lstm-autoencoder.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"lstm-autoencoder.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"authorship_tag":"ABX9TyMw0/cf/lBpVcRYu0TX3qoR"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"eZ5vLdtnHUmn"},"source":["#### Copyright 2020 Connaissance Publishing."]},{"cell_type":"code","metadata":{"id":"e0itxBa9HWgS"},"source":["# 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."],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eyaeh36jFcNq"},"source":["# **Understanding Deep Learning: Application in Rare Event Prediction**\n","\n","Author: Chitta Ranjan, Ph.D.\n","\n","[ProcessMiner, Inc.](https://processminer.com)\n","\n","cranjan@processminer.com\n","\n","LinkedIn: https://www.linkedin.com/in/chitta-ranjan-b0851911/\n","\n","Medium: https://medium.com/@cran2367\n","\n","\n","---\n"]},{"cell_type":"markdown","metadata":{"id":"8u2e7e59jRJF"},"source":["# LSTM Autoencoders"]},{"cell_type":"markdown","metadata":{"id":"Nd3wuEGWHkyp"},"source":["In this notebook we will develop models for a rare event prediction problem. The problem used as an example here is taken from a paper manufacturing plant. \n","\n","## Problem\n","\n","Paper manufacturing is a continuous manufacturing process. Any unwanted break in the process causes significant losses.\n","\n","\n","![Paper Machine.](https://i.ibb.co/Qk301Sz/paper-machine.png)\n","\n","Paper sheets sometimes breaks in the process due to its fragile nature. Think about what happens when paper tears inside a printer. We have to open the printer, take out the stuck paper, and put the printer back together. This takes time and effort. A sheet break in paper manufacturing is similar but takes several times the time and effort.\n","\n","## Motivation\n","\n","A typical paper manufacturing plant faces at least one sheet break a day. Each such break can cost upwards of \\$25,000. Sheet breaks are known to cost \\$6-8 Million a year in a plant. Considering the worldwide paper industries, this translates to loss revenue of $3 billions every year. \n","\n","Additionally, parts of paper machines have hazardous conditions. Resuming the operations after a sheet break requires operators to face such hazards.\n","\n","Developing a solution to address this problem can save costs and reduce work hazards.\n","\n","## Solution\n","\n","Predicting a sheet break in advance can help in preventing the break. Therefore, here **we will work on developing a prediction model that does early prediction for sheet breaks**.\n","\n","## Challenges\n","\n","* Early prediction--low predictive signal.\n","* Rare event--extremely unbalanced data.\n","* High-dimensional timeseries.\n","\n","----------------"]},{"cell_type":"markdown","metadata":{"id":"ElBWj90KHqFo"},"source":["# Connection"]},{"cell_type":"code","metadata":{"id":"EC_UZmPqHmB4","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1604174812852,"user_tz":240,"elapsed":24170,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"171ff68e-c7e3-41fb-b103-28e6bb293b6c"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive', force_remount=True)\n","root_path = 'gdrive/My Drive/understanding-deep-learning/codebase'\n","import os\n","os.chdir(root_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TVMhD2fRH34v"},"source":["os.getcwd()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"l6MvUvGgIAxy"},"source":["# Libraries"]},{"cell_type":"code","metadata":{"id":"_m8snzeAH9Vl"},"source":["%matplotlib inline\n","\n","import pandas as pd\n","import numpy as np\n","\n","%tensorflow_version 2.x\n","import tensorflow as tf\n","from tensorflow.keras import optimizers\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Input \n","from tensorflow.keras.layers import Dense\n","from tensorflow.keras.layers import LSTM\n","from tensorflow.keras.layers import TimeDistributed\n","from tensorflow.keras.layers import Reshape\n","from tensorflow.keras.layers import Flatten\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","from tensorflow.keras.callbacks import TensorBoard\n","from tensorflow.python.keras import backend as K\n","\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import precision_recall_curve\n","from sklearn.metrics import recall_score\n","from sklearn.metrics import auc\n","from sklearn.metrics import roc_curve\n","from sklearn.metrics import precision_recall_fscore_support\n","from sklearn.metrics import f1_score\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# user-defined libraries\n","import utilities.datapreprocessing as dp\n","import utilities.performancemetrics as pm\n","import utilities.simpleplots as sp\n","import utilities.reconstructionperformance as rp\n","\n","from numpy.random import seed\n","seed(1)\n","\n","SEED = 123  # used to help randomly select the data points\n","DATA_SPLIT_PCT = 0.2\n","\n","from pylab import rcParams\n","rcParams['figure.figsize'] = 6, 4\n","plt.rcParams.update({'font.size': 22})"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dm2QJeEXIljC","colab":{"base_uri":"https://localhost:8080/","height":37},"executionInfo":{"status":"ok","timestamp":1604174818105,"user_tz":240,"elapsed":369,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"a7dc4674-8812-4d4e-bbb6-e4c89ca08ed6"},"source":["# Check the tensorflow version.\n","tf.__version__"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'2.3.0'"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"i204fsnsjsh2"},"source":["# Data Preparation"]},{"cell_type":"markdown","metadata":{"id":"E2XteM5KLLiV"},"source":["## Read and process data"]},{"cell_type":"markdown","metadata":{"id":"vfAum_oRLWWU"},"source":["The data is taken from https://arxiv.org/abs/1809.10717. Please use this source for any citation."]