLvBq5NmLVS9p+W/usb16aZXrz54yf7lTy9VpMPuC/o9EiKVHt8Y/JNjnIct/AcEKvqm9sX5BAAEmqC5jzwAtCQuIlxxaeEamHb4X2COMJvW3TNBe6vqfEF/t+/S/QM6Mf3gPnUNHi3Zsu+I73PWgO56ftpY3/MLnvhEYTZD0c4wRTvDFOMMU7TTrmhnmE5IidUlo3r62n6+uVThYTZFO/zbOcNsrNoPAACADkWQBxB0wuw2pcc3jqyP6nXkdoYMvTBtjPZU1DaF/oNz9fdUHFC08+BfgQ0er77Z7Trisc4c0N0vyP/6pS9V4/YcXpvN0Kn9kvXirw5+QXDDq1+pvsHb9OWAXVHNXxI47OqRGKVzT0z1td1aWq2IcJuSY5wKt9sOOz4AAABAkAcQshxhNp05IKXF10zT9Ls83zAMvfqbk1Vd16Bqd4Oq6zyqrmtQVV2Dqusa1Kd7jN++vZOjm15vbHegvjHUN3hNff+i//+sK1Z1C6FfkkZmJfgF+cueXqJiV50MQ+oW7VRavFOpsRFKiYvQgNQYXXlqb1/bigP1inWGcSs/AACALoYgD6BLMgxDjrCDAdhuMzSub+sWyTMMQ+/+z+l+2zxes+kLgAYZ8g/W9188RJVNXwgc+uVAdZ1HvZOj/do6w+wKsxlq8JoqrapTaVWd1qrxSoGRWQl+Qf78P36ikspapcRGKCWuMfCnxTf+3ic5RhMGp7XpnAAAACA4EOQBoB3YbUbj3P6I8MNemziiR6uP898/nCWv11RZjVtFFbUqqaxVsatOxa5aJcccvPOGaZraW1Wneo+pXU3TBg41qleiX5DPffxjNXi8SomLUFpchFLjnEqNaxzp75UUpWGZCW3/0AAAALAEQR4AAozNZig5xtkU3ONbbGMYhr65O097KxtDfuOj8fciV62yux0c6fd6TW3fV616j6lt+2oOO9boXon6x7Wn+J5f8ewyGYaUemjgj238PS0+Qunxke3+mQEAANB6BHkACFLhdpsyEiKVkfDDwdowpEU3ndEY9ivrVOKqVVFF4+/Frlq/lf69XlNLt+xTg7fl2/vl9E7S/GvG+Z5PfnapJCkp2qmkqHAlRjuU1PTokRCpEVmJ7fBJAQAAcCiCPACEOMMwlJ0crezvzcdviSnphWljVdQ0yl/SNMJf7Gr8AiAtPuJgW9PUsi1lrQ79Zz26WB6vqcRoh7pFO5QY5VBSdLiSop3qnRztNxXAVVuvGAcL+QEAALSEIA8A8LHbDJ3WP7lVbU1TenbqaO2vcausul5l1XUqq67X/mq3ymrcOikj/pC2pgrLauTxNv78vpP7JPkF+bMfXayyarcSoxyNo/xRjaP8idGOw1bv31BUKUeYTeF2Qw67TeF2m8KbnofbbHwZAAAAQg5BHgBwTGw244i392vJopt+pP01bu2rcvvCf+NPt/p0P3i1gGma2l9TL68p7at2a1+12+84J/dJ8gvyk59dqtIq/zbNhmcmaMF1p/qen/fHT1RWXadwu+2Q0G8o3G5Tn+QYPfbzYb62d//rG5VVuxvb2I2mn42P5BiHfn16H1/bd1bvVmVtw2FteydHqV9KbKvPEQAAQGsQ5AEAHc4wDPXpHtPqtuvumaDymsYQ3zzCv7+6Mfynf+/y/ohwu2KcYar3eOX2eGUecqW/w27zO/beytojhv56j9fvecG6khavHpCkPsnRfkH+yf9s1vqiysPaXf2jPrrt/EFH/cwAAABtQZAHAAQcR5hNKU23x/shhmHo01vO9tvm8Zqq93hV7/Hq+7P3X7v6ZNXWe5teN9XQFP7rPaaiHXa/tv9zTn+V17jV4DVV3+Bt+qKg8diJUf63GRzXt5syk6J871vfYMrt8arHURYiBAAAOBYEeQBASLHbDNltdkWE2w97rS2XuV86qmer28668KRWtwUAADhetqM3AQAAAAAAgYIgDwAAAABAECHIAwAAAAAQRAjyAAAAAAAEEYI8AAAAAABBhCAPAAAAAEAQIcgDAAAAABBECPIAAAAAAAQRgjwAAAAAAEGEIA8AAAAAQBAhyAMAAAAAEEQI8gAAAAAABBGCPAAAAAAAQYQgDwAAAABAECHIAwAAAAAQRAjyAAAAAAAEEYI8AAAAAABBhCAPAAAAAEAQIcgDAAAAABBECPIAAAAAAAQRgjwAAAAAAEGEIA8AAAAAQBAJiCA/d+5cZWdnKyIiQjk5OVq+fPkPtn/jjTc0cOBARUREaMiQIXrvvff8XjdNUzNnzlR6eroiIyOVm5urTZs2deRHAAAAx6m9/z0AAECosjzIz58/X/n5+Zo1a5ZWrlypYcOGKS8vTyUlJS22//zzz3X55Zfrqquu0ldffaWJEydq4sSJWrt2ra/Nww8/rCeeeELz5s3TsmXLFB0drby8PNXW1nbWxwIAAG3QEf8eAAAgVBmmaZpWFpCTk6MxY8boySeflCR5vV5lZmbqhhtu0K233npY+0mTJqm6ulrvvPOOb9vJJ5+s4cOHa968eTJNUxkZGfr973+vm2++WZJUUVGh1NRUvfDCC7rsssuOWpPL5VJ8fLwqKioUFxfXTp8UAIBjF+p9U3v/e+BoQv18AgCCT1v6prBOqqlFbrdbK1as0IwZM3zbbDabcnNztWTJkhb3WbJkifLz8/225eXlacGCBZKkrVu3qqioSLm5ub7X4+PjlZOToyVLlrQY5Ovq6lRXV+d7XlFRIanxRAIAEAia+ySLv3/vEB3x74Hvo68HAAS6tvT1lgb50tJSeTwepaam+m1PTU3V+vXrW9ynqKioxfZFRUW+15u3HanN982ePVt33333YdszMzNb90EAAOgklZWVio+Pt7qMdtUR/x74Pvp6AECwaE1fb2mQDxQzZszw+1bf6/WqrKxM3bp1k2EYx3Vsl8ulzMxM7dixg0v3Wolz1nacs7bjnLUd56zt2vOcmaapyspKZWRktFN1XUtH9vUS/320Feer7Thnbcc5azvOWdtZ1ddbGuSTk5Nlt9tVXFzst724uFhpaWkt7pOWlvaD7Zt/FhcXKz093a/N8OHDWzym0+mU0+n025aQkNCWj3JUcXFx/MfQRpyztuOctR3nrO04Z23XXucs1Ebim3XEvwe+rzP6eon/PtqK89V2nLO245y1Hees7Tq7r7d01XqHw6FRo0apoKDAt83r9aqgoEDjxo1rcZ9x48b5tZekRYsW+dr37t1baWlpfm1cLpeWLVt2xGMCAADrdMS/BwAACGWWX1qfn5+vqVOnavTo0Ro7dqzmzJmj6upqTZs2TZI0ZcoU9ejRQ7Nnz5Yk3XjjjTrjjDP02GOP6YILLtBrr72mL7/8Uk8//bQkyTAMTZ8+Xffdd5/69++v3r17684771RGRoYmTpxo1ccEAAA/oL3/PQAAQCizPMhPmjRJe/fu1cyZM1VUVKThw4dr4cKFvgVsCgsLZbMdvHDglFNO0SuvvKI77rhDt912m/r3768FCxZo8ODBvjZ/+MMfVF1drauvvlrl5eU67bTTtHDhQkVERHT653M6nZo1a9Zhl/PhyDhnbcc5azvOWdtxztqOc9Z6HfHvgc7E/9dtw/lqO85Z23HO2o5z1nZWnTPL7yMPAAAAAABaz9I58gAAAAAAoG0I8gAAAAAABBGCPAAAAAAAQYQgDwAAAABAECHId7C5c+cqOztbERERysnJ0fLly60uKWDNnj1bY8aMUWxsrFJSUjRx4kRt2LDB6rKCxoMPPui7/SKObNeuXbriiivUrVs3RUZGasiQIfryyy+tLitgeTwe3Xnnnerdu7ciIyPVt29f3XvvvWKd1IP++9//6sILL1RGRoYMw9CCBQv8XjdNUzNnzlR6eroiIyOVm5urTZs2WVMsOgR9fevR1x8/+vvWob9vG/r7owu0/p4g34Hmz5+v/Px8zZo1SytXrtSwYcOUl5enkpISq0sLSB9//LGuu+46LV26VIsWLVJ9fb3Gjx+v6upqq0sLeF988YX+8pe/aOjQoVaXEtD279+vU089VeHh4fr3v/+tb7/9Vo899pgSExOtLi1gPfTQQ3rqqaf05JNPat26dXrooYf08MMP609/+pPVpQWM6upqDRs2THPnzm3x9YcfflhPPPGE5s2bp2XLlik6Olp5eXmqra3t5ErREejr24a+/vjQ37cO/X3b0d8fXcD19yY6zNixY83rrrvO99zj8ZgZGRnm7NmzLawqeJSUlJiSzI8//tjqUgJaZWWl2b9/f3PRokXmGWecYd54441WlxSwbrnlFvO0006zuoygcsEFF5i/+tWv/Lb99Kc/NSdPnmxRRYFNkvnWW2/5nnu9XjMtLc185JFHfNvKy8tNp9NpvvrqqxZUiPZGX3986Otbj/6+9ejv247+vm0Cob9nRL6DuN1urVixQrm5ub5tNptNubm5WrJkiYWVBY+KigpJUlJSksWVBLbrrrtOF1xwgd+fNbTs7bff1ujRo/Wzn/1MKSkpGjFihJ555hmrywpop5xyigoKCrRx40ZJ0tdff61PP/1U5513nsWVBYetW7eqqKjI77/P+Ph45eTk0BeEAPr640df33r0961Hf9929PfHx4r+PqxDjgqVlpbK4/EoNTXVb3tqaqrWr19vUVXBw+v1avr06Tr11FM1ePBgq8sJWK+99ppWrlypL774wupSgsKWLVv01FNPKT8/X7fddpu++OIL/c///I8cDoemTp1qdXkB6dZbb5XL5dLAgQNlt9vl8Xh0//33a/LkyVaXFhSKiookqcW+oPk1BC/6+uNDX9969PdtQ3/fdvT3x8eK/p4gj4B03XXXae3atfr000+tLiVg7dixQzfeeKMWLVqkiIgIq8sJCl6vV6NHj9YDDzwgSRoxYoTWrl2refPm0bEfweuvv66XX35Zr7zyik466SStWrVK06dPV0ZGBucMwHGhr28d+vu2o79vO/r74MOl9R0kOTlZdrtdxcXFftuLi4uVlpZmUVXB4frrr9c777yjjz76SD179rS6nIC1YsUKlZSUaOTIkQoLC1NYWJg+/vhjPfHEEwoLC5PH47G6xICTnp6uE0880W/boEGDVFhYaFFFge9///d/deutt+qyyy7TkCFD9Mtf/lI33XSTZs+ebXVpQaH573v6gtBEX3/s6Otbj/6+7ejv247+/vhY0d8T5DuIw+HQqFGjVFBQ4Nvm9XpVUFCgcePGWVhZ4DJNU9dff73eeust/ec//1Hv3r2tLimgnXPOOVqzZo1WrVrle4wePVqTJ0/WqlWrZLfbrS4x4Jx66qmH3eZo48aN6tWrl0UVBb6amhrZbP5dhd1ul9frtaii4NK7d2+lpaX59QUul0vLli2jLwgB9PVtR1/fdvT3bUd/33b098fHiv6eS+s7UH5+vqZOnarRo0dr7NixmjNnjqqrqzVt2jSrSwtI1113nV555RX985//VGxsrG8+SXx8vCIjIy2uLvDExsYeNqcwOjpa3bp1Y67hEdx000065ZRT9MADD+jnP/+5li9frqefflpPP/201aUFrAsvvFD333+/srKydNJJJ+mrr77S448/rl/96ldWlxYwqqqqtHnzZt/zrVu3atWqVUpKSlJWVpamT5+u++67T/3791fv3r115513KiMjQxMnTrSuaLQb+vq2oa9vO/r7tqO/bzv6+6MLuP6+Q9bCh8+f/vQnMysry3Q4HObYsWPNpUuXWl1SwJLU4uP555+3urSgwe1oju5f//qXOXjwYNPpdJoDBw40n376aatLCmgul8u88cYbzaysLDMiIsLs06ePefvtt5t1dXVWlxYwPvrooxb/7po6dappmo23pLnzzjvN1NRU0+l0muecc465YcMGa4tGu6Kvbz36+vZBf3909PdtQ39/dIHW3xumaZod8xUBAAAAAABob8yRBwAAAAAgiBDkAQAAAAAIIgR5AAAAAACCCEEeAAAAAIAgQpAHAAAAACCIEOQBAAAAAAgiBHkAAAAAAIIIQR4AAAAAgCBCkAcQkAzD0IIFC6wuAwAAdBD6euDYEeQBHObKK6+UYRiHPSZMmGB1aQAAoB3Q1wPBLczqAgAEpgkTJuj555/32+Z0Oi2qBgAAtDf6eiB4MSIPoEVOp1NpaWl+j8TEREmNl8I99dRTOu+88xQZGak+ffroH//4h9/+a9as0dlnn63IyEh169ZNV199taqqqvzaPPfcczrppJPkdDqVnp6u66+/3u/10tJSXXzxxYqKilL//v319ttvd+yHBgCgC6GvB4IXQR7AMbnzzjt1ySWX6Ouvv9bkyZN12WWXad26dZKk6upq5eXlKTExUV988YXeeOMNffjhh36d91NPPaXrrrtOV199tdasWaO3335b/fr183uPu+++Wz//+c+1evVqnX/++Zo8ebLKyso69XMCANBV0dcDAcwEgO+ZOnWqabfbzejoaL/H/fffb5qmaUoyf/vb3/rtk5OTY1577bWmaZrm008/bSYmJppVVVW+1999913TZrOZRUVFpmmaZkZGhnn77bcfsQZJ5h133OF7XlVVZUoy//3vf7fb5wQAoKuirweCG3PkAbTorLPO0lNPPeW3LSkpyff7uHHj/F4bN26cVq1aJUlat26dhg0bpujoaN/rp556qrxerzZs2CDDMLR7926dc845P1jD0KFDfb9HR0crLi5OJSUlx/qRAADAIejrgeBFkAfQoujo6MMuf2svkZGRrWoXHh7u99wwDHm93o4oCQCALoe+HghezJEHcEyWLl162PNBgwZJkgYNGqSvv/5a1dXVvtc/++wz2Ww2DRgwQLGxscrOzlZBQUGn1gwAAFqPvh4IXIzIA2hRXV2dioqK/LaFhYUpOTlZkvTGG29o9OjROu200/Tyyy9r+fLl+utf/ypJmjx5smbNmqWpU6fqrrvu0t69e3XDDTfol7/8pVJTUyVJd911l377298qJSVF5513niorK/XZZ5/phhtu6NwPCgBAF0VfDwQvgjyAFi1cuFDp6el+2wYMGKD169dLalxl9rXXXtPvfvc7paen69VXX9WJJ54oSYqKitL777+vG2+8UWPGjFFUVJQuueQSPf74475jTZ06VbW1tfq///s/3XzzzUpOTtall17aeR8QAIAujr4eCF6GaZqm1UUACC6GYeitt97SxIkTrS4FAAB0APp6ILAxRx4AAAAAgCBCkAcAAAAAIIhwaT0AAAAAAEGEEXkAAAAAAIIIQR4AAAAAgCBCkAcAAAAAIIgQ5AEAAAAACCIEeQAAAAAAgghBHgAAAACAIEKQBwAAAAAgiBDkAQAAAAAIIgR5AAAAAACCCEEeAAAAAIAgQpAHAAAAACCIEOQBAAAAAAgiBHkAAAAAAIIIQR4AAAAAgCBCkAcAAAAAIIgQ5AEAAAAACCIEeQAAAAAAgghBHgAAAACAIEKQBwAAAAAgiBDkAQAAAAAIIgR5AAAAAACCSMAH+f/+97+68MILlZGRIcMwtGDBgqPus3jxYo0cOVJOp1P9+vXTCy+80OF1AgCAY0NfDwBA2wR8kK+urtawYcM0d+7cVrXfunWrLrjgAp111llatWqVpk+frl//+td6//33O7hSAABwLOjrAQBoG8M0TdPqIlrLMAy99dZbmjhx4hHb3HLLLXr33Xe1du1a37bLLrtM5eXlWrhwYSdUCQAAjhV9PQAARxdmdQHtbcmSJcrNzfXblpeXp+nTpx9xn7q6OtXV1fmee71elZWVqVu3bjIMo6NKBQCg1UzTVGVlpTIyMmSzBfwFdR2Kvh4AEIra0teHXJAvKipSamqq37bU1FS5XC4dOHBAkZGRh+0ze/Zs3X333Z1VIgAAx2zHjh3q2bOn1WVYir4eABDKWtPXh1yQPxYzZsxQfn6+73lFRYWysrK0Y8cOxcXFWVgZAACNXC6XMjMzFRsba3UpQYm+HgAQ6NrS14dckE9LS1NxcbHftuLiYsXFxbX4Db0kOZ1OOZ3Ow7bHxcXRuQMAAgqXgdPXAwBCW2v6+pCbZDdu3DgVFBT4bVu0aJHGjRtnUUUAAKA90dcDALq6gA/yVVVVWrVqlVatWiWp8ZYzq1atUmFhoaTGS+WmTJnia//b3/5WW7Zs0R/+8AetX79ef/7zn/X666/rpptusqJ8AABwFPT1AAC0TcAH+S+//FIjRozQiBEjJEn5+fkaMWKEZs6cKUnas2ePr6OXpN69e+vdd9/VokWLNGzYMD322GN69tlnlZeXZ0n9AADgh9HXAwDQNkF1H/nO4nK5FB8fr4qKCubNAcAReL2mPKYpr2nK65U8pimP15TZ9NNjmjJNNf7ubfr90DbmIdt97U15vGpdG9OU19v0/t/ryb7ftbXY0X1/nxZafb+H/H6LlnrQ7x/nhNRYjclOaqmCNqFval+cTwBAoGlL3xRyi90BwJF4vabc/7+9O4+Pqr73P/6efckeQjYIhE0W2ZStgEWsKIpFcaWIsra9WrQgtVdQAZcC4lZqpVJtxWt/WhG30qq4oFIXEAuKWllEEVBJAgLZk0lmzu+PSSYZspBAJjMTXs/HYx6Zc873nPOZw/LNe8453+P1qcLrU4XXUKXXp0qfoUqvoUqfT16foQqvPzhWBKb9P6vbeX2+mjZBy6q25Wtkmbd6uS/wvjLofc0yw/AHQp/hD6WG5A+rVaHVkD88+2f52/uq2lVP+9eptSxoWzVtjl3f56vejn+Z75havL66wRkNu/ZHnVskyAMAAFQjyAM4Kb6q0Oup9AfcCq//fSAwV9YOz76q5UbQdPV61e/926o17fWponqez6h57z1m21XbrZ5X6Q0O7l7SZ6uzmE2ymEwymep5bzbJZPLPs5hNMpslc9W02WyS2VQ1HdRWddYzVbU7doDXY8d7rW8E2Lpt6vsUpkbb1LdK7Ta9MzjbCwAAWhZBHogChmGovNKn8kp/SC6v9Fb99AV+Hhugq8Owp3YwrvpZXjv0Btr5agVoo95tVXiNwL6q51dGcTg2mSSb2SyL2SSrxSSr2SSL2SybxR8QbZaqZYHl5qD3/jamqvWrlgW1Cd5eoE3VMqvZHLTd6josZpM/mMofPgNBVZLZLJlkkmrPq2pjMvmX1Q62ZpMk+UOxqYH21dusr7251jart1tdn7kqVJurArrZ5N9WzXsekwYAABAKBHngOAzDUFmFTyWeSpVWeP2BusIfcssrvFU/q6brCdjlDQRvT6X3mOmG2vm3HS2sVQHYZjHJbrVU/TRXzfPPr/3TXj3fWrWOxR9ug5ZZzLJZTYHpustNslnNQcvtx+yvpgZTYJsWgiYAAIhy1be9Vd/eF3TLoM+Qt2q65jbBmlsIK2st8wbWC75dr3osnNq36/kCt+3VvA/cDtiENsHLFdSmZv1a7X111/fW3r4veJ912vpqT9dq66u/bd3PoKpt1PMZfIY6tXPr5V//uFX/3AnyaBNqh+0Sj1elFV6VeLz+8O3xqtjjVWnVshKPV6XVPysqVVxe8772spLqdSq89Q5oFU4Oqz+YOqyWWu/NtQK0ORB0a4doe60wXB187bUCtK2qrSNonZrltbdV3/4IxwAAIJoYhhG4xa/y2Fv2vA3fCuip9Ife2rcRVh5z21+lt6Fb/vy3CQbGxjlewG5oPJ9a4/EgvIrLK1t9nwR5RIyCsgp9fbBYu/OKlFtQFhS2SzxeFZeHP2w7qgKzw2aR3WKWw2au+mmRI2i66qfVEgjZ1cE7eDo4kDelnc1iqvdeXwAAgGhW4fWptMKrsqqTMqUV/t/5yip8Kqs1XVrhVVnVyz/PF5hX6vGqrLJ6vaqrH48N1lXj+lRWDWDbVlWPMWMN3CZoDry3WEyB2wuPvZ2w9jg15lq3+JkDt+5V34J3/DY1t/PV3mb1dNW8qvFuTGq8TfVnqr0PS2D9WmPr1K7HXFNj7e0Gb6fm1sHan6l6v+ZjPkvtWwyra7RbW/+p7gR5tCrDMJRbUK7deUX66mBR0M+8wvIW2YfDapbbbpHbbpXLbpHbbpHLZlGMo2raVjXPbq1q52/rn2dpYJ5VLpuFs80AAOCU5PUZ/iseyytV7PGquLwycPVj9UmW2mHa/95XZ15phVflFbWDek1Ij5RBae21btuzWcyymWu9r+c2wWNvGbTWmm+3mgO3HVZf5WitGj8neOycmvFygsfoqTteT9AYPYHxdoLXrR44Fm0XQR4hUeH1ae8PxdqdV6yvDhbpq7wi7a76WezxNrhe+ziHurePVWaiS7GO4LBdE8qtinHUvA9ebiVsAwCAU9axtxsWe6pvI6wJ3tXT/qsda0L5seG82FOpknL/z7KK1huvx2ySXDb/73ZOm/+ETOCn3SKXzRxY7rD6f7qCllvkrGpTfTVjY2PpWAPj7tQ8JQWIdAR5nJTal8PXPsO+74eSBu/XsZhN6pzsVtf2seqeGqtu7WPUPTVWXdvHKsFla+VPAAAAEBkMw1BBWaWOFHt0pMSjoyUVOlLi0ZGSCh0t8aigtELFxwvjnsqQ3m5oNkkxdqvcjpqrF6uvdKwdsJ21wnftgF07ZNeeV7OOP1QTpoHGEeRxXMdeDl87sOcWNHw5vNtuUbdjwnq39rHq3C4mLPeRAAAAtJYKr68mjBfXhPEj1eE8aJ6/3dHSiha9vNx/a2FN4I5x1ATv6jAeU3UrYmPTgXUcVjmshGwgEhDkEVDf5fD+4F6sokZGYkyNc6hb+1h1S41R9/ax6pbqD+/p8U7+owcAAFHNMAyVeLyBsH34mLPlx541P1zsn9fY707H47ZblOS2K9FtU3KMXYluu5LcNiW4bLXCuFUxdovcDv/POmHcZuEeaaANI8ifogzD0Jvb87R135HA/etNuRy+W9VZdS6HBwAA0aT63vGjpVVnv0sqlF9acya8evpIcUWdoO7xntj94SaTlOCyKbkqlPvDuT+UJ8VUBfXqeTG2QHh3WC0t/OkBtDUE+VPUn//9te55dUed+TF2SyCsV18Sz+XwAAAgUhiGoWKPV0erQnZ+VQivDuj+6ZqAnl8dyEsr5Kk88QHb7FazP4DXE8prnzUPBHW3XfEuG4PwAggJgvwp6JtDxfr9G7skSZcMzNQZWYlcDg8AAFpV9cBu+bVCuD941z1Lfuz0yTz722o2KbHqMvVEt12JLpsS3DYluqouXz8mjFeHdrfdwu9IEaaswqsD+WU6kF+qA0fLlFNQpu+Plmpol2RdMrCDJOmHonL9aOn6Brdx6RkddO8VAyRJpR6v+t/5WoNtL+iboT9OOiMw3eO2Vxpse/Zp7fWXqUMC0/3ueE1lFTVPbjKZTGof61B6glODOifp1nG9A8t25BQoyW1XSqyDL4LQIIL8KcYwDN320mcqr/TprO4pWj5xIJ0SAABoMYZhKL+0QjkFZcotKFduQZly88uUW1hruqBMh4o8JzWwm91iVqLb5n+57FVhvGraba8K6v4QnlBrfgyBPCocG9I7Jrk0rGs7SdL3R0t10UPv6khJRb3reip9gSAvqdEvfo69a6LxtsGNG2t77O2qlV7jmPaGvjtaqu+OlsptD76V4mePbtLRkgpZzSalxTuVkeBUeoJTmYku9cmI14Qzaj6bYRj8fT5FEeRPMc9t+Vbv7/5BTptZiy/tyz98AADQZCWeyqAwnlsV1nMKypRXK7iXN+MSdqfNrESXPSiUJ7przpInVgX0oGm3TS4bgTxalXq8yikok9VsUlayW5J0uNij367Zpu/zy5STX1onpF8xqGMgyCfH2APLXTaLMhKdykxw+cNuglNndEoKrJfotmvT/HMbrMVlqwnRDqu50baOY24zbaztsbekvvPb0UGPBazw+nSwqFwHjpYpzlkTyTyVPrlsFhWYKlTpqwn71Uad1j4oyA9Z/KYcVktQ2E+Pdyoz0anslBj1So9vsEZEN4L8KeRgYbl+9/J2SdKcMaepc7uYMFcEAAAiQYXXp4OFwYE8pyqo59V6X1jW9JHYE902pcc7lRrvVFqc/xLi1Hin0uOdSot3qH2cQ0luu5w2BnZrS3w+IzBafnF5pVa9v0cH8suUk19WJ6RfOaij7rvSf1m7227R+h15QduqHdJPS4sNzHfaLHr9plFKi3Mq3mVt9Asdi9mk9ARnk2o3N6OtpGa1TYuv2zYr2S11Cp5nt5q1cf65qqwO+vllOnC06sqE/DJ1San5/b3EU6lDRR5JCgr71Uad1l5PzhgamJ76+GbFOa3KSHAqI8Hl/5no/8ll/NGHIH8KuetfXyi/tEKnZ8br52d1CXc5AACgBR0sLNe+wyU6kF/qD01Vv/znVw3ydu3wzirxeJVbUKb3dx/S1weLVV7pU3mlt1n3nJtNksNqkcNqlt1q1jm9UnVaWpzS4h3anVekTV//ILvFHPTos4NF5TpYVK75/TPVMz1OkvT6f3P09OZ9De7nN+f1VL+OCZKkDbsOatX7expse8M53TU4O1mStPGrH/Tnf3/VYNtf/LirRnZPkSRt3XdED63/ssG2U4dn65xeqZKk/36fr/te29lg258NydIFfTMkSbvzCgMnT+pz6RkdApd+7z9cogX/+LzBthf1y9CVg7MkSXkFZfrf5z9tsO2Y3mm65kedJUlHSzyas/qTBtv+uEd7zaz6fbDU49X1T21psO2wLu10/ehukiSvz9DM//soaLnXZ+hQkUcH8ks1pnea7q8K5xazSfe/vqvebR57ObnTZtF9V/RXSpwjEDTjnQ2H9NPS4hqsty2wWsxVYdtVJ+xXc1ot2jj/J3XCfk7VLQm9M2qOUYmnUht2HWxwf6N7ttcT0/2hv8Lr0y+e/E+DbQd1StKN5/YITM944iP5jPr/D+mbmaCbx/YMTF/3ty0qq/TW27ZHaqxuu6hPYHr2Mx8rv7T+2yc6J7t15yV9A9P/+9w25RWW19s2Pd6pey7vH5i+/aXP9O2Rul98SFKS267fTxwYmL77X1/oq4NF9baNsVu1YvKZ9S5rDQT5U8TbO/L0z23fy2yS7rmsv6wWRqAHACAaVHp9yissD9wvXB3ScwpK9cCVA+WyW3S0xKN5z39a54xmbf/Ze6TJ+zyre4q6p8YqNd6hrXuP6M3tNdv1GVJphVelVQN3XfujzurbwR+4V7y9W5u+Ptzgdn81unvg/XdHS/XOzoaDxfSRNScdcvIbb/uzIVmB93mFZY22vXhAZuD9D0WeRtue1yct8P5oSUWjbc+q+nJAkvJLKxtte2atS7+Lyhtv2zuj5tLo0gpvo22za11t6an0Ndo2Nc4ReF/pa7xtnDP4UcONtT2QXxOQnDaLpg7vrASXTekJLmUkOhsN6dVfWKBpzGbTccN+oK3JpD9NPlPfHy2tCvo1wT+3oEzpta4Y8BlGo3/Gx95isGHXwQbHuzh2/nu7D6movP4re4qOueLng69+0MEGwvnpmcG3DHy457D2/lBSb9uuKcFXIf/nmyPakVNYb9v0Y66c2LrviD7ed7TetuF+BDdB/hRQXF6p21/yf9M786wugW+3AQBAeNWE9Jozadf8qHPgcvO7//WFVr2/Rw2NCbf3hw90IL9Mh4s9je4n1mFRx0S3MpNcSot3yOeTvIaheKdV8S7/CO4xdquqc9XYvumKrwpvn3+Xr7Gnpze47Q6JrsD7n/RKDQqIx6p9WfBZ3VN03xX9G2zbs9YZ1yHZyY22rf4iQZLOyEpqtO2gzjUhuk9mfKNtz6zVtntqbKNtB2QlBt53budutO3pmTX1ZiQ4G21b+x7n5Bh7o227p9Zcfh7ntDXatvafhcNqabRt9X3skmSS6rQ1mUxqF2sPhPTaap81Rfg4bRaN65dR77JKry9oXAur2dzo34fa/+Yladnl/WU0cEb+2FsKfjehryqOHWGwSkps8P8dC3/aJ2ik/9oS3fag6XkX9GrwC4LaYxBI0tzzTmvwTL/rmCtFbvxJd/1QVP//r+F+NLfJaOion8IKCgqUkJCg/Px8xcdH/wARd/7zv1r1/jfqmOTS6zeNktvO9zcAEG3aWt8Ubq1xPGuH9H4dEgO/9P1t0149v+Vb5eSXKa+wrE5If/rnw1TpM7TnULFe2Pqttn2b36T9pcc71SUlRl3ax6hrSoy6to9Rl5RYdUh0hf0XTgDA8TWnbyLRtXEf7zuiJz74RpK05NJ+hHgAAELg37sO6p2dB5VTUOq/7P2YkP7Wb85W1/b+s6WHCsv1yf6jgXWr7zmXpPJKr67+y4cN7ifOYa0K6DHq2j7WH9yrXjEO+ngAOFXwP34bVuH1af4Ln8kw/IOqjDqtfbhLAgCgTdq674ger2cwNqvZpASXTave/0YlHq++PlSk3XnBAydV33NezWYxqXO7qrBe68x6l5QYpcTaeeQaAIAg35Y9+u+vtSOnUElum26/qHe4ywEAoE3KLSiT02bRiG7tVOH1qai8UocKPTpYVK5Kn6Efij3