},{"cell_type":"code","metadata":{"id":"ZM-RR7qcIr5K","colab":{"base_uri":"https://localhost:8080/","height":343},"executionInfo":{"status":"ok","timestamp":1604174819847,"user_tz":240,"elapsed":2085,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"1af39277-4172-4ea1-f5bb-8138d229402d"},"source":["'''Download data here:\n","https://docs.google.com/forms/d/e/1FAIpQLSdPCkTFW1wTp6tAP1C1BhT7HWTQ60-GfCGvWs9Ca4WbR0FIdg/viewform\n","'''\n","df = pd.read_csv(\"data/processminer-sheet-break-rare-event-dataset.csv\")\n","df.head(n=5)  # visualize the data."],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>DateTime</th>\n","      <th>SheetBreak</th>\n","      <th>RSashScanAvg</th>\n","      <th>CT#1 BLADE PSI</th>\n","      <th>P4 CT#2 BLADE PSI</th>\n","      <th>Bleached GWD Flow</th>\n","      <th>ShwerTemp</th>\n","      <th>BlndStckFloTPD</th>\n","      <th>C1 BW SPREAD CD</th>\n","      <th>RS BW SPREAD CD</th>\n","      <th>C1 BW SPREAD MD</th>\n","      <th>RS BW SPREAD MD</th>\n","      <th>C1 BW SCAN AVG</th>\n","      <th>RS BW SCAN AVG</th>\n","      <th>CoatBrkFlo</th>\n","      <th>Clay Flow</th>\n","      <th>CouchLoVac</th>\n","      <th>COUCH VAC</th>\n","      <th>4PrsTopLd</th>\n","      <th>4PrsBotLod</th>\n","      <th>CalndrDrw</th>\n","      <th>2DryrDrw</th>\n","      <th>3DryrDrw</th>\n","      <th>4DryrDraw</th>\n","      <th>1PrsTopDrw</th>\n","      <th>4PrsBotDrw</th>\n","      <th>FanPmpSpd</th>\n","      <th>FlBxHdrVac</th>\n","      <th>FlatBxVac</th>\n","      <th>Grade&amp;Bwt</th>\n","      <th>UnblGWDFlo</th>\n","      <th>Hdbox pH</th>\n","      <th>HdBxLiqLvl</th>\n","      <th>TotHead\"</th>\n","      <th>HorzSlcPos</th>\n","      <th>KraftFlow</th>\n","      <th>CouchLoad</th>\n","      <th>C1MoSprdCD</th>\n","      <th>RSMoSprdCD</th>\n","      <th>C1MoSprdMD</th>\n","      <th>RSMoSprdMD</th>\n","      <th>RL MoisAct</th>\n","      <th>PrScrRjFlo</th>\n","      <th>RwBrkFlo</th>\n","      <th>RcycFbrFlo</th>\n","      <th>RetnAidFlo</th>\n","      <th>RUSH DRAG</th>\n","      <th>Rush/Drag</th>\n","      <th>SilicaFlo</th>\n","      <th>HBxSlcTemp</th>\n","      <th>SodAlumFlo</th>\n","      <th>CouchSpd</th>\n","      <th>MachSpd</th>\n","      <th>1PrsTopSpd</th>\n","      <th>4PrsBotSpd</th>\n","      <th>WtNStarFlo</th>\n","      <th>BasWgtFlo</th>\n","      <th>TMP Flow</th>\n","      <th>HBxTotHead</th>\n","      <th>TrayCons</th>\n","      <th>UpprHdTmpRL</th>\n","      <th>VertSlcPos</th>\n","      <th>EventPress</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>5/1/99 0:00</td>\n","      <td>0</td>\n","      <td>0.376665</td>\n","      <td>-4.596435</td>\n","      <td>-4.095756</td>\n","      <td>13.497687</td>\n","      <td>-0.118830</td>\n","      <td>-20.669883</td>\n","      <td>0.000732</td>\n","      <td>-0.061114</td>\n","      <td>-0.059966</td>\n","      <td>-0.038189</td>\n","      <td>0.877951</td>\n","      <td>-0.052959</td>\n","      <td>-13.306135</td>\n","      <td>0.101068</td>\n","      <td>0.041800</td>\n","      <td>0.199901</td>\n","      <td>-2.327329</td>\n","      <td>-0.944167</td>\n","      <td>3.075199</td>\n","      <td>0.123154</td>\n","      <td>-0.104334</td>\n","      <td>-0.570710</td>\n","      <td>-9.784456</td>\n","      <td>0.355960</td>\n","      <td>15.842819</td>\n","      <td>-0.451974</td>\n","      <td>-0.105282</td>\n","      <td>96</td>\n","      <td>-134.277862</td>\n","      <td>0.058726</td>\n","      <td>-0.021645</td>\n","      <td>9.366755</td>\n","      <td>0.002151</td>\n","      <td>-69.187583</td>\n","      <td>4.232571</td>\n","      <td>-0.225267</td>\n","      <td>-0.196872</td>\n","      <td>-0.072449</td>\n","      <td>-0.103732</td>\n","      <td>-0.720746</td>\n","      <td>-5.412436</td>\n","      <td>76.679042</td>\n","      <td>-0.632727</td>\n","      <td>1351.632859</td>\n","      <td>-0.657095</td>\n","      <td>-0.434947</td>\n","      <td>-108.775969</td>\n","      <td>0.084856</td>\n","      <td>10.210182</td>\n","      <td>11.295155</td>\n","      <td>29.984624</td>\n","      <td>10.091721</td>\n","      <td>0.053279</td>\n","      <td>-4.936434</td>\n","      <td>-24.590146</td>\n","      <td>18.515436</td>\n","      <td>3.473400</td>\n","      <td>0.033444</td>\n","      <td>0.953219</td>\n","      <td>0.006076</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>5/1/99 0:02</td>\n","      <td>0</td>\n","      <td>0.475720</td>\n","      <td>-4.542502</td>\n","      <td>-4.018359</td>\n","      <td>16.230658</td>\n","      <td>-0.128733</td>\n","      <td>-18.758079</td>\n","      <td>0.000732</td>\n","      <td>-0.061114</td>\n","      <td>-0.059966</td>\n","      <td>-0.038189</td>\n","      <td>0.873273</td>\n","      <td>-0.014244</td>\n","      <td>-13.306135</td>\n","      <td>0.101108</td>\n","      <td>0.041447</td>\n","      <td>0.304313</td>\n","      <td>-2.340627</td>\n","      <td>-0.939994</td>\n","      <td>3.075199</td>\n","      <td>0.123154</td>\n","      <td>-0.104334</td>\n","      <td>-0.574861</td>\n","      <td>-9.784456</td>\n","      <td>0.360160</td>\n","      <td>16.491684</td>\n","      <td>-0.450451</td>\n","      <td>-0.092430</td>\n","      <td>96</td>\n","      <td>-134.480194</td>\n","      <td>0.058759</td>\n","      <td>-0.004579</td>\n","      <td>9.350215</td>\n","      <td>0.002149</td>\n","      <td>-68.585197</td>\n","      <td>4.311490</td>\n","      <td>-0.225267</td>\n","      <td>-0.196872</td>\n","      <td>-0.059103</td>\n","      <td>-0.083895</td>\n","      <td>-0.720746</td>\n","      <td>-8.343222</td>\n","      <td>78.181598</td>\n","      <td>-0.632727</td>\n","      <td>1370.378949</td>\n","      <td>-0.875628</td>\n","      <td>-1.125819</td>\n","      <td>-108.848967</td>\n","      <td>0.085146</td>\n","      <td>12.534340</td>\n","      <td>11.290761</td>\n","      <td>29.984624</td>\n","      <td>10.095871</td>\n","      <td>0.062801</td>\n","      <td>-4.937179</td>\n","      <td>-32.413266</td>\n","      <td>22.760065</td>\n","      <td>2.682933</td>\n","      <td>0.033536</td>\n","      <td>1.090502</td>\n","      <td>0.006083</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5/1/99 0:04</td>\n","      <td>0</td>\n","      <td>0.363848</td>\n","      <td>-4.681394</td>\n","      <td>-4.353147</td>\n","      <td>14.127997</td>\n","      <td>-0.138636</td>\n","      <td>-17.836632</td>\n","      <td>0.010803</td>\n","      <td>-0.061114</td>\n","      <td>-0.030057</td>\n","      <td>-0.018352</td>\n","      <td>1.004910</td>\n","      <td>0.065150</td>\n","      <td>-9.619596</td>\n","      <td>0.101148</td>\n","      <td>0.041095</td>\n","      <td>0.252839</td>\n","      <td>-2.353925</td>\n","      <td>-0.935824</td>\n","      <td>3.075199</td>\n","      <td>0.123154</td>\n","      <td>-0.104334</td>\n","      <td>-0.579013</td>\n","      <td>-9.784456</td>\n","      <td>0.364356</td>\n","      <td>15.972885</td>\n","      <td>-0.448927</td>\n","      <td>-0.097144</td>\n","      <td>96</td>\n","      <td>-133.946587</td>\n","      <td>0.058791</td>\n","      <td>-0.084658</td>\n","      <td>9.037409</td>\n","      <td>0.002148</td>\n","      <td>-67.838187</td>\n","      <td>4.809914</td>\n","      <td>-0.225267</td>\n","      <td>-0.186801</td>\n","      <td>-0.048696</td>\n","      <td>-0.073823</td>\n","      <td>-0.720746</td>\n","      <td>-1.085166</td>\n","      <td>79.684154</td>\n","      <td>-0.632727</td>\n","      <td>1368.123089</td>\n","      <td>-0.037775</td>\n","      <td>-0.519541</td>\n","      <td>-109.086577</td>\n","      <td>0.085436</td>\n","      <td>18.582893</td>\n","      <td>11.286366</td>\n","      <td>29.984624</td>\n","      <td>10.100265</td>\n","      <td>0.072322</td>\n","      <td>-4.937924</td>\n","      <td>-34.183774</td>\n","      <td>27.004663</td>\n","      <td>3.537487</td>\n","      <td>0.033629</td>\n","      <td>1.840540</td>\n","      <td>0.006090</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>5/1/99 0:06</td>\n","      <td>0</td>\n","      <td>0.301590</td>\n","      <td>-4.758934</td>\n","      <td>-4.023612</td>\n","      <td>13.161566</td>\n","      <td>-0.148142</td>\n","      <td>-18.517601</td>\n","      <td>0.002075</td>\n","      <td>-0.061114</td>\n","      <td>-0.019986</td>\n","      <td>-0.008280</td>\n","      <td>0.930037</td>\n","      <td>-0.067199</td>\n","      <td>-15.196531</td>\n","      <td>0.101188</td>\n","      <td>0.040742</td>\n","      <td>0.072873</td>\n","      <td>-2.367223</td>\n","      <td>-0.931651</td>\n","      <td>3.075199</td>\n","      <td>0.123154</td>\n","      <td>-0.104334</td>\n","      <td>-0.583165</td>\n","      <td>-9.784456</td>\n","      <td>0.368556</td>\n","      <td>15.608688</td>\n","      <td>-0.447404</td>\n","      <td>-0.160073</td>\n","      <td>96</td>\n","      <td>-134.002586</td>\n","      <td>0.058824</td>\n","      <td>-0.055118</td>\n","      <td>9.020625</td>\n","      <td>0.002146</td>\n","      <td>-67.091148</td>\n","      <td>5.308343</td>\n","      <td>-0.225267</td>\n","      <td>-0.186801</td>\n","      <td>-0.047017</td>\n","      <td>-0.063752</td>\n","      <td>-0.720746</td>\n","      <td>6.172891</td>\n","      <td>81.186702</td>\n","      <td>-0.632727</td>\n","      <td>1365.691449</td>\n","      <td>-0.987410</td>\n","      <td>0.674524</td>\n","      <td>-109.562774</td>\n","      <td>0.085726</td>\n","      <td>17.719032</td>\n","      <td>11.281972</td>\n","      <td>29.984624</td>\n","      <td>10.104660</td>\n","      <td>0.081600</td>\n","      <td>-4.938669</td>\n","      <td>-35.954281</td>\n","      <td>21.672449</td>\n","      <td>3.986095</td>\n","      <td>0.033721</td>\n","      <td>2.554880</td>\n","      <td>0.006097</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5/1/99 0:08</td>\n","      <td>0</td>\n","      <td>0.265578</td>\n","      <td>-4.749928</td>\n","      <td>-4.333150</td>\n","      <td>15.267340</td>\n","      <td>-0.155314</td>\n","      <td>-17.505913</td>\n","      <td>0.000732</td>\n","      <td>-0.061114</td>\n","      <td>-0.030057</td>\n","      <td>-0.008280</td>\n","      <td>0.828410</td>\n","      <td>-0.018472</td>\n","      <td>-14.609266</td>\n","      <td>0.101229</td>\n","      <td>0.040390</td>\n","      <td>0.171033</td>\n","      <td>-2.380521</td>\n","      <td>-0.927478</td>\n","      <td>3.075199</td>\n","      <td>0.123154</td>\n","      <td>-0.104334</td>\n","      <td>-0.587316</td>\n","      <td>-9.784456</td>\n","      <td>0.372756</td>\n","      <td>15.606125</td>\n","      <td>-0.445880</td>\n","      <td>-0.131630</td>\n","      <td>96</td>\n","      <td>-133.145706</td>\n","      <td>0.058856</td>\n","      <td>-0.153851</td>\n","      <td>9.344233</td>\n","      <td>0.002145</td>\n","      <td>-65.991813</td>\n","      <td>5.806771</td>\n","      <td>-0.225267</td>\n","      <td>-0.186801</td>\n","      <td>-0.057088</td>\n","      <td>-0.063752</td>\n","      <td>-0.720746</td>\n","      <td>-3.379599</td>\n","      <td>82.689258</td>\n","      <td>-0.632727</td>\n","      <td>1363.257859</td>\n","      <td>-0.238444</td>\n","      <td>-0.063044</td>\n","      <td>-110.038909</td>\n","      <td>0.086016</td>\n","      <td>16.855202</td>\n","      <td>11.277577</td>\n","      <td>29.984624</td>\n","      <td>10.109054</td>\n","      <td>0.091121</td>\n","      <td>-4.939414</td>\n","      <td>-37.724789</td>\n","      <td>21.907251</td>\n","      <td>3.601573</td>\n","      <td>0.033777</td>\n","      <td>1.410494</td>\n","      <td>0.006105</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      DateTime  SheetBreak  RSashScanAvg  ...  UpprHdTmpRL  VertSlcPos  EventPress\n","0  5/1/99 0:00           0      0.376665  ...     0.953219    0.006076           0\n","1  5/1/99 0:02           0      0.475720  ...     1.090502    0.006083           0\n","2  5/1/99 0:04           0      0.363848  ...     1.840540    0.006090           0\n","3  5/1/99 0:06           0      0.301590  ...     2.554880    0.006097           0\n","4  5/1/99 0:08           0      0.265578  ...     1.410494    0.006105           0\n","\n","[5 rows x 63 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"TpXyxgnkLZhB"},"source":["# Convert Categorical column to hot dummy columns\n","hotencoding1 = pd.get_dummies(df['Grade&Bwt'])\n","hotencoding1 = hotencoding1.add_prefix('grade_')\n","hotencoding2 = pd.get_dummies(df['EventPress'])\n","hotencoding2 = hotencoding2.add_prefix('eventpress_')\n","\n","df=df.drop(['Grade&Bwt', 'EventPress'], axis=1)\n","\n","df=pd.concat([df, hotencoding1, hotencoding2], axis=1)\n","\n","# Rename response column name for ease of understanding\n","df=df.rename(columns={'SheetBreak':'y'})\n","\n","# Shift the response column y by 2 rows to do a 4-min ahead prediction.\n","df = dp.curve_shift(df, shift_by=-2)\n","\n","# Sort by time and drop the time column.\n","df['DateTime'] = pd.to_datetime(df.DateTime)\n","df = df.sort_values(by='DateTime')\n","df = df.drop(['DateTime'], axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7GGllJp3vQVp"},"source":["input_X = df.loc[:, df.columns != 'y'].values  # converts df to numpy array\n","input_y = df['y'].values"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"oHOIMk0FL-EN"},"source":["\n","## Temporalization \n","\n","Temporal models, such as LSTM and CNN, requires temporalization. Data temporalization means creating block sequences where each block is a time window. In temporalization, each block is mapped with a corresponding response label. In temporalization process, we will create 3-dimensional arrays of shape: `(samples, timesteps, features)`. Samples is the number of data points. Timesteps is the size of a block, i.e. the time window. This is sometimes called the lookback period. The features is the number of features in a sample."]},{"cell_type":"code","metadata":{"id":"O6War7v0Luhg"},"source":["# Temporalize the data\n","lookback = 5\n","X, y = dp.temporalize(X=input_X, y=input_y, lookback=lookback)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"l6EpvEC4Mw9N"},"source":["## Split into train, valid, and test"]},{"cell_type":"code","metadata":{"id":"s24c7L1nMDwF"},"source":["X_train, X_test, y_train, y_test = train_test_split(np.array(X),\n","                                                    np.array(y),\n","                                                    test_size=DATA_SPLIT_PCT,\n","                                                    random_state=SEED)\n","X_train, X_valid, y_train, y_valid = train_test_split(X_train,\n","                                                      y_train,\n","                                                      test_size=DATA_SPLIT_PCT,\n","                                                      random_state=SEED)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"O_El1abiM0v4","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1604174849106,"user_tz":240,"elapsed":721,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"aafdf3b0-62f2-4510-ae72-6970e4d448d7"},"source":["X_train.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(13002, 5, 69)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"vE03z36ZM7Ym"},"source":["# Rare Event Prediction as Anomaly Detection\n","\n","If an event is extremely rare, we can use Anomaly Detection approach to predict the rare event. In this approach, the rare event is treated as anomalies. The model is trained to detect the anomaly. \n","\n","The Anomaly Detection approach works using a Reconstruction Model. The approach is:\n","\n","- divide the data into two parts: majority class (negatively labeled) and minority class (positively labeled).\n","\n","- The majority class is treated as *normal* state of the process. A normal state is when the process is eventless.\n","\n","- Train a reconstruction model on the normal state samples (i.e. ignore the positively labeled data).\n","\n","- A well-trained reconstruction model will be able to accurately reconstruct a new sample if it belongs to the **normal state**, i.e. have a **small reconstruction error**.\n","\n","- However, a sample from a **rare-event** would be *abnormal* for the model. The reconstruction model will struggle reconstructing it, i.e. we will have **large reconstruction error**.\n","\n","- We catch such high reconstruction error instances and label them as a rare event prediction.\n","\n","# Challenges\n","There are some critical challenges in this approach that we need to keep into considerations. High-dimensional time-series, such as the sheet break problem here, can be both,\n","\n","a. non-stationary, and\n","\n","b. contain outliers.\n","\n","Non-stationarity can be dealt with by doing an online model training, i.e. periodically retrain the model with the most recent window of data. This works under the assumption that the process is stationary within the window. This approach is recommended to be followed when deploying this system for real-time operations. Here we will skip it.\n","\n","Handling outliers could be more challenging. Identifying outliers in a high-dimensional time-series is non-trivial. One possible approach is using PCA and detecting outliers in a reduced dimension of the PC space. Or we can train a reconstruction model, remove the training samples that have high reconstruction error, and iterate until we do not have such training samples."]},{"cell_type":"markdown","metadata":{"id":"p7Ckx0euNG-Y"},"source":["# Extract ''Normal'' samples\n","\n","As part of the anomaly detection approach, we require to extract the samples that belong to the ''normal'' state, i.e. the samples with response as 1."]},{"cell_type":"code","metadata":{"id":"E2Wta6liM2zA"},"source":["X_train_y0 = X_train[y_train==0]  # Normal state samples.\n","X_train_y1 = X_train[y_train==1]  # Anomalous samples.\n","\n","X_valid_y0 = X_valid[y_valid==0]  # Normal state samples.\n","X_valid_y1 = X_valid[y_valid==1]  # Anomalous samples."],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"nUYK8zsqNJ6z","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1604174849108,"user_tz":240,"elapsed":703,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"5888f821-5304-42c0-8a8a-04ab9c5c1e74"},"source":["X_train_y0.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(12823, 5, 69)"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"rudYh1HANMXI"},"source":["# Initialize a scaler using the training data.