626a9ddbLTHCqS/vqM+qx6lp1SXyHRBeD0gIAGkWQb6O+PlikP1Q9TmXh+D5qF9vwwDMAAODEPfzW7nqDuiTFO63q2j5WXVOCL4fPTnFzuxsA4ITRg7RBhmHo1hc/k6fSp1GntdeEqueUAgCAltczPU49UmMDl8DXXA4fo+QYLoUHALQ8gnwb9Ox/9mvT14flslm0eEJffoEAACCErvlRZ13zo87hLgMAcArhBqw2Jq+wTItf3i7J/4zE2s/9BAAAAABEP4J8G3Pn2i9UUFapfh0SNH1kdrjLAQAAAAC0MIJ8G/LmF7l6+bMDsphNWnpZP0a8BQAAAIA2iKTXRhSWVWjBPz6XJP38x13Ut0NCmCsCAAAAAIQCQb6NuP+1nTqQX6ZOyW7NOfe0cJcDAAAAAAgRgnwbsGXvET1Z9fzaJZf2k8tuCXNFAAAAAIBQIchHOU+lT/Nf+FSGIV1+Zked1SMl3CUBAAAAAEKIIB/lVm74Srtyi9Quxq7bL+od7nIAAAAAACFGkI9iu/OK9PBbuyVJC8f3UVKMPcwVAQAAAABCjSAfpXw+Q7e+8Jk8Xp/OPq29Lh6QGe6SAAAAAACtgCAfpZ75aL82f3NYbrtFiy/tK5PJFO6SAAAAAACtgCAfhfIKyrT01e2SpN+c31Mdk9xhrggAAAAA0FoI8lFo0dr/qrCsUgM6JmjaiOxwlwMAAAAAaEUE+Sjz2n9z9OrnObKYTVp6WX9ZzFxSDwAAAACnEoJ8FCkoq9DCf3wuSfrlqK7qkxkf5ooAAAAAAK2NIB9F7l23Q7kF5cpu59bsc3uEuxwAAAAAQBgQ5KPEf745rP+3aZ8kacll/eS0WcJcEQAAAAAgHAjyUaC80qt5L3wmSbpqcEeN6JYS5ooAAAAAAOFCkI8Cj7zzlXbnFSkl1q5bx/UOdzkAAAAAgDAiyEe4L3MLteLt3ZKkReNPV6LbHuaKAAAAAADhRJCPYD6foXkvfKYKr6Gf9ErVT/tnhLskAAAAAECYEeQj2FOb92nL3iOKsVt094S+Mpl4ZjwAAAAAnOoI8hEqJ79My17dIUm6eWxPdUh0hbkiAAAAAEAkIMhHqIX/+FxF5ZUamJWoKcOzw10OAAAAACBCEOQj0LrPD+j1L3JlNZt0z+X9ZDFzST0AAAAAwI8gH2HySyu08B//lSRdd3Y39UqPD3NFAAAAAIBIQpCPMPe8ukN5heXqmhKjG37SPdzlAAAAAAAiDEE+gnz49Q/6++Z9kqQll/WT02YJc0UAAAAAgEhDkI8QZRVezX/xM0nSz4Zk6Udd24W5IgAAAABAJCLIR4g/vb1bXx8sVvs4h+Zf2Dvc5QAAAAAAIhRBPgLsyi3UIxu+kiTdefHpSnDbwlwRAAAAACBSEeTDzOszdMvzn6rCa2hM7zRd2Dc93CUBAAAAACJYVAT5FStWKDs7W06nU8OGDdPmzZsbbb98+XL17NlTLpdLWVlZuummm1RWVtZK1TbP/9u0Vx/vO6pYh1V3TzhdJhPPjAcAnHracl8PAEBLi/ggv3r1as2dO1eLFi3S1q1bNWDAAI0dO1Z5eXn1tn/66ac1b948LVq0SNu3b9df//pXrV69WrfeemsrV3583x8t1b3rdkiS/veCnspIcIW5IgAAWl9b7usBAAiFiA/yDz74oH7xi19o+vTp6tOnj1auXCm3263HH3+83vYffPCBRo4cqauvvlrZ2dk6//zzNWnSpON+s9/aDMPQwn98rmKPV2d2StQ1wzqHuyQAAMKirfb1AACESkQHeY/Hoy1btmjMmDGBeWazWWPGjNHGjRvrXWfEiBHasmVLoDP/+uuv9corr2jcuHEN7qe8vFwFBQVBr1B75bMcvbk9TzaLSfdc3l9mM5fUAwBOPW25rwcAIFSs4S6gMYcOHZLX61VaWlrQ/LS0NO3YsaPeda6++modOnRIZ511lgzDUGVlpa677rpGL7dbunSp7rzzzhatvTH5JRVatPa/kqTrR3fXaWlxrbZvAAAiSVvt6wEACKWIPiN/It555x0tWbJEf/rTn7R161a98MILevnll3X33Xc3uM78+fOVn58feO3fvz+kNS59dbsOFZWrW/sYzTqnW0j3BQBAWxMNfT0AAKEU0WfkU1JSZLFYlJubGzQ/NzdX6en1P6ZtwYIFuvbaa/Xzn/9cktSvXz8VFxfrl7/8pW677TaZzXW/u3A4HHI4HC3/Aeqx8asf9MxH/l8e7rm8vxxWS6vsFwCASNQW+3oAAEItos/I2+12DRo0SOvXrw/M8/l8Wr9+vYYPH17vOiUlJXU6cIvFH5YNwwhdsU1QVuHVrS9+Jkm6elgnDclODms9AACEW1vr6wEAaA0RfUZekubOnaupU6dq8ODBGjp0qJYvX67i4mJNnz5dkjRlyhR16NBBS5culSSNHz9eDz74oM444wwNGzZMu3fv1oIFCzR+/PhAJx8uf3zrS+05VKzUOIfmXdgrrLUAABAp2lJfDwBAa4j4ID9x4kQdPHhQCxcuVE5OjgYOHKh169YFBsXZt29f0Lfyt99+u0wmk26//XZ99913at++vcaPH6/FixeH6yNIkrYfKNCfN3wtSbrrktMV77SFtR4AACJFW+nrAQBoLSaDa9DqKCgoUEJCgvLz8xUfH3/S2/P6DF32yAfatv+oxp6epj9fO7gFqgQAnEpaum861XE8AQCRpjl9U0TfI99WPLnxG23bf1RxDqvuuqRvuMsBAAAAAEQxgnyIfXukRPe9tlOSdMuFvZQW7wxzRQAAAACAaEaQDyHDMLTgpc9V4vFqSHaSrh7aKdwlAQAAAACiHEE+hEorvPIakt1i1tLL+slsNoW7JAAAAABAlIv4Ueujmdtu1f9NH6JduUXqnhoX7nIAAAAAAG0AZ+RDzGQyqWc6IR4AAAAA0DII8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEkagI8itWrFB2dracTqeGDRumzZs3N9r+6NGjmjVrljIyMuRwOHTaaafplVdeaaVqAQBAc9HXAwDQdNZwF3A8q1ev1ty5c7Vy5UoNGzZMy5cv19ixY7Vz506lpqbWae/xeHTeeecpNTVVzz33nDp06KC9e/cqMTGx9YsHAADHRV8PAEDzmAzDMMJdRGOGDRumIUOG6OGHH5Yk+Xw+ZWVl6cYbb9S8efPqtF+5cqXuu+8+7dixQzab7YT2WVBQoISEBOXn5ys+Pv6k6gcAoCW05b6Jvh4AgOb1TRF9ab3H49GWLVs0ZsyYwDyz2awxY8Zo48aN9a6zdu1aDR8+XLNmzVJaWpr69u2rJUuWyOv1Nrif8vJyFRQUBL0AAEDo0dcDANB8ER3kDx06JK/Xq7S0tKD5aWlpysnJqXedr7/+Ws8995y8Xq9eeeUVLViwQA888IB+97vfNbifpUuXKiEhIfDKyspq0c8BAADqR18PAEDzRXSQPxE+n0+pqal69NFHNWjQIE2cOFG33XabVq5c2eA68+fPV35+fuC1f//+VqwYAAA0B309AOBUF9GD3aWkpMhisSg3Nzdofm5urtLT0+tdJyMjQzabTRaLJTCvd+/eysnJkcfjkd1ur7OOw+GQw+Fo2eIBAMBx0dcDANB8EX1G3m63a9CgQVq/fn1gns/n0/r16zV8+PB61xk5cqR2794tn88XmLdr1y5lZGTU27EDAIDwoa8HAKD5IjrIS9LcuXP12GOP6f/+7/+0fft2XX/99SouLtb06dMlSVOmTNH8+fMD7a+//nodPnxYs2fP1q5du/Tyyy9ryZIlmjVrVrg+AgAAaAR9PQAAzRPRl9ZL0sSJE3Xw4EEtXLhQOTk5GjhwoNatWxcYFGffvn0ym2u+j8jKytJrr72mm266Sf3791eHDh00e/Zs3XLLLeH6CAAAoBH09QAANE/EP0c+HHi2LAAg0tA3tSyOJwAg0rSZ58gDAAAAAIBgIbu03uv16oknntD69euVl5cXNCCNJL311luh2jUAAAAAAG1WyIL87Nmz9cQTT+iiiy5S3759ZTKZQrUrAAAAAABOGSEL8s8884yeffZZjRs3LlS7AAAAAADglBOyIG+329W9e/dQbR4AEEEMw1BlZaW8Xm+4S4laFotFVquVK9gAAMBxhSzI/+Y3v9Ef/vAHPfzww/xSAgBtmMfj0YEDB1RSUhLuUqKe2+1WRkaG7HZ7uEsBAAARLGRB/r333tPbb7+tV199VaeffrpsNlvQ8hdeeCFUuwYAtBKfz6c9e/bIYrEoMzNTdrudL29PgGEY8ng8OnjwoPbs2aMePXoEPTcdAACgtpAF+cTERF166aWh2jwAIAJ4PB75fD5lZWXJ7XaHu5yo5nK5ZLPZtHfvXnk8HjmdznCXBAAAIlTIgvyqVatCtWkAQITh7HHL4DgCAICmCFmQr3bw4EHt3LlTktSzZ0+1b98+1LsEAAAAAKDNCtlX/8XFxZoxY4YyMjI0atQojRo1SpmZmZo5cyYDIgEAAAAAcIJCdkZ+7ty52rBhg/75z39q5MiRkvwD4P3617/Wb37zGz3yyCOh2jUAAGGRnZ2tOXPmaM6cOeEuJWQ+/fTTJrft379/CCsBAODUFbIg//zzz+u5557T6NGjA/PGjRsnl8ulq666iiAPAAib442sv2jRIt1xxx3N3u5HH32kmJiYE6wqOgwcOFAmk0mGYdS7vHqZyWSS1+tt5eoAADg1hCzIl5SUKC0trc781NRULq0HAITVgQMHAu9Xr16thQsXBsZzkaTY2NjAe8Mw5PV6ZbUev8s8FcaB2bNnT7hLAADglBeye+SHDx+uRYsWqaysLDCvtLRUd955p4YPHx6q3QIAwswwDJV4KsPyaugs8bHS09MDr4SEBJlMpsD0jh07FBcXp1dffVWDBg2Sw+HQe++9p6+++kqXXHKJ0tLSFBsbqyFDhujNN98M2m52draWL18emDaZTPrLX/6iSy+9VG63Wz169NDatWtb8nC3us6dOzf5BQAAQiNkZ+T/8Ic/aOzYserYsaMGDBggSdq2bZucTqdee+21UO0WABBmpRVe9VkYnv/nv7hrrNz2luna5s2bp/vvv19du3ZVUlKS9u/fr3Hjxmnx4sVyOBx68sknNX78eO3cuVOdOnVqcDt33nmn7r33Xt1333364x//qMmTJ2vv3r1KTk5ukTpbW3O+iLj44otDWAkAAKeukAX5vn376ssvv9RTTz2lHTt2SJImTZqkyZMny+VyhWq3AAC0iLvuukvnnXdeYDo5OTnwxbQk3X333XrxxRe1du1a3XDDDQ1uZ9q0aZo0aZIkacmSJXrooYe0efNmXXDBBaErPoQmTJjQpHbcIw8AQOiE9Dnybrdbv/jFL0K5CwBAhHHZLPrirrFh23dLGTx4cNB0UVGR7rjjDr388ss6cOCAKisrVVpaqn379jW6ndojt8fExCg+Pl55eXktVmdr8/l84S4BAIBTXosG+bVr1+rCCy+UzWY77qV3XG4HAG2TyWRqscvbw+nY0edvvvlmvfHGG7r//vvVvXt3uVwuXXHFFfJ4PI1ux2azBU2bTCbCMAAAOCkt+pvWhAkTlJOTo9TU1EYvveNyOwBAtHn//fc1bdo0XXrppZL8Z+i/+eab8BYVAYqLi7Vhwwbt27evzpcav/71r8NUFQAAbVuLBvnaZxg42wAAaEt69OihF154QePHj5fJZNKCBQtO+b7u448/1rhx41RSUqLi4mIlJyfr0KFDcrvdSk1NJcgDABAiIXv8XH2OHj3amrsDAKDFPPjgg0pKStKIESM0fvx4jR07VmeeeWa4ywqrm266SePHj9eRI0fkcrm0adMm7d27V4MGDdL9998f7vIAAGizTEZTH7rbTMuWLVN2drYmTpwoSbryyiv1/PPPKyMjQ6+88krQyL+RpqCgQAkJCcrPz1d8fHy4ywGAiFVWVqY9e/aoS5cucjqd4S4n6jV2PCOxb0pMTNSHH36onj17KjExURs3blTv3r314YcfaurUqYGn1kSiSDyeAIBTW3P6ppCdkV+5cqWysrIkSW+88YbefPNNrVu3ThdeeKF++9vfhmq3AACgldhsNpnN/l8lUlNTAyP4JyQkaP/+/eEsDQCANi1kwwrn5OQEgvy//vUvXXXVVTr//POVnZ2tYcOGhWq3AACglZxxxhn66KOP1KNHD5199tlauHChDh06pL/97W/q27dvuMsDAKDNCtkZ+aSkpMC38evWrdOYMWMkSYZhMGI9AABtwJIlS5SRkSFJWrx4sZKSknT99dfr4MGD+vOf/xzm6gAAaLtCdkb+sssu09VXX60ePXrohx9+0IUXXijJP8Jt9+7dQ7VbAADQSgYPHhx4n5qaqnXr1oWxGgAATh0hC/K///3vlZ2drf379+vee+9VbGysJOnAgQP61a9+FardAgCAVrJnzx5VVlaqR48eQfO//PJL2Ww2ZWdnh6cwAADauJAFeZvNpptvvrnO/JtuuilUuwQAAK1o2rRpmjFjRp0g/+GHH+ovf/mL3nnnnfAUBgBAG9eiQX7t2rW68MILZbPZtHbt2kbbXnzxxS25awAA0Mo+/vhjjRw5ss78H/3oR7rhhhvCUBEAAKeGFg3yEyZMUE5OjlJTUzVhwoQG25lMJga8AwAgyplMJhUWFtaZn5+fTz8PAEAIteio9T6fT6mpqYH3Db3o3AEAiH6jRo3S0qVLg/p1r9erpUuX6qyzzgpjZQAAtG0hu0ceAIC2bPTo0Ro4cKCWL18e7lLCZtmyZRo1apR69uypH//4x5Kkd999VwUFBXrrrbfCXB0AAG1XyJ4j/+tf/1oPPfRQnfkPP/yw5syZE6rdAgBwXOPHj9cFF1xQ77J3331XJpNJn376aStXFX369OmjTz/9VFdddZXy8vJUWFioKVOmaMeOHerbt2+4ywMAoM0K2Rn5559/vt4B70aMGKF77rnnlD6DAQAIr5kzZ+ryyy/Xt99+q44dOwYtW7VqlQYPHqz+/fuHqbrokpmZqSVLloS7DAAATikhOyP/ww8/KCEhoc78+Ph4HTp0KFS7BQBEiBJPZYOvsgpvi7dtjp/+9Kdq3769nnjiiaD5RUVFWrNmjSZMmKBJkyapQ4cOcrvd6tevn/7+97+f0HFo6959911dc801GjFihL777jtJ0t/+9je99957Ya4MAIC2K2Rn5Lt3765169bVefzMq6++qq5du4ZqtwCACNFn4WsNLjunZ3utmj40MD3o7jdVWlH/QKjDuiRr9f8MD0yftextHS721Gn3zT0XNbk2q9WqKVOm6IknntBtt90mk8kkSVqzZo28Xq+uueYarVmzRrfccovi4+P18ssv69prr1W3bt00dOjQ42z91PH888/r2muv1eTJk7V161aVl5dL8o9av2TJEr3yyithrhAAgLYpZGfk586dq//93//VokWLtGHDBm3YsEELFy7UvHnzdNNNN4VqtwAANMmMGTP01VdfacOGDYF5q1at0uWXX67OnTvr5ptv1sCBA9W1a1fdeOONuuCCC/Tss8+GseLI87vf/U4rV67UY489JpvNFpg/cuRIbd26NYyVAQDQtoXsjPyMGTNUXl6uxYsX6+6775YkZWdn65FHHtGUKVNCtVsAQIT44q6xDS4zV50Br7ZlwZgmt33vlnNOrrAqvXr10ogRI/T4449r9OjR2r17t959913ddddd8nq9WrJkiZ599ll999138ng8Ki8vl9vtbpF9txU7d+7UqFGj6sxPSEjQ0aNHW78gAABOESF9/Nz111+v66+/XgcPHpTL5VJsbGwodwcAiCBue9O7mFC1PZ6ZM2fqxhtv1IoVK7Rq1Sp169ZNZ599tpYtW6Y//OEPWr58ufr166eYmBjNmTNHHk/dS/pPZenp6dq9e7eys7OD5r/33nvcRgcAQAiF7NJ6SaqsrNSbb76pF154QYZhSJK+//57FRUVhXK3AAA0yVVXXSWz2aynn35aTz75pGbMmCGTyaT3339fl1xyia655hoNGDBAXbt21a5du8JdbsT5xS9+odmzZ+vDDz+UyWTS999/r6eeekq/+c1vdP3114e7PAAA2qyQnZHfu3evLrjgAu3bt0/l5eU677zzFBcXp2XLlqm8vFwrV64M1a4BAGiS2NhYTZw4UfPnz1dBQYGmTZsmSerRo4eee+45ffDBB0pKStKDDz6o3Nxc9enTJ7wFR5h58+bJ5/Pp3HPPVUlJiUaNGiWHw6Hf/va3+vnPfx7u8gAAaLNCdkZ+9uzZGjx4sI4cOSKXyxWYf+mll2r9+vWh2i0AAM0yc+ZMHTlyRGPHjlVmZqYk6fbbb9eZZ56psWPHavTo0UpPT9eECRPCW2gEMplMuu2223T48GF9/vnn2rRpkw4ePKiEhAR16dIl3OUBANBmheyM/LvvvqsPPvhAdrs9aH52dnbgObMAAITb8OHDA7d/VUtOTtZLL73U6HrvvPNO6IqKcOXl5brjjjv0xhtvBM7AT5gwQatWrdKll14qi8XCE2oAAAihkAV5n88nr7fuM4G//fZbxcXFhWq3AAAgxBYuXKg///nPGjNmjD744ANdeeWVmj59ujZt2qQHHnhAV155pSwWS7jLBACgzQrZpfXnn3++li9fHpg2mUwqKirSokWLNG7cuFDtFgAAhNiaNWv05JNP6rnnntPrr78ur9eryspKbdu2TT/72c8I8QAAhFjIzsjff//9uuCCC9SnTx+VlZXp6quv1pdffqmUlBT9/e9/D9VuAQBAiH377bcaNGiQJKlv375yOBy66aabZDKZwlwZAACnhpAF+aysLG3btk2rV6/Wtm3bVFRUpJkzZ2ry5MlBg98BAIDo4vV6g8bAsVqtio2NDWNFAACcWkIS5CsqKtSrVy/961//0uTJkzV58uRQ7AYAECGOHSwOJyZajqNhGJo2bZocDockqaysTNddd51iYmKC2r3wwgvhKA8AgDYvJEHeZrOprKwsFJsGAEQQm80mSSopKeFqqxZQUlIiqea4RqqpU6cGTV9zzTVhqgQAgFNTyC6tnzVrlpYtW6a//OUvslpDthsAQBhZLBYlJiYqLy9PkuR2u7lP+gQYhqGSkhLl5eUpMTEx4geLW7VqVbhLAADglBayhP3RRx9p/fr1ev3119WvXz8utwOANio9PV2SAmEeJy4xMTFwPAEAABoSsiCfmJioyy+/PFSbBwBECJPJpIyMDKWmpqqioiLc5UQtm80W8WfiAQBAZGjxIO/z+XTfffdp165d8ng8+slPfqI77riDeycBoI2zWCwEUQAAgFZgbukNLl68WLfeeqtiY2PVoUMHPfTQQ5o1a1ZL7wYAAAAAgFNSiwf5J598Un/605/02muv6aWXXtI///lPPfXUU/L5fC29KwAAAAAATjktHuT37duncePGBabHjBkjk8mk77//vqV3BQAAAADAKafFg3xlZaWcTmfQPJvNxgBIAAAAAAC0gBYf7M4wDE2bNk0OhyMwr6ysTNddd13QI+h4/BwAAAAAAM3X4mfkp06dqtTUVCUkJARe11xzjTIzM4PmNceKFSuUnZ0tp9OpYcOGafPmzU1a75lnnpHJZNKECRNO4JMAAIDWRH8PAEDTtPgZ+VWrVrXo9lavXq25c+dq5cqVGjZsmJYvX66xY8dq586dSk1NbXC9b775RjfffLN+/OMft2g9AACg5dHfAwDQdC1+Rr6lPfjgg/rFL36h6dOnq0+fPlq5cqXcbrcef/zxBtfxer2aPHmy7rzzTnXt2rUVqwUAACeC/h4AgKaL6CDv8Xi0ZcsWjRkzJjDPbDZrzJgx2rhxY4Pr3XXXXUpNTdXMmTObtJ/y8nIVFBQEvQAAQOtojf6evh4A0JZEdJA/dOiQvF6v0tLSguanpaUpJyen3nXee+89/fWvf9Vjjz3W5P0sXbo06P79rKysk6obAAA0XWv09/T1AIC2JKKDfHMVFhbq2muv1WOPPaaUlJQmrzd//nzl5+cHXvv37w9hlQAA4GScSH9PXw8AaEtafLC7lpSSkiKLxaLc3Nyg+bm5uUpPT6/T/quvvtI333yj8ePHB+b5fD5JktVq1c6dO9WtW7c66zkcjqDH5QEAgNbTGv09fT0AoC2J6DPydrtdgwYN0vr16wPzfD6f1q9fr+HDh9dp36tXL3322Wf65JNPAq+LL75Y55xzjj755BMuowMAIALR3wMA0DwRfUZekubOnaupU6dq8ODBGjp0qJYvX67i4mJNnz5dkjRlyhR16NBBS5culdPpVN++fYPWT0xMlKQ68wEAQOSgvwcAoOkiPshPnDhRBw8e1MKFC5WTk6OBAwdq3bp1gQFx9u3bJ7M5oi8sAAAAx0F/DwBA05kMwzDCXUSkKSgoUEJCgvLz8xUfHx/ucgAAoG9qYRxPAECkaU7fxFfbAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAUIcgDAAAAABBFCPIAAAAAAEQRgjwAAAAAAFGEIA8AAAAAQBQhyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFImKIL9ixQplZ2fL6XRq2LBh2rx5c4NtH3vsMf34xz9WUlKSkpKSNGbMmEbbAwCAyEB/DwBA00R8kF+9erXmzp2rRYsWaevWrRowYIDGjh2rvLy8etu/8847mjRpkt5++21t3LhRWVlZOv/88/Xdd9+1cuUAAKCp6O8BAGg6k2EYRriLaMywYcM0ZMgQPfzww5Ikn8+nrKws3XjjjZo3b95x1/d6vUpKStLDDz+sKVOmNGmfBQUFSkhIUH5+vuLj40+qfgAAWkJb75tau79v68cTABB9mtM3RfQZeY/Hoy1btmjMmDGBeWazWWPGjNHGjRubtI2SkhJVVFQoOTm5wTbl5eUqKCgIegEAgNbRGv09fT0AoC2J6CB/6NAheb1epaWlBc1PS0tTTk5Ok7Zxyy23KDMzM+iXg2MtXbpUCQkJgVdWVtZJ1Q0AAJquNfp7+noAQFsS0UH+ZN1zzz165pln9OKLL8rpdDbYbv78+crPzw+89u/f34pVAgCAk9GU/p6+HgDQlljDXUBjUlJSZLFYlJubGzQ/NzdX6enpja57//3365577tGbb76p/v37N9rW4XDI4XCcdL0AAKD5WqO/p68HALQlEX1G3m63a9CgQVq/fn1gns/n0/r16zV8+PAG17v33nt19913a926dRo8eHBrlAoAAE4Q/T0AAM0T0WfkJWnu3LmaOnWqBg8erKFDh2r58uUqLi7W9OnTJUlTpkxRhw4dtHTpUknSsmXLtHDhQj399NPKzs4O3FsXGxur2NjYsH0OAADQMPp7AACaLuKD/MSJE3Xw4EEtXLhQOTk5GjhwoNatWxcYEGffvn0ym2suLHjkkUfk8Xh0xRVXBG1n0aJFuuOOO1qzdAAA0ET09wAANF3EP0c+HHi2LAAg0tA3tSyOJwAg0rSZ58gDAAAAAIBgBHkAAAAAAKIIQR4AAAAAgChCkAcAAAAAIIoQ5AEAAAAAiCIEeQAAAAAAoghBHgAAAACAKEKQBwAAAAAgihDkAQAAAACIItZwFwAAQHN5fYa+P1qqvT+UaO/hYh0p9kiSLhnYQVnJbknStv1H9fbOPBmGZFSvaBiB9xPO6KBu7WMlSZ9/l69XPjsgw9/E31Q1K14ysIP6ZMZLkr74vkDPb/22artG9WYDLh6YqTM7JYXok6O1eb1eVVRUhLuMqGWz2WSxWMJdBgC0OQR5AEBEKqvw6tsjJcpMdMlt93dXz235Vive3q1vj5SowmvUWefMzkmBIP/pt0e1/M0vG9z+wKzEQJDfkVOoP73zVYNt+3ZICAT5PYeK9df39jTYtk9mPEG+DTAMQzk5OTp69Gi4S4l6iYmJSk9Pl8lkCncpANBmEOQBAGGVk1+mrfuO+M+u/1Ac+HmgoEyGIT3982Ea0T1FkuQzDO05VCxJslvM6tTOrc7JbqXEOmQ2S6lxzsB2T0uL0+RhnVSdHUwyyWSSqqNExyR3oG2P1FjNGNnF365WG5NJMplM6to+JtC2W2qMrju7W63tVq/jn9EnI75lDxDCojrEp6amyu12E0JPgGEYKikpUV5eniQpIyMjzBUBQNtBkAcAhIxhGDpSUqFvfijWvh9KAj9nnNVFfTskSJI27MrTLc9/Vu/6MXaLjpTUXNY8+rT2evoXw5TdLkbp8U6ZzQ2Hq2Fd22lY13ZNqnNAVqIGZCU2qW2v9HjNu5Cw3pZ5vd5AiG/Xrml/h1A/l8slScrLy1NqaiqX2QNACyHIAwBOis9nKLewTDEOq+KdNknSe18e0j3rtmvvoRIVllfWWWdY1+RAkO+eGqczOiWqc7JbndvFqHM7d9UrRu1i7EFnQlPjnUqNd9bZHtCSqu+Jd7vdx2mJpqg+jhUVFQR5AGghBHkAQJPkl1Tok2+PBl3+vveHEu07XKLySp/uvaK/rhqcJUkym6TPvysIrJuR4FSn5JqA3r9jYmDZoM5JevFXI1v74wDHxeX0LYPjCAAtjyAPAJBhGDpU5NH+IyXaf7hE+34o0f4jJRrXL0Oje6ZKkj77Ll9TH99c7/oWs0lHSzyB6dM7JOgvUwarczu3spLdcto4CwcAANBSCPIAcIoo8VTq2yOlinVYlZnov2/1i+8LdNPqT7TvcIlKK7x11kmPdwaCfHaKW6elxfovf691dr1zO7cyE12yWcyB9RJcNo3pk9Y6HwxASGVnZ2vOnDmaM2dOuEsBAFQhyANAG3O0xKPX/5ur/Uf8l73vO1yi/YdLdaioXJL063N7aO55p0mSYhwW7cwtlOQfeT0j3qmOyW51qnqN6FYz0FfHJLdev+ns1v9AAJrkeJewL1q0SHfccUezt/vRRx8pJibm+A0BAK2GIA+gzTEMQyUer/JLK1RQVqH8kgoVlFWqczu3TkuLk+S/3/uht76Uy2aRy26Rw2qWy27xT9ss6tI+Rr3S/SOTe32GvjtSKqfdHFhurXX2uTXll1QEAvr+wzVB/bw+aZoyPFuSdLSkQv/7/Kf1rh/ntMrnq3n+emaiS/83Y6g6JbuVmeiUw8ol8EC0OnDgQOD96tWrtXDhQu3cuTMwLzY2NvDeMAx5vV5Zrcf/VbB9+/YtWygA4KQR5AFEtOLySu3MLVRBaUVVMK9UQWmF/1VWoXN7pQUu4d5+oEBXP7ZJBWWV8tYKq9VmndNNvx3bS5J0uMSjv763p8H9ThuRrTsuPl2S9ENRuUbd93bQcpvFJKfVIqfdosvO7KD5F/aWJJV6vPrVU1vkrAr8zqovB5w2/5cAvTPidW5vf72GYeidnQf9bWt9iVDp82n/kVIluW2BQeG+PVKicX94VwVldUeAl6T2cQ5NGe5/n5no0o97pKhTsv/+9Oqz61lJbiW4bcd8DrPOPo1f0oHjMQyj3ttPWoPLZmnSgHHp6emB9wkJCTKZTIF577zzjs455xy98soruv322/XZZ5/p9ddfV1ZWlubOnatNmzapuLhYvXv31tKlSzVmzJjAto69tN5kMumxxx7Tyy+/rNdee00dOnTQAw88oIsvvrhlPzgAoEEEeQAh46n0KbegTPmlFWof51Ba1WPDvj1SotUf7fcH86pwXv0+v7RC14/upukju0iSvswr0mV/+qDBfSTH2ANB3mkLfua4zWJSgsumeKdNcS6bUuNqHlsW67DqurO7qazCq1KPV2WV/p+lFV6VVXjVJaXmMtLySp/cdotKK7wyqr4fqPAaqvBWqrC8UiXlNb/cF3sq9fbOgw3We9mZHQJBvrzSp+lPfNRg2ysGddT9VyZK8gf16se4tY9zKCvJFQjoHZPd6pNR81xzu9Wsv80c1uB2ATRfaYVXfRa+FpZ9f3HXWLntLfMr27x583T//fera9euSkpK0v79+zVu3DgtXrxYDodDTz75pMaPH6+dO3eqU6dODW7nzjvv1L333qv77rtPf/zjHzV58mTt3btXycnJLVInAKBxBHkALeLbIyV6cet3gUu9vz1Squ/zSwPB99ZxvfTLUd0kST8UefTHt3Y3uK0fimpGP09229Uh0eUP5C5rIJj7p20a2qXml8YOiS69ftOowHKnzdzgWaz2cQ7Nu7BXkz5bVrJbX9x1gQzDUHmlT2UVXpVV+FRa9SVAvKvmv1K33aL7rugf3Kb6y4IKr87slBRoW+H1qV+HhKDlpRVemeS/Hz01zhFo67Ba9Obcs5WR4GyxX+gBnHruuusunXfeeYHp5ORkDRgwIDB9991368UXX9TatWt1ww03NLidadOmadKkSZKkJUuW6KGHHtLmzZt1wQUXhK54AEAAvw0CaFSJp1L7D5cG3ZO9/7D/0WRTR2Rr8rDOkvzh+4E3dtVZ3241K8ltk8Vcc095RoJTU4d3VnxQKLcGpjMSas6cd2rn1vvzftKkWu1Wc+Ae+FAwmUxy2iyNPkrNbbfqyqpnqR9PnNOmf954VpP336197PEbAQgJl82iL+4aG7Z9t5TBgwcHTRcVFemOO+7Qyy+/rAMHDqiyslKlpaXat29fo9vp379/4H1MTIzi4+OVl5fXYnUCABpHkAdOcZVenw7klwWeH949NU6DOvvPGn/2bb7GP/xeg+vuzisKvM9uF6OrBndUVpJbndq51THJf9l3Sqy9zlnx1Hin7rykb2g+EACEgMlkahNXwxw7+vzNN9+sN954Q/fff7+6d+8ul8ulK664Qh6Pp4Et+NlsweNtmEwm+Xy+Fq8XAFC/6O+RADTKMAxV+ozAM77zCsr0+zd3Bc6yf3+0VJW1BoabMbJLIMhnJvrPjCe4bFUDp7mUVTVoWqdkt3qk1ZwhTnDbdO8VAwQAiB7vv/++pk2bpksvvVSS/wz9N998E96iAADHRZAH2oBKr0/f/FASOKu+v9azw/cfLtFVQ7K04Kd9JElms0l/37w/aH27xayOSf6Q3i215mxNcoxd2xadrwRX8JkXAEDb0KNHD73wwgsaP368TCaTFixYwJl1AIgCBHkgAhiGoaLySvkMBUJzqcerf277XvlVI7kfLfUov7QyMH12jxTNPb+nJOlISYXGPLihwe3vO1wSeN8uxq6bxpymDlWjnmclu5QW55TZXHdQOJPJRIgHgDbswQcf1IwZMzRixAilpKTolltuUUFBQbjLAgAch8kwjLoPWz7FFRQUKCEhQfn5+YqPjz/+CoAkn89QYXllzfPOq35mJLo0MCtRknS0xKPbXvo88Bz0/FrPRvf6DE0amqWll/kHEMovrdCAO19vcH/j+qXrT5MHSfJ/ETBk8XqlxNrrPjs82aWOSe5GB2gDEPnom1pWY8ezrKxMe/bsUZcuXeR0OhvYApqK4wkATdOcvp4z8kA9yiu92nGgsCpk1wTu6oA+tEuyLj2joyTpQH6pLlj+rgrLKuSr52uxSUOzAkHebDbp5U8PNLjfwrLKwPs4h1U/6ZWqeKf/kWvVj1urfp+V7A60NZlM+s/tY1rmwwMAAACIaAR5tCmGYaiswhcI4HFOqzISXJKkw8Ue/W3j3jrhvPrs+JWDs3TTeadJkvIKynXJivcb3Vd1kHfbrcovrQjMd1jNgbB9bOCOtVu14Kd9AssS3bagtrXPmpvNJj0+bUiLHRsAAAAAbQNBHhHH5zNUWFZZb+DOL63QgKxE/ahrO0nS3h+KNfuZT1RQVh3IK+Xx1gzS8z+jumr+uN6SpOLySv3+zbrPOa+WV1gWeJ/gtikzwel/rnn12XCn/1nnCS6bBnRMDLSNd1r15txRgWegN3YJu9ls0syzupzooQEAAAAAgjxaVqXXp8KyShWWVSrWaVVyjF2SdLCwXC9/+r0KyypVVF6pgrJKFZZVVLX1nw2fNLSTJGl7ToEueqjhZ5f/z9ldA0HebDLpk/1H67SxmE2Kd1pltdQM4JYcY9ekoZ1qXaZuDQT0BJdNGQk19+3FO236YP65TfrMJpNJ3VPjmtQWAAAAAE4WQR5Byiq8OpBfVitkVwa9H9m9nQZnJ0uSth8o0PwXPgtaXlrhDWzrt2N7atY53SVJuQVluuOfXzS43yFdkgPv453+UdKdtppL1KvDdoLLptMzEwJtU+MdevTaQUH3jse7bIqxW2QyBY/CHuOwaull/U7+IAEAAABAGBHkTyH5JRXakVOg7QcKtCOnULtyC3WkpEK/Gt1NVw7OkiR9vO+oJj22qcFtWC09A0He6zPqPRsuSS6bRb5aI7+lxjk0rl+64hw2xTmtinNW//S/754aG2jbIdGlnb+7QA7r8UdZd1gtOv/09KZ8fAAAAABoEwjybVCl1yeP1ye33f/Hu3XfEd3w1FZ9n19Wb/u8wvLA+3iXVbEOayBk+9/XhO8+GTWPQchOidFjUwYH2sdXtYt1WmWzmIP2kRrvDDwq7XjMZpMcZh6VBgAAAAD1IchHucPFHu04UKDtOYVVZ9oLtCu3SDec012/PreHJKl9rCMQ4jsmudQrPV69M+LUMz1OqXFOdW5XM6r66ZkJ+vzOsU3ad6zDqvP6pLX8hwIAAAAANIggHyUqvD4Vl1cq0e0fPO77o6WasOL9oLPptX2ZVxR43zHJpTXXDVfP9LjA/ecAAAAAgOhEkI9ABwvLtSOnQDsO+M+yb88p1O68Qo0fkKkHrxooyX/P+dES/7PLO7dzq1d6XNWZdv/Z9qykmrPsJpNJQ7KT69sVAABAwOjRozVw4EAtX7483KUAABpBkA+j8kqvjpZUKC3e/9izCq9PZy17S7kF9Z9l3/tDSeC91WLWS7NGqlM7t2Id/DECAHCqGz9+vCoqKrRu3bo6y959912NGjVK27ZtU//+/cNQHQCgJZEAW4FhGDpYWK4vqkaL33GgQNsPFOqrg0UakJWo568fIUmyWcyKcVhlMpWrS7sY9crwn2XvlR6n3hnx6pjkCtpun8z4+nYHAABOQTNnztTll1+ub7/9Vh07dgxatmrVKg0ePJgQDwBtBEG+FVz00Hv64kBBvcty8stkGEbgmeePTx2i1HhHYMR5AAAQOUo8lQ0uM5tMctosLdq2Ob8P/PSnP1X79u31xBNP6Pbbbw/MLyoq0po1azRv3jxNmjRJ//73v3XkyBF169ZNt956qyZNmtTkfQAAIgNpsRWkJzi1I6dAXVJiqu5h959l75URr8wEZyDES/5HugEAgMjUZ+FrDS47p2d7rZo+NDA96O43VVrhrbftsC7JWv0/wwPTZy17W4eLPXXafXPPRU2uzWq1asqUKXriiSd02223BX6/WLNmjbxer6655hqtWbNGt9xyi+Lj4/Xyyy/r2muvVbdu3TR06NDjbB0AEEkI8q3gnsv6Kd5lC/rmHQAAoKXNmDFD9913nzZs2KDRo0dL8l9Wf/nll6tz5866+eabA21vvPFGvfbaa3r22WcJ8gAQZQjyrSC1ajA7AAAQ3b64a2yDy8y1rrCTpC0LxjS57Xu3nHNyhVXp1auXRowYoccff1yjR4/W7t279e677+quu+6S1+vVkiVL9Oyzz+q7776Tx+NReXm53G738TcMAIgoBHkAAIAmas4966FqezwzZ87UjTfeqBUrVmjVqlXq1q2bzj77bC1btkx/+MMftHz5cvXr108xMTGaM2eOPJ66l/QDACKbOdwFAAAAoOVcddVVMpvNevrpp/Xkk09qxowZMplMev/993XJJZfommuu0YABA9S1a1ft2rUr3OUCAE4AQR4AAKANiY2N1cSJEzV//nwdOHBA06ZNkyT16NFDb7zxhj744ANt375d//M//6Pc3NzwFgsAOCEEeQAAgDZm5syZOnLkiMaOHavMzExJ0u23364zzzxTY8eO1ejRo5Wenq4JEyaEt1AAwAnhHnkAAIA2Zvjw4TIMI2hecnKyXnrppUbXe+edd0JXFACgxXBGHgAAAACAKEKQBwAAAAAgihDkAQAAAACIIgR5AAAAAACiCEEeAACgHscOFocTw3EEgJZHkAcAAKjFZrNJkkpKSsJcSdtQfRyrjysA4OTx+DkAAIBaLBaLEhMTlZeXJ0lyu90ymUxhrir6GIahkpIS5eXlKTExURaLJdwlAUCbQZAHAAA4Rnp6uiQFwjxOXGJiYuB4AgBaBkEeAADgGCaTSRkZGUpNTVVFRUW4y4laNpuNM/EAEAIEeQAAgAZYLBaCKAAg4kTFYHcrVqxQdna2nE6nhg0bps2bNzfafs2aNerVq5ecTqf69eunV155pZUqBQAAJ4r+HgCApon4IL969WrNnTtXixYt0tatWzVgwACNHTu2wXvWPvjgA02aNEkzZ87Uxx9/rAkTJmjChAn6/PPPW7lyAADQVPT3AAA0ncmI8Id7Dhs2TEOGDNHDDz8sSfL5fMrKytKNN96oefPm1Wk/ceJEFRcX61//+ldg3o9+9CMNHDhQK1eubNI+CwoKlJCQoPz8fMXHx7fMBwEA4CS09b6ptfv7tn48AQDRpzl9U0TfI+/xeLRlyxbNnz8/MM9sNmvMmDHauHFjvets3LhRc+fODZo3duxYvfTSSw3up7y8XOXl5YHp/Px8Sf4DCQBAJKjukyL8+/cT0hr9PX09ACDSNaevj+ggf+jQIXm9XqWlpQXNT0tL044dO+pdJycnp972OTk5De5n6dKluvPOO+vMz8rKOoGqAQAIncLCQiUkJIS7jBbVGv09fT0AIFo0pa+P6CDfWubPnx/0rb7P59Phw4fVrl07mUymk9p2QUGBsrKytH//fi7dayKOWfNxzJqPY9Z8HLPma8ljZhiGCgsLlZmZ2ULVnVpC2ddL/PtoLo5X83HMmo9j1nwcs+YLV18f0UE+JSVFFotFubm5QfNzc3OVnp5e7zrp6enNai9JDodDDocjaF5iYuKJFd2A+Ph4/jE0E8es+Thmzccxaz6OWfO11DFra2fiq7VGf98afb3Ev4/m4ng1H8es+Thmzccxa77W7usjetR6u92uQYMGaf369YF5Pp9P69ev1/Dhw+tdZ/jw4UHtJemNN95osD0AAAgv+nsAAJonos/IS9LcuXM1depUDR48WEOHDtXy5ctVXFys6dOnS5KmTJmiDh06aOnSpZKk2bNn6+yzz9YDDzygiy66SM8884z+85//6NFHHw3nxwAAAI2gvwcAoOkiPshPnDhRBw8e1MKFC5WTk6OBAwdq3bp1gQFu9u3bJ7O55sKCESNG6Omnn9btt9+uW2+9VT169NBLL72kvn37hqV+h8OhRYsW1bmcDw3jmDUfx6z5OGbNxzFrPo5Z09Hfn1o4Xs3HMWs+jlnzccyaL1zHLOKfIw8AAAAAAGpE9D3yAAAAAAAgGEEeAAAAAIAoQpAHAAAAACCKEOQBAAAAAIgiBPkQW7FihbKzs+V0OjVs2DBt3rw53CVFrKVLl2rIkCGKi4tTamqqJkyYoJ07d4a7rKhxzz33yGQyac6cOeEuJaJ99913uuaaa9SuXTu5XC7169dP//nPf8JdVsTyer1asGCBunTpIpfLpW7duunuu+8W46TW+Pe//63x48crMzNTJpNJL730UtBywzC0cOFCZWRkyOVyacyYMfryyy/DUyxCgr6+6ejrTx79fdPQ3zcP/f3xRVp/T5APodWrV2vu3LlatGiRtm7dqgEDBmjs2LHKy8sLd2kRacOGDZo1a5Y2bdqkN954QxUVFTr//PNVXFwc7tIi3kcffaQ///nP6t+/f7hLiWhHjhzRyJEjZbPZ9Oqrr+qLL77QAw88oKSkpHCXFrGWLVumRx55RA8//LC2b9+uZcuW6d5779Uf//jHcJcWMYqLizVgwACtWLGi3uX33nuvHnroIa1cuVIffvihYmJiNHbsWJWVlbVypQgF+vrmoa8/OfT3TUN/33z098cXcf29gZAZOnSoMWvWrMC01+s1MjMzjaVLl4axquiRl5dnSDI2bNgQ7lIiWmFhodGjRw/jjTfeMM4++2xj9uzZ4S4pYt1yyy3GWWedFe4yospFF11kzJgxI2jeZZddZkyePDlMFUU2ScaLL74YmPb5fEZ6erpx3333BeYdPXrUcDgcxt///vcwVIiWRl9/cujrm47+vuno75uP/r55IqG/54x8iHg8Hm3ZskVjxowJzDObzRozZow2btwYxsqiR35+viQpOTk5zJVEtlmzZumiiy4K+ruG+q1du1aDBw/WlVdeqdTUVJ1xxhl67LHHwl1WRBsxYoTWr1+vXbt2SZK2bdum9957TxdeeGGYK4sOe/bsUU5OTtC/z4SEBA0bNoy+oA2grz959PVNR3/fdPT3zUd/f3LC0d9bQ7JV6NChQ/J6vUpLSwuan5aWph07doSpqujh8/k0Z84cjRw5Un379g13ORHrmWee0datW/XRRx+Fu5So8PXXX+uRRx7R3Llzdeutt+qjjz7Sr3/9a9ntdk2dOjXc5UWkefPmqaCgQL169ZLFYpHX69XixYs1efLkcJcWFXJyciSp3r6gehmiF339yaGvbzr6++ahv28++vuTE47+niCPiDRr1ix9/vnneu+998JdSsTav3+/Zs+erTfeeENOpzPc5UQFn8+nwYMHa8mSJZKkM844Q59//rlWrlxJx96AZ599Vk899ZSefvppnX766frkk080Z84cZWZmcswAnBT6+qahv28++vvmo7+PPlxaHyIpKSmyWCzKzc0Nmp+bm6v09PQwVRUdbrjhBv3rX//S22+/rY4dO4a7nIi1ZcsW5eXl6cwzz5TVapXVatWGDRv00EMPyWq1yuv1hrvEiJORkaE+ffoEzevdu7f27dsXpooi329/+1vNmzdPP/vZz9SvXz9de+21uummm7R06dJwlxYVqv+/py9om+jrTxx9fdPR3zcf/X3z0d+fnHD09wT5ELHb7Ro0aJDWr18fmOfz+bR+/XoNHz48jJVFLsMwdMMNN+jFF1/UW2+9pS5duoS7pIh27rnn6rPPPtMnn3wSeA0ePFiTJ0/WJ598IovFEu4SI87IkSPrPOZo165d6ty5c5gqinwlJSUym4O7CovFIp/PF6aKokuXLl2Unp4e1BcUFBToww8/pC9oA+jrm4++vvno75uP/r756O9PTjj6ey6tD6G5c+dq6tSpGjx4sIYOHarly5eruLhY06dPD3dpEWnWrFl6+umn9Y9//ENxcXGB+0kSEhLkcrnCXF3kiYuLq3NPYUxMjNq1a8e9hg246aabNGLECC1ZskRXXXWVNm/erEcffVSPPvpouEuLWOPHj9fixYvVqVMnnX766fr444/14IMPasaMGeEuLWIUFRVp9+7dgek9e/bok08+UXJysjp16qQ5c+bod7/7nXr06KEuXbpowYIFyszM1IQJE8JXNFoMfX3z0Nc3H/1989HfNx/9/fFFXH8fkrHwEfDHP/7R6NSpk2G3242hQ4camzZtCndJEUtSva9Vq1aFu7SoweNoju+f//yn0bdvX8PhcBi9evUyHn300XCXFNEKCgqM2bNnG506dTKcTqfRtWtX47bbbjPKy8vDXVrEePvtt+v9v2vq1KmGYfgfSbNgwQIjLS3NcDgcxrnnnmvs3LkzvEWjRdHXNx19fcugvz8++vvmob8/vkjr702GYRih+YoAAAAAAAC0NO6RBwAAAAAgihDkAQAAAACIIgR5AAAAAACiCEEeAAAAAIAoQpAHAAAAACCKEOQBAAAAAIgiBHkAAAAAAKIIQR4AAAAAgChCkAcQkUwmk1566aVwlwEAAEKEvh44cQR5AHVMmzZNJpOpzuuCCy4Id2kAAKAF0NcD0c0a7gIARKYLLrhAq1atCprncDjCVA0AAGhp9PVA9OKMPIB6ORwOpaenB72SkpIk+S+Fe+SRR3ThhRfK5XKpa9eueu6554LW/+yzz/STn/xELpdL7dq10y9/+UsVFRUFtXn88cd1+umny+FwKCMjQzfccEPQ8kOHDunSSy+V2+1Wjx49tHbt2tB+aAAATiH09UD0IsgDOCELFizQ5Zdfrm3btmny5Mn62c9+pu3bt0uSiouLNXbsWCUlJemjjz7SmjVr9OabbwZ13o888ohmzZqlX/7yl/rss8+0du1ade/ePWgfd955p6666ip9+umnGjdunCZPnqzDhw+36ucEAOBURV8PRDADAI4xdepUw2KxGDExMUGvxYsXG4ZhGJKM6667LmidYcOGGddff71hGIbx6KOPGklJSUZRUVFg+csvv2yYzWYjJyfHMAzDyMzMNG677bYGa5Bk3H777YHpoqIiQ5Lx6quvttjnBADgVEVfD0Q37pEHUK9zzjlHjzzySNC85OTkwPvhw4cHLRs+fLg++eQTSdL27ds1YMAAxcTEBJaPHDlSPp9PO3fulMlk0vfff69zzz230Rr69+8feB8TE6P4+Hjl5eWd6EcCAAC10NcD0YsgD6BeMTExdS5/aykul6tJ7Ww2W9C0yWSSz+cLRUkAAJxy6OuB6MU98gBOyKZNm+pM9+7dW5LUu3dvbdu2TcXFxYHl77//vsxms3r27Km4uDhlZ2dr/fr1rVozAABoOvp6IHJxRh5AvcrLy5WTkxM0z2q1KiUlRZK0Zs0aDR48WGeddZaeeuopbd68WX/9618lSZMnT9aiRYs0depU3XHHHTp48KBuvPFGXXvttUpLS5Mk3XHHHbruuuuUmpqqCy+8UIWFhXr//fd14403tu4HBQDgFEVfD0QvgjyAeq1bt04ZGRlB83r27KkdO3ZI8o8y+8wzz+hXv/qVMjIy9Pe//119+vSRJLndbr322muaPXu2hgwZIrfbrcsvv1wPPvhgYFtTp05VWVmZfv/73+vmm29WSkqKrrjiitb7gAAAnOLo64HoZTIMwwh3EQCii8lk0osvvqgJEyaEuxQAABAC9PVAZOMeeQAAAAAAoghBHgAAAACAKMKl9QAAAAAARBHOyAMAAAAAEEUI8gAAAAAARBGCPAAAAAAAUYQgDwAAAABAFCHIAwAAAAAQRQjyAAAAAABEEYI8AAAAAABRhCAPAAAAAEAU+f/JqBsoHiWbdAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(resampled_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1PuH3A2vnwrh"
   },
   "source": [
    "### Re-train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KFLxRL8eoDE5"
   },
   "source": [
    "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n",
    "\n",
    "So break up the epochs to give the `tf.keras.callbacks.EarlyStopping` finer control over when to stop training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:22:46.181758Z",
     "iopub.status.busy": "2024-01-17T02:22:46.181519Z",
     "iopub.status.idle": "2024-01-17T02:23:03.568742Z",
     "shell.execute_reply": "2024-01-17T02:23:03.567996Z"
    },
    "id": "e_yn9I26qAHU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 28s - loss: 0.7586 - cross entropy: 0.0615 - Brier score: 0.0185 - tp: 925.0000 - fp: 1185.0000 - tn: 45358.0000 - fn: 149.0000 - accuracy: 0.9720 - precision: 0.4384 - recall: 0.8613 - auc: 0.9903 - prc: 0.7954"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 6/20 [========>.....................] - ETA: 0s - loss: 0.7205 - cross entropy: 0.1768 - Brier score: 0.0597 - tp: 5494.0000 - fp: 4759.0000 - tn: 46808.0000 - fn: 796.0000 - accuracy: 0.9040 - precision: 0.5358 - recall: 0.8734 - auc: 0.9741 - prc: 0.8549"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 9/20 [============>.................] - ETA: 0s - loss: 0.7088 - cross entropy: 0.2256 - Brier score: 0.0772 - tp: 8302.0000 - fp: 6836.0000 - tn: 47697.0000 - fn: 1166.0000 - accuracy: 0.8750 - precision: 0.5484 - recall: 0.8768 - auc: 0.9660 - prc: 0.8634"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "12/20 [=================>............] - ETA: 0s - loss: 0.7014 - cross entropy: 0.2654 - Brier score: 0.0912 - tp: 11069.0000 - fp: 8952.0000 - tn: 48641.0000 - fn: 1483.0000 - accuracy: 0.8512 - precision: 0.5529 - recall: 0.8819 - auc: 0.9597 - prc: 0.8695"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "15/20 [=====================>........] - ETA: 0s - loss: 0.6949 - cross entropy: 0.2978 - Brier score: 0.1028 - tp: 13848.0000 - fp: 11041.0000 - tn: 49593.0000 - fn: 1807.0000 - accuracy: 0.8316 - precision: 0.5564 - recall: 0.8846 - auc: 0.9540 - prc: 0.8741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "18/20 [==========================>...] - ETA: 0s - loss: 0.6875 - cross entropy: 0.3241 - Brier score: 0.1123 - tp: 16616.0000 - fp: 13109.0000 - tn: 50589.0000 - fn: 2119.0000 - accuracy: 0.8153 - precision: 0.5590 - recall: 0.8869 - auc: 0.9491 - prc: 0.8774"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 2s 47ms/step - loss: 0.6826 - cross entropy: 0.3390 - Brier score: 0.1176 - tp: 18430.0000 - fp: 14493.0000 - tn: 51299.0000 - fn: 2307.0000 - accuracy: 0.8058 - precision: 0.5598 - recall: 0.8887 - auc: 0.9464 - prc: 0.8794 - val_loss: 1.0004 - val_cross entropy: 1.0004 - val_Brier score: 0.3825 - val_tp: 79.0000 - val_fp: 36434.0000 - val_tn: 9053.0000 - val_fn: 3.0000 - val_accuracy: 0.2004 - val_precision: 0.0022 - val_recall: 0.9634 - val_auc: 0.9261 - val_prc: 0.5749\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.6128 - cross entropy: 0.6128 - Brier score: 0.2166 - tp: 932.0000 - fp: 649.0000 - tn: 366.0000 - fn: 101.0000 - accuracy: 0.6338 - precision: 0.5895 - recall: 0.9022 - auc: 0.8640 - prc: 0.9046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.6238 - cross entropy: 0.6238 - Brier score: 0.2204 - tp: 4636.0000 - fp: 3318.0000 - tn: 1790.0000 - fn: 496.0000 - accuracy: 0.6275 - precision: 0.5829 - recall: 0.9034 - auc: 0.8643 - prc: 0.9036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.6121 - cross entropy: 0.6121 - Brier score: 0.2165 - tp: 7455.0000 - fp: 5232.0000 - tn: 2909.0000 - fn: 788.0000 - accuracy: 0.6326 - precision: 0.5876 - recall: 0.9044 - auc: 0.8675 - prc: 0.9065"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.6076 - cross entropy: 0.6076 - Brier score: 0.2152 - tp: 10247.0000 - fp: 7162.0000 - tn: 4030.0000 - fn: 1089.0000 - accuracy: 0.6337 - precision: 0.5886 - recall: 0.9039 - auc: 0.8690 - prc: 0.9080"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.6016 - cross entropy: 0.6016 - Brier score: 0.2133 - tp: 12980.0000 - fp: 9064.0000 - tn: 5258.0000 - fn: 1370.0000 - accuracy: 0.6361 - precision: 0.5888 - recall: 0.9045 - auc: 0.8720 - prc: 0.9097"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.5947 - cross entropy: 0.5947 - Brier score: 0.2111 - tp: 15732.0000 - fp: 10890.0000 - tn: 6532.0000 - fn: 1662.0000 - accuracy: 0.6395 - precision: 0.5909 - recall: 0.9044 - auc: 0.8739 - prc: 0.9110"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.5885 - cross entropy: 0.5885 - Brier score: 0.2089 - tp: 18504.0000 - fp: 12626.0000 - tn: 7862.0000 - fn: 1968.0000 - accuracy: 0.6437 - precision: 0.5944 - recall: 0.9039 - auc: 0.8752 - prc: 0.9121"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.5885 - cross entropy: 0.5885 - Brier score: 0.2089 - tp: 18504.0000 - fp: 12626.0000 - tn: 7862.0000 - fn: 1968.0000 - accuracy: 0.6437 - precision: 0.5944 - recall: 0.9039 - auc: 0.8752 - prc: 0.9121 - val_loss: 0.8348 - val_cross entropy: 0.8348 - val_Brier score: 0.3117 - val_tp: 79.0000 - val_fp: 28884.0000 - val_tn: 16603.0000 - val_fn: 3.0000 - val_accuracy: 0.3661 - val_precision: 0.0027 - val_recall: 0.9634 - val_auc: 0.9395 - val_prc: 0.6709\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.5246 - cross entropy: 0.5246 - Brier score: 0.1866 - tp: 906.0000 - fp: 571.0000 - tn: 488.0000 - fn: 83.0000 - accuracy: 0.6807 - precision: 0.6134 - recall: 0.9161 - auc: 0.9045 - prc: 0.9290"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.5325 - cross entropy: 0.5325 - Brier score: 0.1885 - tp: 4596.0000 - fp: 2791.0000 - tn: 2394.0000 - fn: 459.0000 - accuracy: 0.6826 - precision: 0.6222 - recall: 0.9092 - auc: 0.8941 - prc: 0.9239"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.5312 - cross entropy: 0.5312 - Brier score: 0.1875 - tp: 7353.0000 - fp: 4428.0000 - tn: 3859.0000 - fn: 744.0000 - accuracy: 0.6843 - precision: 0.6241 - recall: 0.9081 - auc: 0.8950 - prc: 0.9239"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.5231 - cross entropy: 0.5231 - Brier score: 0.1848 - tp: 10128.0000 - fp: 5984.0000 - tn: 5402.0000 - fn: 1014.0000 - accuracy: 0.6894 - precision: 0.6286 - recall: 0.9090 - auc: 0.8971 - prc: 0.9259"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.5156 - cross entropy: 0.5156 - Brier score: 0.1822 - tp: 12908.0000 - fp: 7499.0000 - tn: 6945.0000 - fn: 1320.0000 - accuracy: 0.6924 - precision: 0.6325 - recall: 0.9072 - auc: 0.8981 - prc: 0.9273"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.5103 - cross entropy: 0.5103 - Brier score: 0.1804 - tp: 15667.0000 - fp: 8963.0000 - tn: 8551.0000 - fn: 1635.0000 - accuracy: 0.6956 - precision: 0.6361 - recall: 0.9055 - auc: 0.8983 - prc: 0.9276"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.5070 - cross entropy: 0.5070 - Brier score: 0.1790 - tp: 18418.0000 - fp: 10425.0000 - tn: 10193.0000 - fn: 1924.0000 - accuracy: 0.6985 - precision: 0.6386 - recall: 0.9054 - auc: 0.8991 - prc: 0.9280"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 1s 27ms/step - loss: 0.5070 - cross entropy: 0.5070 - Brier score: 0.1790 - tp: 18418.0000 - fp: 10425.0000 - tn: 10193.0000 - fn: 1924.0000 - accuracy: 0.6985 - precision: 0.6386 - recall: 0.9054 - auc: 0.8991 - prc: 0.9280 - val_loss: 0.6975 - val_cross entropy: 0.6975 - val_Brier score: 0.2495 - val_tp: 78.0000 - val_fp: 19535.0000 - val_tn: 25952.0000 - val_fn: 4.0000 - val_accuracy: 0.5712 - val_precision: 0.0040 - val_recall: 0.9512 - val_auc: 0.9499 - val_prc: 0.7048\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.4699 - cross entropy: 0.4699 - Brier score: 0.1640 - tp: 906.0000 - fp: 438.0000 - tn: 609.0000 - fn: 95.0000 - accuracy: 0.7397 - precision: 0.6741 - recall: 0.9051 - auc: 0.9091 - prc: 0.9322"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.4656 - cross entropy: 0.4656 - Brier score: 0.1626 - tp: 4636.0000 - fp: 2254.0000 - tn: 2873.0000 - fn: 477.0000 - accuracy: 0.7333 - precision: 0.6729 - recall: 0.9067 - auc: 0.9108 - prc: 0.9361"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.4578 - cross entropy: 0.4578 - Brier score: 0.1596 - tp: 7376.0000 - fp: 3512.0000 - tn: 4708.0000 - fn: 788.0000 - accuracy: 0.7375 - precision: 0.6774 - recall: 0.9035 - auc: 0.9124 - prc: 0.9370"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.4541 - cross entropy: 0.4541 - Brier score: 0.1580 - tp: 10164.0000 - fp: 4769.0000 - tn: 6524.0000 - fn: 1071.0000 - accuracy: 0.7408 - precision: 0.6806 - recall: 0.9047 - auc: 0.9137 - prc: 0.9379"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.4493 - cross entropy: 0.4493 - Brier score: 0.1561 - tp: 12951.0000 - fp: 5954.0000 - tn: 8422.0000 - fn: 1345.0000 - accuracy: 0.7454 - precision: 0.6851 - recall: 0.9059 - auc: 0.9155 - prc: 0.9390"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.4474 - cross entropy: 0.4474 - Brier score: 0.1554 - tp: 15685.0000 - fp: 7181.0000 - tn: 10320.0000 - fn: 1630.0000 - accuracy: 0.7469 - precision: 0.6860 - recall: 0.9059 - auc: 0.9159 - prc: 0.9392"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.4413 - cross entropy: 0.4413 - Brier score: 0.1530 - tp: 18483.0000 - fp: 8228.0000 - tn: 12349.0000 - fn: 1900.0000 - accuracy: 0.7527 - precision: 0.6920 - recall: 0.9068 - auc: 0.9179 - prc: 0.9406"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.4413 - cross entropy: 0.4413 - Brier score: 0.1530 - tp: 18483.0000 - fp: 8228.0000 - tn: 12349.0000 - fn: 1900.0000 - accuracy: 0.7527 - precision: 0.6920 - recall: 0.9068 - auc: 0.9179 - prc: 0.9406 - val_loss: 0.5893 - val_cross entropy: 0.5893 - val_Brier score: 0.1998 - val_tp: 77.0000 - val_fp: 11782.0000 - val_tn: 33705.0000 - val_fn: 5.0000 - val_accuracy: 0.7413 - val_precision: 0.0065 - val_recall: 0.9390 - val_auc: 0.9552 - val_prc: 0.7246\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.4369 - cross entropy: 0.4369 - Brier score: 0.1502 - tp: 904.0000 - fp: 388.0000 - tn: 655.0000 - fn: 101.0000 - accuracy: 0.7612 - precision: 0.6997 - recall: 0.8995 - auc: 0.9153 - prc: 0.9360"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.4129 - cross entropy: 0.4129 - Brier score: 0.1424 - tp: 4635.0000 - fp: 1829.0000 - tn: 3300.0000 - fn: 476.0000 - accuracy: 0.7749 - precision: 0.7170 - recall: 0.9069 - auc: 0.9237 - prc: 0.9453"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.4054 - cross entropy: 0.4054 - Brier score: 0.1393 - tp: 7435.0000 - fp: 2818.0000 - tn: 5358.0000 - fn: 773.0000 - accuracy: 0.7808 - precision: 0.7252 - recall: 0.9058 - auc: 0.9257 - prc: 0.9469"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.4003 - cross entropy: 0.4003 - Brier score: 0.1373 - tp: 10222.0000 - fp: 3798.0000 - tn: 7469.0000 - fn: 1039.0000 - accuracy: 0.7853 - precision: 0.7291 - recall: 0.9077 - auc: 0.9286 - prc: 0.9487"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.3972 - cross entropy: 0.3972 - Brier score: 0.1358 - tp: 13023.0000 - fp: 4750.0000 - tn: 9584.0000 - fn: 1315.0000 - accuracy: 0.7885 - precision: 0.7327 - recall: 0.9083 - auc: 0.9295 - prc: 0.9492"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.3952 - cross entropy: 0.3952 - Brier score: 0.1350 - tp: 15786.0000 - fp: 5679.0000 - tn: 11737.0000 - fn: 1614.0000 - accuracy: 0.7905 - precision: 0.7354 - recall: 0.9072 - auc: 0.9297 - prc: 0.9493"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.3914 - cross entropy: 0.3914 - Brier score: 0.1335 - tp: 18615.0000 - fp: 6548.0000 - tn: 13896.0000 - fn: 1901.0000 - accuracy: 0.7937 - precision: 0.7398 - recall: 0.9073 - auc: 0.9304 - prc: 0.9500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.3914 - cross entropy: 0.3914 - Brier score: 0.1335 - tp: 18615.0000 - fp: 6548.0000 - tn: 13896.0000 - fn: 1901.0000 - accuracy: 0.7937 - precision: 0.7398 - recall: 0.9073 - auc: 0.9304 - prc: 0.9500 - val_loss: 0.5045 - val_cross entropy: 0.5045 - val_Brier score: 0.1613 - val_tp: 77.0000 - val_fp: 7135.0000 - val_tn: 38352.0000 - val_fn: 5.0000 - val_accuracy: 0.8433 - val_precision: 0.0107 - val_recall: 0.9390 - val_auc: 0.9595 - val_prc: 0.7424\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.3905 - cross entropy: 0.3905 - Brier score: 0.1313 - tp: 888.0000 - fp: 310.0000 - tn: 752.0000 - fn: 98.0000 - accuracy: 0.8008 - precision: 0.7412 - recall: 0.9006 - auc: 0.9300 - prc: 0.9456"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.3700 - cross entropy: 0.3700 - Brier score: 0.1242 - tp: 4549.0000 - fp: 1371.0000 - tn: 3820.0000 - fn: 500.0000 - accuracy: 0.8173 - precision: 0.7684 - recall: 0.9010 - auc: 0.9351 - prc: 0.9523"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.3683 - cross entropy: 0.3683 - Brier score: 0.1233 - tp: 7302.0000 - fp: 2156.0000 - tn: 6116.0000 - fn: 810.0000 - accuracy: 0.8190 - precision: 0.7720 - recall: 0.9001 - auc: 0.9354 - prc: 0.9524"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.3638 - cross entropy: 0.3638 - Brier score: 0.1214 - tp: 10094.0000 - fp: 2888.0000 - tn: 8442.0000 - fn: 1104.0000 - accuracy: 0.8228 - precision: 0.7775 - recall: 0.9014 - auc: 0.9366 - prc: 0.9534"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.3600 - cross entropy: 0.3600 - Brier score: 0.1197 - tp: 12885.0000 - fp: 3573.0000 - tn: 10820.0000 - fn: 1394.0000 - accuracy: 0.8268 - precision: 0.7829 - recall: 0.9024 - auc: 0.9378 - prc: 0.9544"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.3584 - cross entropy: 0.3584 - Brier score: 0.1191 - tp: 15675.0000 - fp: 4318.0000 - tn: 13145.0000 - fn: 1678.0000 - accuracy: 0.8278 - precision: 0.7840 - recall: 0.9033 - auc: 0.9382 - prc: 0.9547"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.3563 - cross entropy: 0.3563 - Brier score: 0.1183 - tp: 18429.0000 - fp: 5050.0000 - tn: 15533.0000 - fn: 1948.0000 - accuracy: 0.8292 - precision: 0.7849 - recall: 0.9044 - auc: 0.9391 - prc: 0.9552"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 26ms/step - loss: 0.3563 - cross entropy: 0.3563 - Brier score: 0.1183 - tp: 18429.0000 - fp: 5050.0000 - tn: 15533.0000 - fn: 1948.0000 - accuracy: 0.8292 - precision: 0.7849 - recall: 0.9044 - auc: 0.9391 - prc: 0.9552 - val_loss: 0.4395 - val_cross entropy: 0.4395 - val_Brier score: 0.1328 - val_tp: 77.0000 - val_fp: 4727.0000 - val_tn: 40760.0000 - val_fn: 5.0000 - val_accuracy: 0.8962 - val_precision: 0.0160 - val_recall: 0.9390 - val_auc: 0.9616 - val_prc: 0.7625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.3345 - cross entropy: 0.3345 - Brier score: 0.1103 - tp: 951.0000 - fp: 232.0000 - tn: 782.0000 - fn: 83.0000 - accuracy: 0.8462 - precision: 0.8039 - recall: 0.9197 - auc: 0.9474 - prc: 0.9627"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.3381 - cross entropy: 0.3381 - Brier score: 0.1108 - tp: 4716.0000 - fp: 1140.0000 - tn: 3932.0000 - fn: 452.0000 - accuracy: 0.8445 - precision: 0.8053 - recall: 0.9125 - auc: 0.9451 - prc: 0.9605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.3337 - cross entropy: 0.3337 - Brier score: 0.1090 - tp: 7548.0000 - fp: 1741.0000 - tn: 6356.0000 - fn: 739.0000 - accuracy: 0.8486 - precision: 0.8126 - recall: 0.9108 - auc: 0.9456 - prc: 0.9611"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.3315 - cross entropy: 0.3315 - Brier score: 0.1082 - tp: 10338.0000 - fp: 2384.0000 - tn: 8790.0000 - fn: 1016.0000 - accuracy: 0.8491 - precision: 0.8126 - recall: 0.9105 - auc: 0.9461 - prc: 0.9612"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.3271 - cross entropy: 0.3271 - Brier score: 0.1065 - tp: 13146.0000 - fp: 2958.0000 - tn: 11273.0000 - fn: 1295.0000 - accuracy: 0.8517 - precision: 0.8163 - recall: 0.9103 - auc: 0.9474 - prc: 0.9621"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.3246 - cross entropy: 0.3246 - Brier score: 0.1057 - tp: 15976.0000 - fp: 3537.0000 - tn: 13733.0000 - fn: 1570.0000 - accuracy: 0.8533 - precision: 0.8187 - recall: 0.9105 - auc: 0.9481 - prc: 0.9626"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.3220 - cross entropy: 0.3220 - Brier score: 0.1047 - tp: 18807.0000 - fp: 4065.0000 - tn: 16241.0000 - fn: 1847.0000 - accuracy: 0.8557 - precision: 0.8223 - recall: 0.9106 - auc: 0.9485 - prc: 0.9631"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.3220 - cross entropy: 0.3220 - Brier score: 0.1047 - tp: 18807.0000 - fp: 4065.0000 - tn: 16241.0000 - fn: 1847.0000 - accuracy: 0.8557 - precision: 0.8223 - recall: 0.9106 - auc: 0.9485 - prc: 0.9631 - val_loss: 0.3867 - val_cross entropy: 0.3867 - val_Brier score: 0.1105 - val_tp: 77.0000 - val_fp: 3192.0000 - val_tn: 42295.0000 - val_fn: 5.0000 - val_accuracy: 0.9298 - val_precision: 0.0236 - val_recall: 0.9390 - val_auc: 0.9635 - val_prc: 0.7711\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.3170 - cross entropy: 0.3170 - Brier score: 0.1032 - tp: 952.0000 - fp: 185.0000 - tn: 814.0000 - fn: 97.0000 - accuracy: 0.8623 - precision: 0.8373 - recall: 0.9075 - auc: 0.9477 - prc: 0.9643"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.3135 - cross entropy: 0.3135 - Brier score: 0.1014 - tp: 4605.0000 - fp: 934.0000 - tn: 4221.0000 - fn: 480.0000 - accuracy: 0.8619 - precision: 0.8314 - recall: 0.9056 - auc: 0.9493 - prc: 0.9629"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.3077 - cross entropy: 0.3077 - Brier score: 0.0990 - tp: 7427.0000 - fp: 1455.0000 - tn: 6758.0000 - fn: 744.0000 - accuracy: 0.8658 - precision: 0.8362 - recall: 0.9089 - auc: 0.9521 - prc: 0.9649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.3056 - cross entropy: 0.3056 - Brier score: 0.0980 - tp: 10267.0000 - fp: 1970.0000 - tn: 9292.0000 - fn: 999.0000 - accuracy: 0.8682 - precision: 0.8390 - recall: 0.9113 - auc: 0.9531 - prc: 0.9655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.3053 - cross entropy: 0.3053 - Brier score: 0.0976 - tp: 13066.0000 - fp: 2471.0000 - tn: 11857.0000 - fn: 1278.0000 - accuracy: 0.8692 - precision: 0.8410 - recall: 0.9109 - auc: 0.9532 - prc: 0.9655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.3040 - cross entropy: 0.3040 - Brier score: 0.0970 - tp: 15844.0000 - fp: 2933.0000 - tn: 14486.0000 - fn: 1553.0000 - accuracy: 0.8712 - precision: 0.8438 - recall: 0.9107 - auc: 0.9534 - prc: 0.9655"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.3012 - cross entropy: 0.3012 - Brier score: 0.0959 - tp: 18607.0000 - fp: 3384.0000 - tn: 17165.0000 - fn: 1804.0000 - accuracy: 0.8733 - precision: 0.8461 - recall: 0.9116 - auc: 0.9545 - prc: 0.9661"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.3012 - cross entropy: 0.3012 - Brier score: 0.0959 - tp: 18607.0000 - fp: 3384.0000 - tn: 17165.0000 - fn: 1804.0000 - accuracy: 0.8733 - precision: 0.8461 - recall: 0.9116 - auc: 0.9545 - prc: 0.9661 - val_loss: 0.3438 - val_cross entropy: 0.3438 - val_Brier score: 0.0932 - val_tp: 77.0000 - val_fp: 2361.0000 - val_tn: 43126.0000 - val_fn: 5.0000 - val_accuracy: 0.9481 - val_precision: 0.0316 - val_recall: 0.9390 - val_auc: 0.9644 - val_prc: 0.7748\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2832 - cross entropy: 0.2832 - Brier score: 0.0901 - tp: 985.0000 - fp: 161.0000 - tn: 810.0000 - fn: 92.0000 - accuracy: 0.8765 - precision: 0.8595 - recall: 0.9146 - auc: 0.9567 - prc: 0.9708"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2837 - cross entropy: 0.2837 - Brier score: 0.0892 - tp: 4717.0000 - fp: 742.0000 - tn: 4338.0000 - fn: 443.0000 - accuracy: 0.8843 - precision: 0.8641 - recall: 0.9141 - auc: 0.9584 - prc: 0.9692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2852 - cross entropy: 0.2852 - Brier score: 0.0896 - tp: 7534.0000 - fp: 1210.0000 - tn: 6942.0000 - fn: 698.0000 - accuracy: 0.8835 - precision: 0.8616 - recall: 0.9152 - auc: 0.9581 - prc: 0.9690"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2840 - cross entropy: 0.2840 - Brier score: 0.0894 - tp: 10342.0000 - fp: 1648.0000 - tn: 9549.0000 - fn: 989.0000 - accuracy: 0.8829 - precision: 0.8626 - recall: 0.9127 - auc: 0.9579 - prc: 0.9688"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2819 - cross entropy: 0.2819 - Brier score: 