\n","scaler = StandardScaler().fit(dp.flatten(X_train_y0))\n","\n","X_train_y0_scaled = dp.scale(X_train_y0, scaler)\n","X_train_y1_scaled = dp.scale(X_train_y1, scaler)\n","X_train_scaled = dp.scale(X_train, scaler)\n","\n","X_valid_scaled = dp.scale(X_valid, scaler)\n","X_valid_y0_scaled = dp.scale(X_valid_y0, scaler)\n","\n","X_test_scaled = dp.scale(X_test, scaler)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uj-LI7sXNObk"},"source":["TIMESTEPS =  X_train_y0_scaled.shape[1]\n","N_FEATURES =  X_train_y0_scaled.shape[2]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"I0Q3t2TXeAzI"},"source":["# Sparse LSTM Autoencoder"]},{"cell_type":"code","metadata":{"id":"fI0LE7UyhMgu","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1604174852859,"user_tz":240,"elapsed":4426,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"7e026802-7395-43bc-8ee9-78d9dce4dcca"},"source":["# LSTM Autoencoder Model\n","# Encoder\n","\n","inputs = Input(shape=(TIMESTEPS, \n","                      N_FEATURES), \n","               name='encoder-input')\n","\n","x = LSTM(units=N_FEATURES, \n","         activation='tanh', \n","         return_sequences=True, \n","         name='encoder-lstm')(inputs)\n","\n","# Shape info needed to build Decoder Model\n","e_shape = tf.keras.backend.int_shape(x)\n","latent_dim = e_shape[1] * e_shape[2]\n","\n","# Generate the latent vector\n","x = Flatten(name='flatten')(x)\n","latent = Dense(units=latent_dim,\n","               activation='linear',\n","               activity_regularizer=\n","                  tf.keras.regularizers.L1(l1=0.01),\n","               name='encoded-vector')(x)\n","\n","# Instantiate Encoder Model\n","encoder = Model(inputs=inputs, \n","                outputs=latent, \n","                name='encoder')\n","encoder.summary()\n","\n","# Decoder\n","latent_inputs = Input(shape=(latent_dim,), \n","                      name='decoder_input')\n","\n","x = Reshape((e_shape[1], e_shape[2]), \n","            name='reshape')(latent_inputs)\n","\n","x = LSTM(units=N_FEATURES, \n","         activation='tanh', \n","         return_sequences=True, \n","         name='decoder-lstm')(x)\n","\n","output = Dense(units=N_FEATURES, \n","               activation='linear',\n","               name='decoded-sequences')(x)\n","\n","# Instantiate Decoder Model\n","decoder = Model(inputs=latent_inputs, \n","                outputs=output, \n","                name='decoder')\n","decoder.summary()\n","\n","# Autoencoder = Encoder + Decoder\n","# Approach A: Instantiate Autoencoder Model using Sequential\n","# autoencoder = Sequential()\n","# autoencoder.add(encoder)\n","# autoencoder.add(decoder)\n","# autoencoder.summary()\n","\n","# Approach B: instantiate Autoencoder Model using Input and Output\n","autoencoder = Model(inputs=inputs, \n","                    outputs=decoder(inputs=encoder(inputs)), \n","                    name='autoencoder')\n","autoencoder.summary()\n","\n","# Plot the network structure\n","tf.keras.utils.plot_model(model=autoencoder, \n","                          show_shapes=False, \n","                          dpi=160)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"encoder\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","encoder-input (InputLayer)   [(None, 5, 69)]           0         \n","_________________________________________________________________\n","encoder-lstm (LSTM)          (None, 5, 69)             38364     \n","_________________________________________________________________\n","flatten (Flatten)            (None, 345)               0         \n","_________________________________________________________________\n","encoded-vector (Dense)       (None, 345)               119370    \n","=================================================================\n","Total params: 157,734\n","Trainable params: 157,734\n","Non-trainable params: 0\n","_________________________________________________________________\n","Model: \"decoder\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","decoder_input (InputLayer)   [(None, 345)]             0         \n","_________________________________________________________________\n","reshape (Reshape)            (None, 5, 69)             0         \n","_________________________________________________________________\n","decoder-lstm (LSTM)          (None, 5, 69)             38364     \n","_________________________________________________________________\n","decoded-sequences (Dense)    (None, 5, 69)             4830      \n","=================================================================\n","Total params: 43,194\n","Trainable params: 43,194\n","Non-trainable params: 0\n","_________________________________________________________________\n","Model: \"autoencoder\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","encoder-input (InputLayer)   [(None, 5, 69)]           0         \n","_________________________________________________________________\n","encoder (Functional)         (None, 345)               157734    \n","_________________________________________________________________\n","decoder (Functional)         (None, 5, 69)             43194     \n","=================================================================\n","Total params: 200,928\n","Trainable params: 200,928\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"},{"output_type":"execute_result","data":{"image/png":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"_KQmZDOLjUQo","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1604175071355,"user_tz":240,"elapsed":222905,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"5fa516a2-7aa9-49f1-9cf5-466a2ae5f553"},"source":["autoencoder.compile(loss='mse', \n","              optimizer='adam')\n","\n","history = autoencoder.fit(x=X_train_y0_scaled, \n","                          y=X_train_y0_scaled, \n","                          epochs=100, \n","                          batch_size=128, \n","                          validation_data=(X_valid_y0_scaled, \n","                                           X_valid_y0_scaled),\n","                          verbose=2).history"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","101/101 - 3s - loss: 0.9544 - val_loss: 0.6502\n","Epoch 2/100\n","101/101 - 2s - loss: 0.5547 - val_loss: 0.4644\n","Epoch 3/100\n","101/101 - 2s - loss: 0.4256 - val_loss: 0.3792\n","Epoch 4/100\n","101/101 - 2s - loss: 0.3490 - val_loss: 0.3118\n","Epoch 5/100\n","101/101 - 2s - loss: 0.2946 - val_loss: 0.2725\n","Epoch 6/100\n","101/101 - 2s - loss: 0.2655 - val_loss: 0.2465\n","Epoch 7/100\n","101/101 - 2s - loss: 0.2380 - val_loss: 0.2273\n","Epoch 8/100\n","101/101 - 2s - loss: 0.2197 - val_loss: 0.2117\n","Epoch 9/100\n","101/101 - 2s - loss: 0.2069 - val_loss: 0.2025\n","Epoch 10/100\n","101/101 - 2s - loss: 0.1946 - val_loss: 0.1896\n","Epoch 11/100\n","101/101 - 2s - loss: 0.1845 - val_loss: 0.1810\n","Epoch 12/100\n","101/101 - 2s - loss: 0.1730 - val_loss: 0.1695\n","Epoch 13/100\n","101/101 - 2s - loss: 0.1647 - val_loss: 0.1633\n","Epoch 14/100\n","101/101 - 2s - loss: 0.1574 - val_loss: 0.1559\n","Epoch 15/100\n","101/101 - 2s - loss: 0.1513 - val_loss: 0.1488\n","Epoch 16/100\n","101/101 - 2s - loss: 0.1448 - val_loss: 0.1442\n","Epoch 17/100\n","101/101 - 2s - loss: 0.1404 - val_loss: 0.1378\n","Epoch 18/100\n","101/101 - 2s - loss: 0.1339 - val_loss: 0.1333\n","Epoch 19/100\n","101/101 - 2s - loss: 0.1319 - val_loss: 0.1299\n","Epoch 20/100\n","101/101 - 2s - loss: 0.1261 - val_loss: 0.1250\n","Epoch 21/100\n","101/101 - 2s - loss: 0.1219 - val_loss: 0.1212\n","Epoch 22/100\n","101/101 - 2s - loss: 0.1187 - val_loss: 0.1192\n","Epoch 23/100\n","101/101 - 2s - loss: 0.1149 - val_loss: 0.1156\n","Epoch 24/100\n","101/101 - 2s - loss: 0.1141 - val_loss: 0.1128\n","Epoch 25/100\n","101/101 - 2s - loss: 0.1091 - val_loss: 0.1103\n","Epoch 26/100\n","101/101 - 2s - loss: 0.1075 - val_loss: 0.1082\n","Epoch 27/100\n","101/101 - 2s - loss: 0.1098 - val_loss: 0.1078\n","Epoch 28/100\n","101/101 - 2s - loss: 0.1036 - val_loss: 0.1025\n","Epoch 29/100\n","101/101 - 2s - loss: 0.0999 - val_loss: 0.1002\n","Epoch 30/100\n","101/101 - 2s - loss: 0.0993 - val_loss: 0.0991\n","Epoch 31/100\n","101/101 - 2s - loss: 0.0970 - val_loss: 0.0975\n","Epoch 32/100\n","101/101 - 2s - loss: 0.0954 - val_loss: 0.0959\n","Epoch 33/100\n","101/101 - 2s - loss: 0.0929 - val_loss: 0.0941\n","Epoch 34/100\n","101/101 - 2s - loss: 0.0914 - val_loss: 0.0955\n","Epoch 35/100\n","101/101 - 2s - loss: 0.0918 - val_loss: 0.0929\n","Epoch 36/100\n","101/101 - 2s - loss: 0.0884 - val_loss: 0.0888\n","Epoch 37/100\n","101/101 - 2s - loss: 0.0863 - val_loss: 0.0872\n","Epoch 38/100\n","101/101 - 2s - loss: 0.0842 - val_loss: 0.0846\n","Epoch 39/100\n","101/101 - 2s - loss: 0.0822 - val_loss: 0.0833\n","Epoch 40/100\n","101/101 - 2s - loss: 0.0809 - val_loss: 0.0801\n","Epoch 41/100\n","101/101 - 2s - loss: 0.0776 - val_loss: 0.0787\n","Epoch 42/100\n","101/101 - 3s - loss: 0.0762 - val_loss: 0.0768\n","Epoch 43/100\n","101/101 - 3s - loss: 0.0743 - val_loss: 0.0773\n","Epoch 44/100\n","101/101 - 3s - loss: 0.0739 - val_loss: 0.0750\n","Epoch 45/100\n","101/101 - 3s - loss: 0.0717 - val_loss: 0.0711\n","Epoch 46/100\n","101/101 - 2s - loss: 0.0689 - val_loss: 0.0699\n","Epoch 47/100\n","101/101 - 2s - loss: 0.0670 - val_loss: 0.0676\n","Epoch 48/100\n","101/101 - 2s - loss: 0.0646 - val_loss: 0.0655\n","Epoch 49/100\n","101/101 - 2s - loss: 0.0640 - val_loss: 0.0646\n","Epoch 50/100\n","101/101 - 2s - loss: 0.0623 - val_loss: 0.0625\n","Epoch 51/100\n","101/101 - 2s - loss: 0.0601 - val_loss: 0.0612\n","Epoch 52/100\n","101/101 - 2s - loss: 0.0593 - val_loss: 0.0589\n","Epoch 53/100\n","101/101 - 2s - loss: 0.0573 - val_loss: 0.0577\n","Epoch 54/100\n","101/101 - 2s - loss: 0.0560 - val_loss: 0.0565\n","Epoch 55/100\n","101/101 - 2s - loss: 0.0543 - val_loss: 0.0543\n","Epoch 56/100\n","101/101 - 2s - loss: 0.0530 - val_loss: 0.0528\n","Epoch 57/100\n","101/101 - 2s - loss: 0.0503 - val_loss: 0.0495\n","Epoch 58/100\n","101/101 - 2s - loss: 0.0475 - val_loss: 0.0464\n","Epoch 59/100\n","101/101 - 2s - loss: 0.0446 - val_loss: 0.0441\n","Epoch 60/100\n","101/101 - 2s - loss: 0.0425 - val_loss: 0.0423\n","Epoch 61/100\n","101/101 - 2s - loss: 0.0410 - val_loss: 0.0432\n","Epoch 62/100\n","101/101 - 2s - loss: 0.0401 - val_loss: 0.0389\n","Epoch 63/100\n","101/101 - 2s - loss: 0.0374 - val_loss: 0.0377\n","Epoch 64/100\n","101/101 - 2s - loss: 0.0365 - val_loss: 0.0377\n","Epoch 65/100\n","101/101 - 2s - loss: 0.0369 - val_loss: 0.0379\n","Epoch 66/100\n","101/101 - 2s - loss: 0.0371 - val_loss: 0.0355\n","Epoch 67/100\n","101/101 - 2s - loss: 0.0346 - val_loss: 0.0353\n","Epoch 68/100\n","101/101 - 2s - loss: 0.0337 - val_loss: 0.0345\n","Epoch 69/100\n","101/101 - 2s - loss: 0.0329 - val_loss: 0.0334\n","Epoch 70/100\n","101/101 - 2s - loss: 0.0322 - val_loss: 0.0331\n","Epoch 71/100\n","101/101 - 2s - loss: 0.0322 - val_loss: 0.0320\n","Epoch 72/100\n","101/101 - 2s - loss: 0.0309 - val_loss: 0.0316\n","Epoch 73/100\n","101/101 - 2s - loss: 0.0300 - val_loss: 0.0318\n","Epoch 74/100\n","101/101 - 2s - loss: 0.0293 - val_loss: 0.0298\n","Epoch 75/100\n","101/101 - 2s - loss: 0.0309 - val_loss: 0.0311\n","Epoch 76/100\n","101/101 - 2s - loss: 0.0288 - val_loss: 0.0290\n","Epoch 77/100\n","101/101 - 2s - loss: 0.0273 - val_loss: 0.0278\n","Epoch 78/100\n","101/101 - 2s - loss: 0.0275 - val_loss: 0.0273\n","Epoch 79/100\n","101/101 - 2s - loss: 0.0265 - val_loss: 0.0270\n","Epoch 80/100\n","101/101 - 2s - loss: 0.0257 - val_loss: 0.0280\n","Epoch 81/100\n","101/101 - 2s - loss: 0.0270 - val_loss: 0.0272\n","Epoch 82/100\n","101/101 - 2s - loss: 0.0255 - val_loss: 0.0267\n","Epoch 83/100\n","101/101 - 2s - loss: 0.0248 - val_loss: 0.0267\n","Epoch 84/100\n","101/101 - 2s - loss: 0.0247 - val_loss: 0.0249\n","Epoch 85/100\n","101/101 - 2s - loss: 0.0242 - val_loss: 0.0259\n","Epoch 86/100\n","101/101 - 2s - loss: 0.0243 - val_loss: 0.0257\n","Epoch 87/100\n","101/101 - 2s - loss: 0.0244 - val_loss: 0.0281\n","Epoch 88/100\n","101/101 - 2s - loss: 0.0248 - val_loss: 0.0237\n","Epoch 89/100\n","101/101 - 2s - loss: 0.0222 - val_loss: 0.0235\n","Epoch 90/100\n","101/101 - 2s - loss: 0.0225 - val_loss: 0.0235\n","Epoch 91/100\n","101/101 - 2s - loss: 0.0230 - val_loss: 0.0261\n","Epoch 92/100\n","101/101 - 2s - loss: 0.0225 - val_loss: 0.0234\n","Epoch 93/100\n","101/101 - 2s - loss: 0.0227 - val_loss: 0.0234\n","Epoch 94/100\n","101/101 - 2s - loss: 0.0213 - val_loss: 0.0218\n","Epoch 95/100\n","101/101 - 2s - loss: 0.0218 - val_loss: 0.0222\n","Epoch 96/100\n","101/101 - 2s - loss: 0.0210 - val_loss: 0.0214\n","Epoch 