0.0889 - tp: 13142.0000 - fp: 2082.0000 - tn: 12170.0000 - fn: 1278.0000 - accuracy: 0.8828 - precision: 0.8632 - recall: 0.9114 - auc: 0.9581 - prc: 0.9690"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2806 - cross entropy: 0.2806 - Brier score: 0.0883 - tp: 15864.0000 - fp: 2477.0000 - tn: 14931.0000 - fn: 1544.0000 - accuracy: 0.8845 - precision: 0.8649 - recall: 0.9113 - auc: 0.9588 - prc: 0.9692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2795 - cross entropy: 0.2795 - Brier score: 0.0880 - tp: 18636.0000 - fp: 2891.0000 - tn: 17616.0000 - fn: 1817.0000 - accuracy: 0.8851 - precision: 0.8657 - recall: 0.9112 - auc: 0.9589 - prc: 0.9692"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2795 - cross entropy: 0.2795 - Brier score: 0.0880 - tp: 18636.0000 - fp: 2891.0000 - tn: 17616.0000 - fn: 1817.0000 - accuracy: 0.8851 - precision: 0.8657 - recall: 0.9112 - auc: 0.9589 - prc: 0.9692 - val_loss: 0.3087 - val_cross entropy: 0.3087 - val_Brier score: 0.0799 - val_tp: 76.0000 - val_fp: 1892.0000 - val_tn: 43595.0000 - val_fn: 6.0000 - val_accuracy: 0.9583 - val_precision: 0.0386 - val_recall: 0.9268 - val_auc: 0.9658 - val_prc: 0.7797\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2733 - cross entropy: 0.2733 - Brier score: 0.0859 - tp: 923.0000 - fp: 141.0000 - tn: 896.0000 - fn: 88.0000 - accuracy: 0.8882 - precision: 0.8675 - recall: 0.9130 - auc: 0.9612 - prc: 0.9711"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2667 - cross entropy: 0.2667 - Brier score: 0.0830 - tp: 4705.0000 - fp: 642.0000 - tn: 4441.0000 - fn: 452.0000 - accuracy: 0.8932 - precision: 0.8799 - recall: 0.9124 - auc: 0.9617 - prc: 0.9717"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2664 - cross entropy: 0.2664 - Brier score: 0.0829 - tp: 7464.0000 - fp: 1013.0000 - tn: 7174.0000 - fn: 733.0000 - accuracy: 0.8934 - precision: 0.8805 - recall: 0.9106 - auc: 0.9614 - prc: 0.9713"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2650 - cross entropy: 0.2650 - Brier score: 0.0825 - tp: 10305.0000 - fp: 1400.0000 - tn: 9811.0000 - fn: 1012.0000 - accuracy: 0.8929 - precision: 0.8804 - recall: 0.9106 - auc: 0.9618 - prc: 0.9719"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2643 - cross entropy: 0.2643 - Brier score: 0.0822 - tp: 13135.0000 - fp: 1756.0000 - tn: 12495.0000 - fn: 1286.0000 - accuracy: 0.8939 - precision: 0.8821 - recall: 0.9108 - auc: 0.9620 - prc: 0.9720"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2626 - cross entropy: 0.2626 - Brier score: 0.0815 - tp: 15955.0000 - fp: 2100.0000 - tn: 15203.0000 - fn: 1558.0000 - accuracy: 0.8949 - precision: 0.8837 - recall: 0.9110 - auc: 0.9624 - prc: 0.9724"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2620 - cross entropy: 0.2620 - Brier score: 0.0812 - tp: 18743.0000 - fp: 2432.0000 - tn: 17955.0000 - fn: 1830.0000 - accuracy: 0.8959 - precision: 0.8851 - recall: 0.9110 - auc: 0.9625 - prc: 0.9724"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2620 - cross entropy: 0.2620 - Brier score: 0.0812 - tp: 18743.0000 - fp: 2432.0000 - tn: 17955.0000 - fn: 1830.0000 - accuracy: 0.8959 - precision: 0.8851 - recall: 0.9110 - auc: 0.9625 - prc: 0.9724 - val_loss: 0.2798 - val_cross entropy: 0.2798 - val_Brier score: 0.0695 - val_tp: 76.0000 - val_fp: 1615.0000 - val_tn: 43872.0000 - val_fn: 6.0000 - val_accuracy: 0.9644 - val_precision: 0.0449 - val_recall: 0.9268 - val_auc: 0.9674 - val_prc: 0.7834\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2736 - cross entropy: 0.2736 - Brier score: 0.0842 - tp: 885.0000 - fp: 133.0000 - tn: 939.0000 - fn: 91.0000 - accuracy: 0.8906 - precision: 0.8694 - recall: 0.9068 - auc: 0.9611 - prc: 0.9675"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2560 - cross entropy: 0.2560 - Brier score: 0.0786 - tp: 4601.0000 - fp: 574.0000 - tn: 4612.0000 - fn: 453.0000 - accuracy: 0.8997 - precision: 0.8891 - recall: 0.9104 - auc: 0.9649 - prc: 0.9730"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2519 - cross entropy: 0.2519 - Brier score: 0.0773 - tp: 7440.0000 - fp: 903.0000 - tn: 7329.0000 - fn: 712.0000 - accuracy: 0.9014 - precision: 0.8918 - recall: 0.9127 - auc: 0.9662 - prc: 0.9743"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2514 - cross entropy: 0.2514 - Brier score: 0.0770 - tp: 10151.0000 - fp: 1235.0000 - tn: 10160.0000 - fn: 982.0000 - accuracy: 0.9016 - precision: 0.8915 - recall: 0.9118 - auc: 0.9663 - prc: 0.9742"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2514 - cross entropy: 0.2514 - Brier score: 0.0769 - tp: 12983.0000 - fp: 1544.0000 - tn: 12887.0000 - fn: 1258.0000 - accuracy: 0.9023 - precision: 0.8937 - recall: 0.9117 - auc: 0.9660 - prc: 0.9741"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2504 - cross entropy: 0.2504 - Brier score: 0.0765 - tp: 15799.0000 - fp: 1860.0000 - tn: 15636.0000 - fn: 1521.0000 - accuracy: 0.9029 - precision: 0.8947 - recall: 0.9122 - auc: 0.9662 - prc: 0.9744"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2480 - cross entropy: 0.2480 - Brier score: 0.0757 - tp: 18645.0000 - fp: 2154.0000 - tn: 18383.0000 - fn: 1778.0000 - accuracy: 0.9040 - precision: 0.8964 - recall: 0.9129 - auc: 0.9668 - prc: 0.9748"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2480 - cross entropy: 0.2480 - Brier score: 0.0757 - tp: 18645.0000 - fp: 2154.0000 - tn: 18383.0000 - fn: 1778.0000 - accuracy: 0.9040 - precision: 0.8964 - recall: 0.9129 - auc: 0.9668 - prc: 0.9748 - val_loss: 0.2551 - val_cross entropy: 0.2551 - val_Brier score: 0.0611 - val_tp: 76.0000 - val_fp: 1428.0000 - val_tn: 44059.0000 - val_fn: 6.0000 - val_accuracy: 0.9685 - val_precision: 0.0505 - val_recall: 0.9268 - val_auc: 0.9691 - val_prc: 0.7858\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2437 - cross entropy: 0.2437 - Brier score: 0.0743 - tp: 936.0000 - fp: 96.0000 - tn: 928.0000 - fn: 88.0000 - accuracy: 0.9102 - precision: 0.9070 - recall: 0.9141 - auc: 0.9654 - prc: 0.9748"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2428 - cross entropy: 0.2428 - Brier score: 0.0738 - tp: 4637.0000 - fp: 522.0000 - tn: 4654.0000 - fn: 427.0000 - accuracy: 0.9073 - precision: 0.8988 - recall: 0.9157 - auc: 0.9679 - prc: 0.9752"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2414 - cross entropy: 0.2414 - Brier score: 0.0734 - tp: 7425.0000 - fp: 784.0000 - tn: 7467.0000 - fn: 708.0000 - accuracy: 0.9089 - precision: 0.9045 - recall: 0.9129 - auc: 0.9672 - prc: 0.9749"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2401 - cross entropy: 0.2401 - Brier score: 0.0731 - tp: 10207.0000 - fp: 1076.0000 - tn: 10267.0000 - fn: 978.0000 - accuracy: 0.9088 - precision: 0.9046 - recall: 0.9126 - auc: 0.9672 - prc: 0.9749"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2389 - cross entropy: 0.2389 - Brier score: 0.0728 - tp: 13027.0000 - fp: 1371.0000 - tn: 13040.0000 - fn: 1234.0000 - accuracy: 0.9091 - precision: 0.9048 - recall: 0.9135 - auc: 0.9678 - prc: 0.9754"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2375 - cross entropy: 0.2375 - Brier score: 0.0723 - tp: 15857.0000 - fp: 1663.0000 - tn: 15809.0000 - fn: 1487.0000 - accuracy: 0.9095 - precision: 0.9051 - recall: 0.9143 - auc: 0.9683 - prc: 0.9758"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2368 - cross entropy: 0.2368 - Brier score: 0.0722 - tp: 18706.0000 - fp: 1922.0000 - tn: 18565.0000 - fn: 1767.0000 - accuracy: 0.9099 - precision: 0.9068 - recall: 0.9137 - auc: 0.9682 - prc: 0.9759"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2368 - cross entropy: 0.2368 - Brier score: 0.0722 - tp: 18706.0000 - fp: 1922.0000 - tn: 18565.0000 - fn: 1767.0000 - accuracy: 0.9099 - precision: 0.9068 - recall: 0.9137 - auc: 0.9682 - prc: 0.9759 - val_loss: 0.2341 - val_cross entropy: 0.2341 - val_Brier score: 0.0543 - val_tp: 75.0000 - val_fp: 1301.0000 - val_tn: 44186.0000 - val_fn: 7.0000 - val_accuracy: 0.9713 - val_precision: 0.0545 - val_recall: 0.9146 - val_auc: 0.9710 - val_prc: 0.7888\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2240 - cross entropy: 0.2240 - Brier score: 0.0675 - tp: 947.0000 - fp: 81.0000 - tn: 933.0000 - fn: 87.0000 - accuracy: 0.9180 - precision: 0.9212 - recall: 0.9159 - auc: 0.9721 - prc: 0.9792"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2247 - cross entropy: 0.2247 - Brier score: 0.0675 - tp: 4736.0000 - fp: 430.0000 - tn: 4654.0000 - fn: 420.0000 - accuracy: 0.9170 - precision: 0.9168 - recall: 0.9185 - auc: 0.9721 - prc: 0.9785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2241 - cross entropy: 0.2241 - Brier score: 0.0675 - tp: 7514.0000 - fp: 718.0000 - tn: 7483.0000 - fn: 669.0000 - accuracy: 0.9153 - precision: 0.9128 - recall: 0.9182 - auc: 0.9721 - prc: 0.9782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2246 - cross entropy: 0.2246 - Brier score: 0.0677 - tp: 10377.0000 - fp: 992.0000 - tn: 10227.0000 - fn: 932.0000 - accuracy: 0.9146 - precision: 0.9127 - recall: 0.9176 - auc: 0.9719 - prc: 0.9782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2244 - cross entropy: 0.2244 - Brier score: 0.0674 - tp: 13200.0000 - fp: 1220.0000 - tn: 13052.0000 - fn: 1200.0000 - accuracy: 0.9156 - precision: 0.9154 - recall: 0.9167 - auc: 0.9718 - prc: 0.9782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2237 - cross entropy: 0.2237 - Brier score: 0.0672 - tp: 16025.0000 - fp: 1463.0000 - tn: 15861.0000 - fn: 1467.0000 - accuracy: 0.9158 - precision: 0.9163 - recall: 0.9161 - auc: 0.9718 - prc: 0.9782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2223 - cross entropy: 0.2223 - Brier score: 0.0667 - tp: 18874.0000 - fp: 1694.0000 - tn: 18675.0000 - fn: 1717.0000 - accuracy: 0.9167 - precision: 0.9176 - recall: 0.9166 - auc: 0.9720 - prc: 0.9785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2223 - cross entropy: 0.2223 - Brier score: 0.0667 - tp: 18874.0000 - fp: 1694.0000 - tn: 18675.0000 - fn: 1717.0000 - accuracy: 0.9167 - precision: 0.9176 - recall: 0.9166 - auc: 0.9720 - prc: 0.9785 - val_loss: 0.2162 - val_cross entropy: 0.2162 - val_Brier score: 0.0488 - val_tp: 75.0000 - val_fp: 1235.0000 - val_tn: 44252.0000 - val_fn: 7.0000 - val_accuracy: 0.9727 - val_precision: 0.0573 - val_recall: 0.9146 - val_auc: 0.9732 - val_prc: 0.7912\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2184 - cross entropy: 0.2184 - Brier score: 0.0651 - tp: 945.0000 - fp: 86.0000 - tn: 924.0000 - fn: 93.0000 - accuracy: 0.9126 - precision: 0.9166 - recall: 0.9104 - auc: 0.9728 - prc: 0.9787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2202 - cross entropy: 0.2202 - Brier score: 0.0656 - tp: 4675.0000 - fp: 421.0000 - tn: 4703.0000 - fn: 441.0000 - accuracy: 0.9158 - precision: 0.9174 - recall: 0.9138 - auc: 0.9727 - prc: 0.9785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2219 - cross entropy: 0.2219 - Brier score: 0.0662 - tp: 7469.0000 - fp: 689.0000 - tn: 7516.0000 - fn: 710.0000 - accuracy: 0.9146 - precision: 0.9155 - recall: 0.9132 - auc: 0.9722 - prc: 0.9782"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2191 - cross entropy: 0.2191 - Brier score: 0.0654 - tp: 10312.0000 - fp: 924.0000 - tn: 10321.0000 - fn: 971.0000 - accuracy: 0.9159 - precision: 0.9178 - recall: 0.9139 - auc: 0.9726 - prc: 0.9787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2198 - cross entropy: 0.2198 - Brier score: 0.0655 - tp: 13098.0000 - fp: 1182.0000 - tn: 13161.0000 - fn: 1231.0000 - accuracy: 0.9158 - precision: 0.9172 - recall: 0.9141 - auc: 0.9724 - prc: 0.9785"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2186 - cross entropy: 0.2186 - Brier score: 0.0652 - tp: 15884.0000 - fp: 1425.0000 - tn: 16024.0000 - fn: 1483.0000 - accuracy: 0.9165 - precision: 0.9177 - recall: 0.9146 - auc: 0.9728 - prc: 0.9787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2172 - cross entropy: 0.2172 - Brier score: 0.0648 - tp: 18681.0000 - fp: 1627.0000 - tn: 18898.0000 - fn: 1754.0000 - accuracy: 0.9175 - precision: 0.9199 - recall: 0.9142 - auc: 0.9732 - prc: 0.9789"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 1s 27ms/step - loss: 0.2172 - cross entropy: 0.2172 - Brier score: 0.0648 - tp: 18681.0000 - fp: 1627.0000 - tn: 18898.0000 - fn: 1754.0000 - accuracy: 0.9175 - precision: 0.9199 - recall: 0.9142 - auc: 0.9732 - prc: 0.9789 - val_loss: 0.2011 - val_cross entropy: 0.2011 - val_Brier score: 0.0444 - val_tp: 75.0000 - val_fp: 1167.0000 - val_tn: 44320.0000 - val_fn: 7.0000 - val_accuracy: 0.9742 - val_precision: 0.0604 - val_recall: 0.9146 - val_auc: 0.9748 - val_prc: 0.7927\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.2164 - cross entropy: 0.2164 - Brier score: 0.0648 - tp: 915.0000 - fp: 68.0000 - tn: 973.0000 - fn: 92.0000 - accuracy: 0.9219 - precision: 0.9308 - recall: 0.9086 - auc: 0.9708 - prc: 0.9775"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2111 - cross entropy: 0.2111 - Brier score: 0.0627 - tp: 4768.0000 - fp: 386.0000 - tn: 4670.0000 - fn: 416.0000 - accuracy: 0.9217 - precision: 0.9251 - recall: 0.9198 - auc: 0.9745 - prc: 0.9802"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2111 - cross entropy: 0.2111 - Brier score: 0.0628 - tp: 7589.0000 - fp: 608.0000 - tn: 7506.0000 - fn: 681.0000 - accuracy: 0.9213 - precision: 0.9258 - recall: 0.9177 - auc: 0.9742 - prc: 0.9801"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2093 - cross entropy: 0.2093 - Brier score: 0.0622 - tp: 10425.0000 - fp: 810.0000 - tn: 10362.0000 - fn: 931.0000 - accuracy: 0.9227 - precision: 0.9279 - recall: 0.9180 - auc: 0.9749 - prc: 0.9806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2099 - cross entropy: 0.2099 - Brier score: 0.0622 - tp: 13226.0000 - fp: 1034.0000 - tn: 13236.0000 - fn: 1176.0000 - accuracy: 0.9229 - precision: 0.9275 - recall: 0.9183 - auc: 0.9747 - prc: 0.9804"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2094 - cross entropy: 0.2094 - Brier score: 0.0620 - tp: 16041.0000 - fp: 1272.0000 - tn: 16093.0000 - fn: 1410.0000 - accuracy: 0.9230 - precision: 0.9265 - recall: 0.9192 - auc: 0.9749 - prc: 0.9805"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2088 - cross entropy: 0.2088 - Brier score: 0.0619 - tp: 18878.0000 - fp: 1484.0000 - tn: 18949.0000 - fn: 1649.0000 - accuracy: 0.9235 - precision: 0.9271 - recall: 0.9197 - auc: 0.9749 - prc: 0.9806"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2088 - cross entropy: 0.2088 - Brier score: 0.0619 - tp: 18878.0000 - fp: 1484.0000 - tn: 18949.0000 - fn: 1649.0000 - accuracy: 0.9235 - precision: 0.9271 - recall: 0.9197 - auc: 0.9749 - prc: 0.9806 - val_loss: 0.1872 - val_cross entropy: 0.1872 - val_Brier score: 0.0405 - val_tp: 75.0000 - val_fp: 1100.0000 - val_tn: 44387.0000 - val_fn: 7.0000 - val_accuracy: 0.9757 - val_precision: 0.0638 - val_recall: 0.9146 - val_auc: 0.9760 - val_prc: 0.7931\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1957 - cross entropy: 0.1957 - Brier score: 0.0583 - tp: 967.0000 - fp: 68.0000 - tn: 925.0000 - fn: 88.0000 - accuracy: 0.9238 - precision: 0.9343 - recall: 0.9166 - auc: 0.9777 - prc: 0.9834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.2061 - cross entropy: 0.2061 - Brier score: 0.0613 - tp: 4662.0000 - fp: 391.0000 - tn: 4770.0000 - fn: 417.0000 - accuracy: 0.9211 - precision: 0.9226 - recall: 0.9179 - auc: 0.9766 - prc: 0.9811"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.2061 - cross entropy: 0.2061 - Brier score: 0.0612 - tp: 7489.0000 - fp: 618.0000 - tn: 7600.0000 - fn: 677.0000 - accuracy: 0.9210 - precision: 0.9238 - recall: 0.9171 - auc: 0.9760 - prc: 0.9810"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.2042 - cross entropy: 0.2042 - Brier score: 0.0606 - tp: 10350.0000 - fp: 824.0000 - tn: 10449.0000 - fn: 905.0000 - accuracy: 0.9233 - precision: 0.9263 - recall: 0.9196 - auc: 0.9766 - prc: 0.9816"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.2026 - cross entropy: 0.2026 - Brier score: 0.0599 - tp: 13135.0000 - fp: 1039.0000 - tn: 13371.0000 - fn: 1127.0000 - accuracy: 0.9245 - precision: 0.9267 - recall: 0.9210 - auc: 0.9770 - prc: 0.9818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.2015 - cross entropy: 0.2015 - Brier score: 0.0596 - tp: 15938.0000 - fp: 1236.0000 - tn: 16245.0000 - fn: 1397.0000 - accuracy: 0.9244 - precision: 0.9280 - recall: 0.9194 - auc: 0.9769 - prc: 0.9818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.2011 - cross entropy: 0.2011 - Brier score: 0.0596 - tp: 18797.0000 - fp: 1439.0000 - tn: 19068.0000 - fn: 1656.0000 - accuracy: 0.9244 - precision: 0.9289 - recall: 0.9190 - auc: 0.9768 - prc: 0.9818"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.2011 - cross entropy: 0.2011 - Brier score: 0.0596 - tp: 18797.0000 - fp: 1439.0000 - tn: 19068.0000 - fn: 1656.0000 - accuracy: 0.9244 - precision: 0.9289 - recall: 0.9190 - auc: 0.9768 - prc: 0.9818 - val_loss: 0.1743 - val_cross entropy: 0.1743 - val_Brier score: 0.0369 - val_tp: 75.0000 - val_fp: 1029.0000 - val_tn: 44458.0000 - val_fn: 7.0000 - val_accuracy: 0.9773 - val_precision: 0.0679 - val_recall: 0.9146 - val_auc: 0.9769 - val_prc: 0.7935\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 17/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1930 - cross entropy: 0.1930 - Brier score: 0.0566 - tp: 964.0000 - fp: 54.0000 - tn: 948.0000 - fn: 82.0000 - accuracy: 0.9336 - precision: 0.9470 - recall: 0.9216 - auc: 0.9779 - prc: 0.9832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1979 - cross entropy: 0.1979 - Brier score: 0.0587 - tp: 4693.0000 - fp: 349.0000 - tn: 4800.0000 - fn: 398.0000 - accuracy: 0.9271 - precision: 0.9308 - recall: 0.9218 - auc: 0.9769 - prc: 0.9820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1955 - cross entropy: 0.1955 - Brier score: 0.0577 - tp: 7518.0000 - fp: 541.0000 - tn: 7683.0000 - fn: 642.0000 - accuracy: 0.9278 - precision: 0.9329 - recall: 0.9213 - auc: 0.9775 - prc: 0.9826"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1963 - cross entropy: 0.1963 - Brier score: 0.0578 - tp: 10305.0000 - fp: 739.0000 - tn: 10603.0000 - fn: 881.0000 - accuracy: 0.9281 - precision: 0.9331 - recall: 0.9212 - auc: 0.9773 - prc: 0.9822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1964 - cross entropy: 0.1964 - Brier score: 0.0577 - tp: 13104.0000 - fp: 944.0000 - tn: 13497.0000 - fn: 1127.0000 - accuracy: 0.9278 - precision: 0.9328 - recall: 0.9208 - auc: 0.9773 - prc: 0.9822"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1964 - cross entropy: 0.1964 - Brier score: 0.0575 - tp: 15924.0000 - fp: 1131.0000 - tn: 16391.0000 - fn: 1370.0000 - accuracy: 0.9282 - precision: 0.9337 - recall: 0.9208 - auc: 0.9773 - prc: 0.9820"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1961 - cross entropy: 0.1961 - Brier score: 0.0575 - tp: 18762.0000 - fp: 1337.0000 - tn: 19238.0000 - fn: 1623.0000 - accuracy: 0.9277 - precision: 0.9335 - recall: 0.9204 - auc: 0.9773 - prc: 0.9821"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 26ms/step - loss: 0.1961 - cross entropy: 0.1961 - Brier score: 0.0575 - tp: 18762.0000 - fp: 1337.0000 - tn: 19238.0000 - fn: 1623.0000 - accuracy: 0.9277 - precision: 0.9335 - recall: 0.9204 - auc: 0.9773 - prc: 0.9821 - val_loss: 0.1636 - val_cross entropy: 0.1636 - val_Brier score: 0.0342 - val_tp: 75.0000 - val_fp: 997.0000 - val_tn: 44490.0000 - val_fn: 7.0000 - val_accuracy: 0.9780 - val_precision: 0.0700 - val_recall: 0.9146 - val_auc: 0.9777 - val_prc: 0.7943\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 18/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1872 - cross entropy: 0.1872 - Brier score: 0.0549 - tp: 916.0000 - fp: 55.0000 - tn: 990.0000 - fn: 87.0000 - accuracy: 0.9307 - precision: 0.9434 - recall: 0.9133 - auc: 0.9791 - prc: 0.9833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1886 - cross entropy: 0.1886 - Brier score: 0.0557 - tp: 4658.0000 - fp: 325.0000 - tn: 4846.0000 - fn: 411.0000 - accuracy: 0.9281 - precision: 0.9348 - recall: 0.9189 - auc: 0.9791 - prc: 0.9834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1893 - cross entropy: 0.1893 - Brier score: 0.0552 - tp: 7474.0000 - fp: 497.0000 - tn: 7753.0000 - fn: 660.0000 - accuracy: 0.9294 - precision: 0.9376 - recall: 0.9189 - auc: 0.9790 - prc: 0.9832"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1888 - cross entropy: 0.1888 - Brier score: 0.0550 - tp: 10210.0000 - fp: 678.0000 - tn: 10730.0000 - fn: 910.0000 - accuracy: 0.9295 - precision: 0.9377 - recall: 0.9182 - auc: 0.9790 - prc: 0.9831"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1893 - cross entropy: 0.1893 - Brier score: 0.0553 - tp: 13031.0000 - fp: 871.0000 - tn: 13597.0000 - fn: 1173.0000 - accuracy: 0.9287 - precision: 0.9373 - recall: 0.9174 - auc: 0.9787 - prc: 0.9830"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1882 - cross entropy: 0.1882 - Brier score: 0.0550 - tp: 15886.0000 - fp: 1060.0000 - tn: 16476.0000 - fn: 1394.0000 - accuracy: 0.9295 - precision: 0.9374 - recall: 0.9193 - auc: 0.9791 - prc: 0.9834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1891 - cross entropy: 0.1891 - Brier score: 0.0554 - tp: 18751.0000 - fp: 1286.0000 - tn: 19292.0000 - fn: 1631.0000 - accuracy: 0.9288 - precision: 0.9358 - recall: 0.9200 - auc: 0.9789 - prc: 0.9833"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 26ms/step - loss: 0.1891 - cross entropy: 0.1891 - Brier score: 0.0554 - tp: 18751.0000 - fp: 1286.0000 - tn: 19292.0000 - fn: 1631.0000 - accuracy: 0.9288 - precision: 0.9358 - recall: 0.9200 - auc: 0.9789 - prc: 0.9833 - val_loss: 0.1544 - val_cross entropy: 0.1544 - val_Brier score: 0.0320 - val_tp: 75.0000 - val_fp: 981.0000 - val_tn: 44506.0000 - val_fn: 7.0000 - val_accuracy: 0.9783 - val_precision: 0.0710 - val_recall: 0.9146 - val_auc: 0.9780 - val_prc: 0.7971\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 19/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1790 - cross entropy: 0.1790 - Brier score: 0.0520 - tp: 956.0000 - fp: 47.0000 - tn: 962.0000 - fn: 83.0000 - accuracy: 0.9365 - precision: 0.9531 - recall: 0.9201 - auc: 0.9801 - prc: 0.9851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1836 - cross entropy: 0.1836 - Brier score: 0.0539 - tp: 4653.0000 - fp: 305.0000 - tn: 4882.0000 - fn: 400.0000 - accuracy: 0.9312 - precision: 0.9385 - recall: 0.9208 - auc: 0.9798 - prc: 0.9839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1855 - cross entropy: 0.1855 - Brier score: 0.0542 - tp: 7463.0000 - fp: 482.0000 - tn: 7808.0000 - fn: 631.0000 - accuracy: 0.9321 - precision: 0.9393 - recall: 0.9220 - auc: 0.9796 - prc: 0.9837"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1844 - cross entropy: 0.1844 - Brier score: 0.0541 - tp: 10346.0000 - fp: 652.0000 - tn: 10651.0000 - fn: 879.0000 - accuracy: 0.9320 - precision: 0.9407 - recall: 0.9217 - auc: 0.9799 - prc: 0.9841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1846 - cross entropy: 0.1846 - Brier score: 0.0540 - tp: 13130.0000 - fp: 814.0000 - tn: 13605.0000 - fn: 1123.0000 - accuracy: 0.9324 - precision: 0.9416 - recall: 0.9212 - auc: 0.9797 - prc: 0.9839"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1841 - cross entropy: 0.1841 - Brier score: 0.0538 - tp: 15971.0000 - fp: 989.0000 - tn: 16502.0000 - fn: 1354.0000 - accuracy: 0.9327 - precision: 0.9417 - recall: 0.9218 - auc: 0.9800 - prc: 0.9841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1833 - cross entropy: 0.1833 - Brier score: 0.0534 - tp: 18789.0000 - fp: 1144.0000 - tn: 19432.0000 - fn: 1595.0000 - accuracy: 0.9331 - precision: 0.9426 - recall: 0.9218 - auc: 0.9802 - prc: 0.9842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 1s 27ms/step - loss: 0.1833 - cross entropy: 0.1833 - Brier score: 0.0534 - tp: 18789.0000 - fp: 1144.0000 - tn: 19432.0000 - fn: 1595.0000 - accuracy: 0.9331 - precision: 0.9426 - recall: 0.9218 - auc: 0.9802 - prc: 0.9842 - val_loss: 0.1461 - val_cross entropy: 0.1461 - val_Brier score: 0.0300 - val_tp: 76.0000 - val_fp: 949.0000 - val_tn: 44538.0000 - val_fn: 6.0000 - val_accuracy: 0.9790 - val_precision: 0.0741 - val_recall: 0.9268 - val_auc: 0.9782 - val_prc: 0.7972\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 20/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1858 - cross entropy: 0.1858 - Brier score: 0.0552 - tp: 985.0000 - fp: 59.0000 - tn: 918.0000 - fn: 86.0000 - accuracy: 0.9292 - precision: 0.9435 - recall: 0.9197 - auc: 0.9779 - prc: 0.9841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 4/20 [=====>........................] - ETA: 0s - loss: 0.1835 - cross entropy: 0.1835 - Brier score: 0.0532 - tp: 3797.0000 - fp: 244.0000 - tn: 3829.0000 - fn: 322.0000 - accuracy: 0.9309 - precision: 0.9396 - recall: 0.9218 - auc: 0.9801 - prc: 0.9841"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 7/20 [=========>....................] - ETA: 0s - loss: 0.1814 - cross entropy: 0.1814 - Brier score: 0.0528 - tp: 6612.0000 - fp: 409.0000 - tn: 6761.0000 - fn: 554.0000 - accuracy: 0.9328 - precision: 0.9417 - recall: 0.9227 - auc: 0.9801 - prc: 0.9842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "10/20 [==============>...............] - ETA: 0s - loss: 0.1808 - cross entropy: 0.1808 - Brier score: 0.0527 - tp: 9480.0000 - fp: 574.0000 - tn: 9640.0000 - fn: 786.0000 - accuracy: 0.9336 - precision: 0.9429 - recall: 0.9234 - auc: 0.9804 - prc: 0.9843"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "13/20 [==================>...........] - ETA: 0s - loss: 0.1791 - cross entropy: 0.1791 - Brier score: 0.0521 - tp: 12358.0000 - fp: 723.0000 - tn: 12543.0000 - fn: 1000.0000 - accuracy: 0.9353 - precision: 0.9447 - recall: 0.9251 - auc: 0.9809 - prc: 0.9847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "16/20 [=======================>......] - ETA: 0s - loss: 0.1795 - cross entropy: 0.1795 - Brier score: 0.0524 - tp: 15169.0000 - fp: 913.0000 - tn: 15455.0000 - fn: 1231.0000 - accuracy: 0.9346 - precision: 0.9432 - recall: 0.9249 - auc: 0.9808 - prc: 0.9846"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "19/20 [===========================>..] - ETA: 0s - loss: 0.1785 - cross entropy: 0.1785 - Brier score: 0.0520 - tp: 17912.0000 - fp: 1077.0000 - tn: 18460.0000 - fn: 1463.0000 - accuracy: 0.9347 - precision: 0.9433 - recall: 0.9245 - auc: 0.9811 - prc: 0.9847"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 1s 27ms/step - loss: 0.1775 - cross entropy: 0.1775 - Brier score: 0.0517 - tp: 18845.0000 - fp: 1120.0000 - tn: 19463.0000 - fn: 1532.0000 - accuracy: 0.9353 - precision: 0.9439 - recall: 0.9248 - auc: 0.9814 - prc: 0.9849 - val_loss: 0.1394 - val_cross entropy: 0.1394 - val_Brier score: 0.0287 - val_tp: 76.0000 - val_fp: 969.0000 - val_tn: 44518.0000 - val_fn: 6.0000 - val_accuracy: 0.9786 - val_precision: 0.0727 - val_recall: 0.9268 - val_auc: 0.9788 - val_prc: 0.7971\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1581 - cross entropy: 0.1581 - Brier score: 0.0463 - tp: 964.0000 - fp: 52.0000 - tn: 968.0000 - fn: 64.0000 - accuracy: 0.9434 - precision: 0.9488 - recall: 0.9377 - auc: 0.9855 - prc: 0.9881"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1717 - cross entropy: 0.1717 - Brier score: 0.0505 - tp: 4717.0000 - fp: 270.0000 - tn: 4864.0000 - fn: 389.0000 - accuracy: 0.9356 - precision: 0.9459 - recall: 0.9238 - auc: 0.9823 - prc: 0.9857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1712 - cross entropy: 0.1712 - Brier score: 0.0501 - tp: 7649.0000 - fp: 413.0000 - tn: 7703.0000 - fn: 619.0000 - accuracy: 0.9370 - precision: 0.9488 - recall: 0.9251 - auc: 0.9824 - prc: 0.9860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1723 - cross entropy: 0.1723 - Brier score: 0.0507 - tp: 10487.0000 - fp: 589.0000 - tn: 10605.0000 - fn: 847.0000 - accuracy: 0.9363 - precision: 0.9468 - recall: 0.9253 - auc: 0.9822 - prc: 0.9857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1730 - cross entropy: 0.1730 - Brier score: 0.0507 - tp: 13292.0000 - fp: 754.0000 - tn: 13539.0000 - fn: 1087.0000 - accuracy: 0.9358 - precision: 0.9463 - recall: 0.9244 - auc: 0.9820 - prc: 0.9855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1729 - cross entropy: 0.1729 - Brier score: 0.0507 - tp: 16163.0000 - fp: 910.0000 - tn: 16418.0000 - fn: 1325.0000 - accuracy: 0.9358 - precision: 0.9467 - recall: 0.9242 - auc: 0.9819 - prc: 0.9854"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1727 - cross entropy: 0.1727 - Brier score: 0.0506 - tp: 19042.0000 - fp: 1056.0000 - tn: 19310.0000 - fn: 1552.0000 - accuracy: 0.9363 - precision: 0.9475 - recall: 0.9246 - auc: 0.9818 - prc: 0.9855"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 1s 26ms/step - loss: 0.1727 - cross entropy: 0.1727 - Brier score: 0.0506 - tp: 19042.0000 - fp: 1056.0000 - tn: 19310.0000 - fn: 1552.0000 - accuracy: 0.9363 - precision: 0.9475 - recall: 0.9246 - auc: 0.9818 - prc: 0.9855 - val_loss: 0.1331 - val_cross entropy: 0.1331 - val_Brier score: 0.0274 - val_tp: 76.0000 - val_fp: 965.0000 - val_tn: 44522.0000 - val_fn: 6.0000 - val_accuracy: 0.9787 - val_precision: 0.0730 - val_recall: 0.9268 - val_auc: 0.9789 - val_prc: 0.7973\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1731 - cross entropy: 0.1731 - Brier score: 0.0511 - tp: 916.0000 - fp: 60.0000 - tn: 1002.0000 - fn: 70.0000 - accuracy: 0.9365 - precision: 0.9385 - recall: 0.9290 - auc: 0.9826 - prc: 0.9851"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1685 - cross entropy: 0.1685 - Brier score: 0.0495 - tp: 4705.0000 - fp: 258.0000 - tn: 4900.0000 - fn: 377.0000 - accuracy: 0.9380 - precision: 0.9480 - recall: 0.9258 - auc: 0.9828 - prc: 0.9861"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1713 - cross entropy: 0.1713 - Brier score: 0.0501 - tp: 7541.0000 - fp: 437.0000 - tn: 7805.0000 - fn: 601.0000 - accuracy: 0.9366 - precision: 0.9452 - recall: 0.9262 - auc: 0.9825 - prc: 0.9856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1729 - cross entropy: 0.1729 - Brier score: 0.0504 - tp: 10448.0000 - fp: 608.0000 - tn: 10635.0000 - fn: 837.0000 - accuracy: 0.9359 - precision: 0.9450 - recall: 0.9258 - auc: 0.9823 - prc: 0.9856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1713 - cross entropy: 0.1713 - Brier score: 0.0501 - tp: 13336.0000 - fp: 765.0000 - tn: 13501.0000 - fn: 1070.0000 - accuracy: 0.9360 - precision: 0.9457 - recall: 0.9257 - auc: 0.9826 - prc: 0.9859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1717 - cross entropy: 0.1717 - Brier score: 0.0503 - tp: 16168.0000 - fp: 943.0000 - tn: 16406.0000 - fn: 1299.0000 - accuracy: 0.9356 - precision: 0.9449 - recall: 0.9256 - auc: 0.9825 - prc: 0.9858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1711 - cross entropy: 0.1711 - Brier score: 0.0501 - tp: 19041.0000 - fp: 1102.0000 - tn: 19283.0000 - fn: 1534.0000 - accuracy: 0.9356 - precision: 0.9453 - recall: 0.9254 - auc: 0.9826 - prc: 0.9859"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.1711 - cross entropy: 0.1711 - Brier score: 0.0501 - tp: 19041.0000 - fp: 1102.0000 - tn: 19283.0000 - fn: 1534.0000 - accuracy: 0.9356 - precision: 0.9453 - recall: 0.9254 - auc: 0.9826 - prc: 0.9859 - val_loss: 0.1275 - val_cross entropy: 0.1275 - val_Brier score: 0.0262 - val_tp: 76.0000 - val_fp: 965.0000 - val_tn: 44522.0000 - val_fn: 6.0000 - val_accuracy: 0.9787 - val_precision: 0.0730 - val_recall: 0.9268 - val_auc: 0.9784 - val_prc: 0.7879\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 23/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1728 - cross entropy: 0.1728 - Brier score: 0.0502 - tp: 932.0000 - fp: 54.0000 - tn: 989.0000 - fn: 73.0000 - accuracy: 0.9380 - precision: 0.9452 - recall: 0.9274 - auc: 0.9817 - prc: 0.9850"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1703 - cross entropy: 0.1703 - Brier score: 0.0491 - tp: 4747.0000 - fp: 280.0000 - tn: 4848.0000 - fn: 365.0000 - accuracy: 0.9370 - precision: 0.9443 - recall: 0.9286 - auc: 0.9829 - prc: 0.9856"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1672 - cross entropy: 0.1672 - Brier score: 0.0484 - tp: 7613.0000 - fp: 429.0000 - tn: 7762.0000 - fn: 580.0000 - accuracy: 0.9384 - precision: 0.9467 - recall: 0.9292 - auc: 0.9836 - prc: 0.9864"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1689 - cross entropy: 0.1689 - Brier score: 0.0487 - tp: 10433.0000 - fp: 585.0000 - tn: 10701.0000 - fn: 809.0000 - accuracy: 0.9381 - precision: 0.9469 - recall: 0.9280 - auc: 0.9832 - prc: 0.9860"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1666 - cross entropy: 0.1666 - Brier score: 0.0481 - tp: 13334.0000 - fp: 746.0000 - tn: 13579.0000 - fn: 1013.0000 - accuracy: 0.9387 - precision: 0.9470 - recall: 0.9294 - auc: 0.9837 - prc: 0.9865"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1657 - cross entropy: 0.1657 - Brier score: 0.0479 - tp: 16223.0000 - fp: 902.0000 - tn: 16451.0000 - fn: 1240.0000 - accuracy: 0.9385 - precision: 0.9473 - recall: 0.9290 - auc: 0.9838 - prc: 0.9867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1657 - cross entropy: 0.1657 - Brier score: 0.0479 - tp: 19074.0000 - fp: 1045.0000 - tn: 19372.0000 - fn: 1469.0000 - accuracy: 0.9386 - precision: 0.9481 - recall: 0.9285 - auc: 0.9838 - prc: 0.9867"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 1s 27ms/step - loss: 0.1657 - cross entropy: 0.1657 - Brier score: 0.0479 - tp: 19074.0000 - fp: 1045.0000 - tn: 19372.0000 - fn: 1469.0000 - accuracy: 0.9386 - precision: 0.9481 - recall: 0.9285 - auc: 0.9838 - prc: 0.9867 - val_loss: 0.1215 - val_cross entropy: 0.1215 - val_Brier score: 0.0249 - val_tp: 76.0000 - val_fp: 939.0000 - val_tn: 44548.0000 - val_fn: 6.0000 - val_accuracy: 0.9793 - val_precision: 0.0749 - val_recall: 0.9268 - val_auc: 0.9785 - val_prc: 0.7882\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 24/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1599 - cross entropy: 0.1599 - Brier score: 0.0460 - tp: 964.0000 - fp: 56.0000 - tn: 963.0000 - fn: 65.0000 - accuracy: 0.9409 - precision: 0.9451 - recall: 0.9368 - auc: 0.9856 - prc: 0.9882"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1633 - cross entropy: 0.1633 - Brier score: 0.0479 - tp: 4841.0000 - fp: 269.0000 - tn: 4779.0000 - fn: 351.0000 - accuracy: 0.9395 - precision: 0.9474 - recall: 0.9324 - auc: 0.9842 - prc: 0.9875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1620 - cross entropy: 0.1620 - Brier score: 0.0473 - tp: 7736.0000 - fp: 410.0000 - tn: 7672.0000 - fn: 566.0000 - accuracy: 0.9404 - precision: 0.9497 - recall: 0.9318 - auc: 0.9843 - prc: 0.9875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1611 - cross entropy: 0.1611 - Brier score: 0.0471 - tp: 10555.0000 - fp: 545.0000 - tn: 10645.0000 - fn: 783.0000 - accuracy: 0.9411 - precision: 0.9509 - recall: 0.9309 - auc: 0.9844 - prc: 0.9875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1627 - cross entropy: 0.1627 - Brier score: 0.0476 - tp: 13376.0000 - fp: 729.0000 - tn: 13557.0000 - fn: 1010.0000 - accuracy: 0.9393 - precision: 0.9483 - recall: 0.9298 - auc: 0.9840 - prc: 0.9871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1629 - cross entropy: 0.1629 - Brier score: 0.0478 - tp: 16196.0000 - fp: 902.0000 - tn: 16479.0000 - fn: 1239.0000 - accuracy: 0.9385 - precision: 0.9472 - recall: 0.9289 - auc: 0.9840 - prc: 0.9870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1631 - cross entropy: 0.1631 - Brier score: 0.0478 - tp: 19006.0000 - fp: 1055.0000 - tn: 19442.0000 - fn: 1457.0000 - accuracy: 0.9387 - precision: 0.9474 - recall: 0.9288 - auc: 0.9839 - prc: 0.9868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 26ms/step - loss: 0.1631 - cross entropy: 0.1631 - Brier score: 0.0478 - tp: 19006.0000 - fp: 1055.0000 - tn: 19442.0000 - fn: 1457.0000 - accuracy: 0.9387 - precision: 0.9474 - recall: 0.9288 - auc: 0.9839 - prc: 0.9868 - val_loss: 0.1166 - val_cross entropy: 0.1166 - val_Brier score: 0.0239 - val_tp: 76.0000 - val_fp: 924.0000 - val_tn: 44563.0000 - val_fn: 6.0000 - val_accuracy: 0.9796 - val_precision: 0.0760 - val_recall: 0.9268 - val_auc: 0.9780 - val_prc: 0.7886\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 25/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1668 - cross entropy: 0.1668 - Brier score: 0.0495 - tp: 918.0000 - fp: 61.0000 - tn: 990.0000 - fn: 79.0000 - accuracy: 0.9316 - precision: 0.9377 - recall: 0.9208 - auc: 0.9834 - prc: 0.9858"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1606 - cross entropy: 0.1606 - Brier score: 0.0473 - tp: 4775.0000 - fp: 273.0000 - tn: 4828.0000 - fn: 364.0000 - accuracy: 0.9378 - precision: 0.9459 - recall: 0.9292 - auc: 0.9844 - prc: 0.9872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1592 - cross entropy: 0.1592 - Brier score: 0.0467 - tp: 7588.0000 - fp: 407.0000 - tn: 7795.0000 - fn: 594.0000 - accuracy: 0.9389 - precision: 0.9491 - recall: 0.9274 - auc: 0.9846 - prc: 0.9874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1597 - cross entropy: 0.1597 - Brier score: 0.0467 - tp: 10496.0000 - fp: 548.0000 - tn: 10674.0000 - fn: 810.0000 - accuracy: 0.9397 - precision: 0.9504 - recall: 0.9284 - auc: 0.9845 - prc: 0.9874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1610 - cross entropy: 0.1610 - Brier score: 0.0472 - tp: 13357.0000 - fp: 706.0000 - tn: 13574.0000 - fn: 1035.0000 - accuracy: 0.9393 - precision: 0.9498 - recall: 0.9281 - auc: 0.9842 - prc: 0.9871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1605 - cross entropy: 0.1605 - Brier score: 0.0470 - tp: 16160.0000 - fp: 856.0000 - tn: 16552.0000 - fn: 1248.0000 - accuracy: 0.9396 - precision: 0.9497 - recall: 0.9283 - auc: 0.9843 - prc: 0.9871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1586 - cross entropy: 0.1586 - Brier score: 0.0464 - tp: 19058.0000 - fp: 971.0000 - tn: 19476.0000 - fn: 1455.0000 - accuracy: 0.9408 - precision: 0.9515 - recall: 0.9291 - auc: 0.9847 - prc: 0.9875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.1586 - cross entropy: 0.1586 - Brier score: 0.0464 - tp: 19058.0000 - fp: 971.0000 - tn: 19476.0000 - fn: 1455.0000 - accuracy: 0.9408 - precision: 0.9515 - recall: 0.9291 - auc: 0.9847 - prc: 0.9875 - val_loss: 0.1119 - val_cross entropy: 0.1119 - val_Brier score: 0.0229 - val_tp: 76.0000 - val_fp: 908.0000 - val_tn: 44579.0000 - val_fn: 6.0000 - val_accuracy: 0.9799 - val_precision: 0.0772 - val_recall: 0.9268 - val_auc: 0.9783 - val_prc: 0.7886\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 26/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1626 - cross entropy: 0.1626 - Brier score: 0.0477 - tp: 957.0000 - fp: 63.0000 - tn: 966.0000 - fn: 62.0000 - accuracy: 0.9390 - precision: 0.9382 - recall: 0.9392 - auc: 0.9848 - prc: 0.9874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1584 - cross entropy: 0.1584 - Brier score: 0.0467 - tp: 4702.0000 - fp: 267.0000 - tn: 4911.0000 - fn: 360.0000 - accuracy: 0.9388 - precision: 0.9463 - recall: 0.9289 - auc: 0.9853 - prc: 0.9875"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1608 - cross entropy: 0.1608 - Brier score: 0.0473 - tp: 7474.0000 - fp: 435.0000 - tn: 7888.0000 - fn: 587.0000 - accuracy: 0.9376 - precision: 0.9450 - recall: 0.9272 - auc: 0.9847 - prc: 0.9868"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1583 - cross entropy: 0.1583 - Brier score: 0.0465 - tp: 10305.0000 - fp: 561.0000 - tn: 10858.0000 - fn: 804.0000 - accuracy: 0.9394 - precision: 0.9484 - recall: 0.9276 - auc: 0.9850 - prc: 0.9872"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1581 - cross entropy: 0.1581 - Brier score: 0.0464 - tp: 13134.0000 - fp: 711.0000 - tn: 13817.0000 - fn: 1010.0000 - accuracy: 0.9400 - precision: 0.9486 - recall: 0.9286 - auc: 0.9850 - prc: 0.9873"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1570 - cross entropy: 0.1570 - Brier score: 0.0459 - tp: 15980.0000 - fp: 842.0000 - tn: 16778.0000 - fn: 1216.0000 - accuracy: 0.9409 - precision: 0.9499 - recall: 0.9293 - auc: 0.9852 - prc: 0.9874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1568 - cross entropy: 0.1568 - Brier score: 0.0459 - tp: 18807.0000 - fp: 974.0000 - tn: 19740.0000 - fn: 1439.0000 - accuracy: 0.9411 - precision: 0.9508 - recall: 0.9289 - auc: 0.9851 - prc: 0.9874"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 26ms/step - loss: 0.1568 - cross entropy: 0.1568 - Brier score: 0.0459 - tp: 18807.0000 - fp: 974.0000 - tn: 19740.0000 - fn: 1439.0000 - accuracy: 0.9411 - precision: 0.9508 - recall: 0.9289 - auc: 0.9851 - prc: 0.9874 - val_loss: 0.1072 - val_cross entropy: 0.1072 - val_Brier score: 0.0219 - val_tp: 76.0000 - val_fp: 881.0000 - val_tn: 44606.0000 - val_fn: 6.0000 - val_accuracy: 0.9805 - val_precision: 0.0794 - val_recall: 0.9268 - val_auc: 0.9779 - val_prc: 0.7889\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 27/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1744 - cross entropy: 0.1744 - Brier score: 0.0526 - tp: 916.0000 - fp: 68.0000 - tn: 981.0000 - fn: 83.0000 - accuracy: 0.9263 - precision: 0.9309 - recall: 0.9169 - auc: 0.9813 - prc: 0.9836"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1585 - cross entropy: 0.1585 - Brier score: 0.0464 - tp: 4708.0000 - fp: 267.0000 - tn: 4892.0000 - fn: 373.0000 - accuracy: 0.9375 - precision: 0.9463 - recall: 0.9266 - auc: 0.9850 - prc: 0.9871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1595 - cross entropy: 0.1595 - Brier score: 0.0464 - tp: 7601.0000 - fp: 421.0000 - tn: 7774.0000 - fn: 588.0000 - accuracy: 0.9384 - precision: 0.9475 - recall: 0.9282 - auc: 0.9848 - prc: 0.9870"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1584 - cross entropy: 0.1584 - Brier score: 0.0463 - tp: 10458.0000 - fp: 573.0000 - tn: 10695.0000 - fn: 802.0000 - accuracy: 0.9390 - precision: 0.9481 - recall: 0.9288 - auc: 0.9850 - prc: 0.9871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1585 - cross entropy: 0.1585 - Brier score: 0.0464 - tp: 13277.0000 - fp: 728.0000 - tn: 13635.0000 - fn: 1032.0000 - accuracy: 0.9386 - precision: 0.9480 - recall: 0.9279 - auc: 0.9849 - prc: 0.9871"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1578 - cross entropy: 0.1578 - Brier score: 0.0462 - tp: 16188.0000 - fp: 871.0000 - tn: 16519.0000 - fn: 1238.0000 - accuracy: 0.9394 - precision: 0.9489 - recall: 0.9290 - auc: 0.9850 - prc: 0.9873"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1562 - cross entropy: 0.1562 - Brier score: 0.0457 - tp: 19045.0000 - fp: 1010.0000 - tn: 19477.0000 - fn: 1428.0000 - accuracy: 0.9405 - precision: 0.9496 - recall: 0.9302 - auc: 0.9854 - prc: 0.9876"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.1562 - cross entropy: 0.1562 - Brier score: 0.0457 - tp: 19045.0000 - fp: 1010.0000 - tn: 19477.0000 - fn: 1428.0000 - accuracy: 0.9405 - precision: 0.9496 - recall: 0.9302 - auc: 0.9854 - prc: 0.9876 - val_loss: 0.1032 - val_cross entropy: 0.1032 - val_Brier score: 0.0211 - val_tp: 76.0000 - val_fp: 864.0000 - val_tn: 44623.0000 - val_fn: 6.0000 - val_accuracy: 0.9809 - val_precision: 0.0809 - val_recall: 0.9268 - val_auc: 0.9774 - val_prc: 0.7704\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 28/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1554 - cross entropy: 0.1554 - Brier score: 0.0457 - tp: 972.0000 - fp: 39.0000 - tn: 963.0000 - fn: 74.0000 - accuracy: 0.9448 - precision: 0.9614 - recall: 0.9293 - auc: 0.9850 - prc: 0.9882"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1550 - cross entropy: 0.1550 - Brier score: 0.0456 - tp: 4741.0000 - fp: 231.0000 - tn: 4895.0000 - fn: 373.0000 - accuracy: 0.9410 - precision: 0.9535 - recall: 0.9271 - auc: 0.9854 - prc: 0.9878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1555 - cross entropy: 0.1555 - Brier score: 0.0452 - tp: 7636.0000 - fp: 346.0000 - tn: 7817.0000 - fn: 585.0000 - accuracy: 0.9432 - precision: 0.9567 - recall: 0.9288 - auc: 0.9854 - prc: 0.9876"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1546 - cross entropy: 0.1546 - Brier score: 0.0447 - tp: 10505.0000 - fp: 481.0000 - tn: 10748.0000 - fn: 794.0000 - accuracy: 0.9434 - precision: 0.9562 - recall: 0.9297 - auc: 0.9858 - prc: 0.9878"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1534 - cross entropy: 0.1534 - Brier score: 0.0445 - tp: 13382.0000 - fp: 615.0000 - tn: 13681.0000 - fn: 994.0000 - accuracy: 0.9439 - precision: 0.9561 - recall: 0.9309 - auc: 0.9861 - prc: 0.9881"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1530 - cross entropy: 0.1530 - Brier score: 0.0444 - tp: 16179.0000 - fp: 755.0000 - tn: 16667.0000 - fn: 1215.0000 - accuracy: 0.9434 - precision: 0.9554 - recall: 0.9301 - auc: 0.9862 - prc: 0.9881"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1525 - cross entropy: 0.1525 - Brier score: 0.0442 - tp: 19016.0000 - fp: 881.0000 - tn: 19650.0000 - fn: 1413.0000 - accuracy: 0.9440 - precision: 0.9557 - recall: 0.9308 - auc: 0.9862 - prc: 0.9882"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.1525 - cross entropy: 0.1525 - Brier score: 0.0442 - tp: 19016.0000 - fp: 881.0000 - tn: 19650.0000 - fn: 1413.0000 - accuracy: 0.9440 - precision: 0.9557 - recall: 0.9308 - auc: 0.9862 - prc: 0.9882 - val_loss: 0.0998 - val_cross entropy: 0.0998 - val_Brier score: 0.0205 - val_tp: 76.0000 - val_fp: 866.0000 - val_tn: 44621.0000 - val_fn: 6.0000 - val_accuracy: 0.9809 - val_precision: 0.0807 - val_recall: 0.9268 - val_auc: 0.9778 - val_prc: 0.7706\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1483 - cross entropy: 0.1483 - Brier score: 0.0421 - tp: 962.0000 - fp: 42.0000 - tn: 975.0000 - fn: 69.0000 - accuracy: 0.9458 - precision: 0.9582 - recall: 0.9331 - auc: 0.9856 - prc: 0.9890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1483 - cross entropy: 0.1483 - Brier score: 0.0433 - tp: 4735.0000 - fp: 218.0000 - tn: 4956.0000 - fn: 331.0000 - accuracy: 0.9464 - precision: 0.9560 - recall: 0.9347 - auc: 0.9867 - prc: 0.9889"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1463 - cross entropy: 0.1463 - Brier score: 0.0427 - tp: 7612.0000 - fp: 329.0000 - tn: 7902.0000 - fn: 541.0000 - accuracy: 0.9469 - precision: 0.9586 - recall: 0.9336 - auc: 0.9871 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1448 - cross entropy: 0.1448 - Brier score: 0.0423 - tp: 10503.0000 - fp: 444.0000 - tn: 10837.0000 - fn: 744.0000 - accuracy: 0.9473 - precision: 0.9594 - recall: 0.9338 - auc: 0.9875 - prc: 0.9896"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1456 - cross entropy: 0.1456 - Brier score: 0.0424 - tp: 13362.0000 - fp: 580.0000 - tn: 13785.0000 - fn: 945.0000 - accuracy: 0.9468 - precision: 0.9584 - recall: 0.9339 - auc: 0.9873 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1460 - cross entropy: 0.1460 - Brier score: 0.0427 - tp: 16262.0000 - fp: 713.0000 - tn: 16657.0000 - fn: 1184.0000 - accuracy: 0.9455 - precision: 0.9580 - recall: 0.9321 - auc: 0.9871 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1465 - cross entropy: 0.1465 - Brier score: 0.0429 - tp: 19105.0000 - fp: 852.0000 - tn: 19596.0000 - fn: 1407.0000 - accuracy: 0.9448 - precision: 0.9573 - recall: 0.9314 - auc: 0.9870 - prc: 0.9891"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 24ms/step - loss: 0.1465 - cross entropy: 0.1465 - Brier score: 0.0429 - tp: 19105.0000 - fp: 852.0000 - tn: 19596.0000 - fn: 1407.0000 - accuracy: 0.9448 - precision: 0.9573 - recall: 0.9314 - auc: 0.9870 - prc: 0.9891 - val_loss: 0.0968 - val_cross entropy: 0.0968 - val_Brier score: 0.0200 - val_tp: 76.0000 - val_fp: 868.0000 - val_tn: 44619.0000 - val_fn: 6.0000 - val_accuracy: 0.9808 - val_precision: 0.0805 - val_recall: 0.9268 - val_auc: 0.9770 - val_prc: 0.7709\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 30/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1380 - cross entropy: 0.1380 - Brier score: 0.0402 - tp: 944.0000 - fp: 36.0000 - tn: 1000.0000 - fn: 68.0000 - accuracy: 0.9492 - precision: 0.9633 - recall: 0.9328 - auc: 0.9884 - prc: 0.9901"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1441 - cross entropy: 0.1441 - Brier score: 0.0425 - tp: 4759.0000 - fp: 208.0000 - tn: 4922.0000 - fn: 351.0000 - accuracy: 0.9454 - precision: 0.9581 - recall: 0.9313 - auc: 0.9872 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1440 - cross entropy: 0.1440 - Brier score: 0.0423 - tp: 7676.0000 - fp: 334.0000 - tn: 7826.0000 - fn: 548.0000 - accuracy: 0.9462 - precision: 0.9583 - recall: 0.9334 - auc: 0.9872 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1440 - cross entropy: 0.1440 - Brier score: 0.0422 - tp: 10532.0000 - fp: 457.0000 - tn: 10777.0000 - fn: 762.0000 - accuracy: 0.9459 - precision: 0.9584 - recall: 0.9325 - auc: 0.9872 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1452 - cross entropy: 0.1452 - Brier score: 0.0426 - tp: 13370.0000 - fp: 570.0000 - tn: 13742.0000 - fn: 990.0000 - accuracy: 0.9456 - precision: 0.9591 - recall: 0.9311 - auc: 0.9868 - prc: 0.9890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1461 - cross entropy: 0.1461 - Brier score: 0.0429 - tp: 16214.0000 - fp: 701.0000 - tn: 16692.0000 - fn: 1209.0000 - accuracy: 0.9451 - precision: 0.9586 - recall: 0.9306 - auc: 0.9867 - prc: 0.9888"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1465 - cross entropy: 0.1465 - Brier score: 0.0431 - tp: 19112.0000 - fp: 860.0000 - tn: 19584.0000 - fn: 1404.0000 - accuracy: 0.9447 - precision: 0.9569 - recall: 0.9316 - auc: 0.9867 - prc: 0.9888"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.1465 - cross entropy: 0.1465 - Brier score: 0.0431 - tp: 19112.0000 - fp: 860.0000 - tn: 19584.0000 - fn: 1404.0000 - accuracy: 0.9447 - precision: 0.9569 - recall: 0.9316 - auc: 0.9867 - prc: 0.9888 - val_loss: 0.0941 - val_cross entropy: 0.0941 - val_Brier score: 0.0195 - val_tp: 76.0000 - val_fp: 850.0000 - val_tn: 44637.0000 - val_fn: 6.0000 - val_accuracy: 0.9812 - val_precision: 0.0821 - val_recall: 0.9268 - val_auc: 0.9774 - val_prc: 0.7712\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/20 [>.............................] - ETA: 0s - loss: 0.1451 - cross entropy: 0.1451 - Brier score: 0.0428 - tp: 959.0000 - fp: 46.0000 - tn: 978.0000 - fn: 65.0000 - accuracy: 0.9458 - precision: 0.9542 - recall: 0.9365 - auc: 0.9875 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 5/20 [======>.......................] - ETA: 0s - loss: 0.1433 - cross entropy: 0.1433 - Brier score: 0.0421 - tp: 4751.0000 - fp: 214.0000 - tn: 4944.0000 - fn: 331.0000 - accuracy: 0.9468 - precision: 0.9569 - recall: 0.9349 - auc: 0.9875 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      " 8/20 [===========>..................] - ETA: 0s - loss: 0.1423 - cross entropy: 0.1423 - Brier score: 0.0415 - tp: 7616.0000 - fp: 317.0000 - tn: 7910.0000 - fn: 541.0000 - accuracy: 0.9476 - precision: 0.9600 - recall: 0.9337 - auc: 0.9878 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "11/20 [===============>..............] - ETA: 0s - loss: 0.1422 - cross entropy: 0.1422 - Brier score: 0.0415 - tp: 10461.0000 - fp: 453.0000 - tn: 10878.0000 - fn: 736.0000 - accuracy: 0.9472 - precision: 0.9585 - recall: 0.9343 - auc: 0.9879 - prc: 0.9895"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "14/20 [====================>.........] - ETA: 0s - loss: 0.1439 - cross entropy: 0.1439 - Brier score: 0.0421 - tp: 13298.0000 - fp: 596.0000 - tn: 13823.0000 - fn: 955.0000 - accuracy: 0.9459 - precision: 0.9571 - recall: 0.9330 - auc: 0.9875 - prc: 0.9892"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "17/20 [========================>.....] - ETA: 0s - loss: 0.1434 - cross entropy: 0.1434 - Brier score: 0.0419 - tp: 16172.0000 - fp: 721.0000 - tn: 16761.0000 - fn: 1162.0000 - accuracy: 0.9459 - precision: 0.9573 - recall: 0.9330 - auc: 0.9877 - prc: 0.9894"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - ETA: 0s - loss: 0.1436 - cross entropy: 0.1436 - Brier score: 0.0420 - tp: 19077.0000 - fp: 857.0000 - tn: 19655.0000 - fn: 1371.0000 - accuracy: 0.9456 - precision: 0.9570 - recall: 0.9330 - auc: 0.9876 - prc: 0.9893"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restoring model weights from the end of the best epoch: 21.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "20/20 [==============================] - 0s 25ms/step - loss: 0.1436 - cross entropy: 0.1436 - Brier score: 0.0420 - tp: 19077.0000 - fp: 857.0000 - tn: 19655.0000 - fn: 1371.0000 - accuracy: 0.9456 - precision: 0.9570 - recall: 0.9330 - auc: 0.9876 - prc: 0.9893 - val_loss: 0.0912 - val_cross entropy: 0.0912 - val_Brier score: 0.0189 - val_tp: 76.0000 - val_fp: 826.0000 - val_tn: 44661.0000 - val_fn: 6.0000 - val_accuracy: 0.9817 - val_precision: 0.0843 - val_recall: 0.9268 - val_auc: 0.9767 - val_prc: 0.7622\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 31: early stopping\n"
     ]
    }
   ],
   "source": [
    "resampled_model = make_model()\n",
    "resampled_model.load_weights(initial_weights)\n",
    "\n",
    "# Reset the bias to zero, since this dataset is balanced.\n",
    "output_layer = resampled_model.layers[-1] \n",
    "output_layer.bias.assign([0])\n",
    "\n",
    "resampled_history = resampled_model.fit(\n",
    "    resampled_ds,\n",
    "    # These are not real epochs\n",
    "    steps_per_epoch=20,\n",
    "    epochs=10*EPOCHS,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UuJYKv0gpBK1"
   },
   "source": [
    "### Re-check training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:23:03.572708Z",
     "iopub.status.busy": "2024-01-17T02:23:03.571888Z",
     "iopub.status.idle": "2024-01-17T02:23:04.183479Z",
     "shell.execute_reply": "2024-01-17T02:23:04.182835Z"
    },
    "id": "FMycrpJwn39w"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(resampled_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUuE5HOWZiwP"
   },
   "source": [
    "### Evaluate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:23:04.187756Z",
     "iopub.status.busy": "2024-01-17T02:23:04.187090Z",
     "iopub.status.idle": "2024-01-17T02:23:04.643549Z",
     "shell.execute_reply": "2024-01-17T02:23:04.642852Z"
    },
    "id": "C0fmHSgXxFdW"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/90 [..............................] - ETA: 8s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "40/90 [============>.................] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "81/90 [==========================>...] - ETA: 0s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "90/90 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      " 1/28 [>.............................] - ETA: 1s"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "28/28 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:23:04.647397Z",
     "iopub.status.busy": "2024-01-17T02:23:04.646743Z",
     "iopub.status.idle": "2024-01-17T02:23:05.440099Z",
     "shell.execute_reply": "2024-01-17T02:23:05.439394Z"
    },
    "id": "FO0mMOYUDWFk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.13269135355949402\n",
      "cross entropy :  0.13269135355949402\n",
      "Brier score :  0.02699681930243969\n",
      "tp :  96.0\n",
      "fp :  1177.0\n",
      "tn :  55674.0\n",
      "fn :  15.0\n",
      "accuracy :  0.9790737628936768\n",
      "precision :  0.07541241496801376\n",
      "recall :  0.8648648858070374\n",
      "auc :  0.9722627401351929\n",
      "prc :  0.703483521938324\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  55674\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  1177\n",
      "Fraudulent Transactions Missed (False Negatives):  15\n",
      "Fraudulent Transactions Detected (True Positives):  96\n",
      "Total Fraudulent Transactions:  111\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampled_results = resampled_model.evaluate(test_features, test_labels,\n",
    "                                             batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(resampled_model.metrics_names, resampled_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "plot_cm(test_labels, test_predictions_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_xYozM1IIITq"
   },
   "source": [
    "### Plot the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:23:05.443724Z",
     "iopub.status.busy": "2024-01-17T02:23:05.443463Z",
     "iopub.status.idle": "2024-01-17T02:23:05.806546Z",
     "shell.execute_reply": "2024-01-17T02:23:05.805874Z"
    },
    "id": "fye_CiuYrZ1U"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "plot_roc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n",
    "plot_roc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vayGnv0VOe_v"
   },
   "source": [
    "### Plot the AUPRC\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-17T02:23:05.810508Z",
     "iopub.status.busy": "2024-01-17T02:23:05.810269Z",
     "iopub.status.idle": "2024-01-17T02:23:06.243971Z",
     "shell.execute_reply": "2024-01-17T02:23:06.243266Z"
    },
    "id": "wgWXQ8aeOhCZ"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\r\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\r\n",
    "\r\n",
    "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\r\n",
    "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\r\n",
    "\r\n",
    "plot_prc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\r\n",
    "plot_prc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\r\n",
    "plt.legend(loc='lower right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3o3f0ywl8uqW"
   },
   "source": [
    "## Applying this tutorial to your problem\n",
    "\n",
    "Imbalanced data classification is an inherently difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "imbalanced_data.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}