97/100\n","101/101 - 2s - loss: 0.0204 - val_loss: 0.0220\n","Epoch 98/100\n","101/101 - 2s - loss: 0.0204 - val_loss: 0.0223\n","Epoch 99/100\n","101/101 - 2s - loss: 0.0203 - val_loss: 0.0231\n","Epoch 100/100\n","101/101 - 2s - loss: 0.0237 - val_loss: 0.0220\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IdGfO2QNhNO4","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1604175072110,"user_tz":240,"elapsed":223644,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"86f11630-46cb-47a1-bab1-9f6527a62c07"},"source":["plt, fig = sp.plot_metric(history, metric='loss')\n","fig.savefig('lstm-autoencoder-loss.pdf', bbox_inches='tight')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"N3Y45vdldg_G","colab":{"base_uri":"https://localhost:8080/","height":68},"executionInfo":{"status":"ok","timestamp":1604175072110,"user_tz":240,"elapsed":223629,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"97b5c350-b10b-41af-a0b1-0ca49df8f2a8"},"source":["print('\\nLayer 1:', autoencoder.layers[1].name,\n","      '\\nLayer 2:', autoencoder.layers[2].name,)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\n","Layer 1: encoder \n","Layer 2: decoder\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JSHdQEFEelm6","colab":{"base_uri":"https://localhost:8080/","height":595},"executionInfo":{"status":"ok","timestamp":1604175073356,"user_tz":240,"elapsed":224857,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"e0839077-5559-4c29-d62b-3787e969cb5d"},"source":["error_vs_class_valid = rp.reconstructionerror_vs_class(model=autoencoder, \n","                                                       sample=X_valid_scaled, \n","                                                       y=y_valid)\n","\n","plt, fig = rp.error_boxplot(error_vs_class=error_vs_class_valid)\n","# fig.savefig('lstm-autoencoder-master-boxplot.pdf', bbox_inches='tight')\n","\n","plt, fig = rp.error_boxplot(error_vs_class=error_vs_class_valid, ylim=[0.,0.1])\n","# fig.savefig('lstm-autoencoder-zoomed-boxplot.pdf', bbox_inches='tight')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"GMRFaKAVetl6","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1604175073653,"user_tz":240,"elapsed":225139,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"759a2e29-452d-45b2-a47e-487d1c377dc3"},"source":["errors_valid0 = rp.class_errors(error_vs_class=error_vs_class_valid, \n","                                label=0)\n","errors_valid1 = rp.class_errors(error_vs_class=error_vs_class_valid, \n","                                label=1)\n","rp.robust_stat_difference(z1=errors_valid0, z2=errors_valid1)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.8585337323127766"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"JN6miz7yewZj","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1604175075374,"user_tz":240,"elapsed":226844,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"077c5801-0cbf-48ad-e459-49ea7a70ac2e"},"source":["error_vs_class_test = rp.reconstructionerror_vs_class(model=autoencoder, \n","                                                      sample=X_test_scaled, \n","                                                      y=y_test)\n","conf_matrix, fig = rp.model_confusion_matrix(\n","    error_vs_class=error_vs_class_test, \n","    threshold=errors_valid1.quantile(0.50))\n","fig.savefig('lstm-autoencoder-baseline-conf-matrix.pdf', bbox_inches='tight')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 288x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"VgpvRpaqI1Di"},"source":["# LSTM (Auto)encoded Classifier"]},{"cell_type":"markdown","metadata":{"id":"NqeDJaG_JJ0g"},"source":["## Encoding Model"]},{"cell_type":"code","metadata":{"id":"ejA8Hb4J9hA-","colab":{"base_uri":"https://localhost:8080/","height":439},"executionInfo":{"status":"ok","timestamp":1604175076799,"user_tz":240,"elapsed":228250,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"a8cca3ef-4c10-4a54-ff2b-41c73f208a30"},"source":["# Encodings of LSTM Autoencoder\n","encoder_model = Model(inputs=autoencoder.inputs, \n","                      outputs=autoencoder.get_layer('encoder').output)\n","X_train_y0_encoded_features = pd.DataFrame(encoder_model.predict(X_train_y0_scaled))\n","X_train_y0_encoded_features.round(3)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style 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<td>-0.000</td>\n","      <td>0.005</td>\n","      <td>0.007</td>\n","      <td>-0.003</td>\n","      <td>-0.009</td>\n","      <td>-0.000</td>\n","      <td>0.001</td>\n","      <td>0.001</td>\n","      <td>0.012</td>\n","      <td>-0.003</td>\n","      <td>-0.004</td>\n","      <td>0.013</td>\n","      <td>0.008</td>\n","      <td>-0.007</td>\n","      <td>0.003</td>\n","      <td>0.001</td>\n","      <td>0.012</td>\n","      <td>-0.019</td>\n","      <td>-0.170</td>\n","      <td>-0.004</td>\n","      <td>-0.008</td>\n","      <td>-0.009</td>\n","      <td>0.001</td>\n","      <td>0.009</td>\n","      <td>-0.013</td>\n","      <td>-0.002</td>\n","      <td>0.002</td>\n","      <td>0.008</td>\n","      <td>0.000</td>\n","      <td>0.050</td>\n","      <td>0.001</td>\n","      <td>-0.002</td>\n","      <td>-0.000</td>\n","      <td>-0.028</td>\n","      <td>-0.000</td>\n","      <td>-0.002</td>\n","      <td>0.008</td>\n","      <td>0.007</td>\n","      <td>0.004</td>\n","      <td>0.004</td>\n","      <td>...</td>\n","      <td>0.001</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.002</td>\n","      <td>0.0</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>-0.001</td>\n","      <td>0.0</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>0.004</td>\n","      <td>-0.001</td>\n","      <td>-0.013</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.003</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>-0.0</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>-0.000</td>\n","    </tr>\n","    <tr>\n","      <th>12820</th>\n","      <td>-0.000</td>\n","      <td>-0.006</td>\n","      <td>-0.005</td>\n","      <td>0.011</td>\n","      <td>-0.006</td>\n","      <td>0.002</td>\n","      <td>-0.005</td>\n","      <td>-0.003</td>\n","      <td>-0.014</td>\n","      <td>0.003</td>\n","      <td>-0.002</td>\n","      <td>0.002</td>\n","      <td>-0.000</td>\n","      <td>-0.005</td>\n","      <td>-0.022</td>\n","      <td>0.000</td>\n","      <td>-0.003</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>-0.001</td>\n","      <td>-0.007</td>\n","      <td>0.005</td>\n","      <td>-0.000</td>\n","      <td>-0.029</td>\n","      <td>0.009</td>\n","      <td>0.014</td>\n","      <td>-0.004</td>\n","      <td>0.000</td>\n","      <td>0.003</td>\n","      <td>0.008</td>\n","      <td>-0.002</td>\n","      <td>0.000</td>\n","      <td>-0.024</td>\n","      <td>0.001</td>\n","      <td>0.002</td>\n","      <td>0.004</td>\n","      <td>0.001</td>\n","      <td>-0.005</td>\n","      <td>0.004</td>\n","      <td>...</td>\n","      <td>0.000</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>0.0</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>-0.000</td>\n","      <td>-0.0</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>0.001</td>\n","      <td>0.007</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>0.008</td>\n","      <td>-0.004</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      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<td>0.037</td>\n","      <td>0.007</td>\n","      <td>0.007</td>\n","      <td>0.000</td>\n","      <td>0.003</td>\n","      <td>0.000</td>\n","      <td>-0.007</td>\n","      <td>0.005</td>\n","      <td>-0.003</td>\n","      <td>0.009</td>\n","      <td>0.000</td>\n","      <td>...</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>0.0</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.0</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>-0.000</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>0.001</td>\n","      <td>-0.004</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>0.002</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","      <td>-0.0</td>\n","      <td>-0.000</td>\n","      <td>-0.001</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>0.000</td>\n","    </tr>\n","    <tr>\n","      <th>12822</th>\n","      <td>-0.000</td>\n","      <td>-0.004</td>\n","      <td>-0.009</td>\n","      <td>0.006</td>\n","      <td>0.002</td>\n","      <td>0.001</td>\n","      <td>-0.006</td>\n","      <td>0.012</td>\n","      <td>-0.011</td>\n","      <td>-0.003</td>\n","      <td>-0.017</td>\n","      <td>-0.000</td>\n","      <td>0.004</td>\n","      <td>0.009</td>\n","      <td>0.003</td>\n","      <td>-0.001</td>\n","      <td>0.010</td>\n","      <td>-0.000</td>\n","      <td>0.003</td>\n","      <td>-0.015</td>\n","      <td>-0.004</td>\n","      <td>0.009</td>\n","      <td>-0.003</td>\n","      <td>-0.003</td>\n","      <td>-0.005</td>\n","      <td>0.001</td>\n","      <td>-0.036</td>\n","      <td>0.003</td>\n","      <td>0.000</td>\n","      <td>0.040</td>\n","      <td>0.053</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","      <td>0.023</td>\n","      <td>-0.000</td>\n","      <td>0.008</td>\n","      <td>0.017</td>\n","      <td>0.000</td>\n","      <td>-0.010</td>\n","      <td>0.003</td>\n","      <td>...</td>\n","      <td>-0.001</td>\n","      <td>0.001</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.0</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>-0.001</td>\n","      <td>0.001</td>\n","      <td>-0.002</td>\n","      <td>0.000</td>\n","      <td>0.0</td>\n","      <td>0.000</td>\n","      <td>0.001</td>\n","      <td>0.000</td>\n","      <td>-0.004</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>0.003</td>\n","      <td>-0.000</td>\n","      <td>0.004</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>0.002</td>\n","      <td>0.005</td>\n","      <td>-0.000</td>\n","      <td>-0.002</td>\n","      <td>-0.000</td>\n","      <td>-0.000</td>\n","      <td>-0.002</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.0</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.001</td>\n","      <td>0.000</td>\n","      <td>0.000</td>\n","      <td>-0.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>12823 rows × 345 columns</p>\n","</div>"],"text/plain":["         0      1      2      3      4    ...    340    341    342    343    344\n","0     -0.000  0.079 -0.001  0.004 -0.004  ...  0.000 -0.000 -0.000 -0.000 -0.000\n","1      0.000  0.001 -0.001  0.003  0.004  ...  0.000  0.001 -0.000 -0.001  0.000\n","2     -0.000 -0.037  0.002 -0.004  0.015  ... -0.000 -0.000  0.000  0.000 -0.000\n","3      0.000  0.001  0.002 -0.006  0.013  ...  0.000 -0.000 -0.000 -0.000 -0.000\n","4     -0.001 -0.080 -0.001 -0.012  0.006  ...  0.001  0.003 -0.001  0.002  0.020\n","...      ...    ...    ...    ...    ...  ...    ...    ...    ...    ...    ...\n","12818 -0.000 -0.011  0.001 -0.001  0.020  ...  0.000  0.000  0.000  0.001  0.001\n","12819 -0.000  0.005  0.007 -0.003 -0.009  ...  0.001  0.000 -0.000 -0.001 -0.000\n","12820 -0.000 -0.006 -0.005  0.011 -0.006  ... -0.000 -0.002 -0.001 -0.000 -0.000\n","12821 -0.000  0.002  0.038  0.007 -0.002  ... -0.001 -0.000 -0.000 -0.000  0.000\n","12822 -0.000 -0.004 -0.009  0.006  0.002  ...  0.000 -0.001  0.000  0.000 -0.000\n","\n","[12823 rows x 345 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"markdown","metadata":{"id":"1PRFbQCjJNj6"},"source":["## Encode the data sets"]},{"cell_type":"code","metadata":{"id":"8eVjhbyeivYb"},"source":["# Encode the data\n","X_train_encoded_features = encoder_model.predict(X_train_scaled)\n","X_valid_encoded_features = encoder_model.predict(X_valid_scaled)\n","X_test_encoded_features = encoder_model.predict(X_test_scaled)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"gHaeeipZJRzM"},"source":["## Classifier Model"]},{"cell_type":"code","metadata":{"id":"Ay66J2Arjkhk","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1604175243707,"user_tz":240,"elapsed":395126,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"b72c9f7d-ac45-4859-8363-614b42956586"},"source":["model = Sequential()\n","model.add(Input(shape=(e_shape[1] * e_shape[2]),\n","                name='input'))\n","\n","model.add(Reshape((e_shape[1], e_shape[2]), \n","                  name='reshape'))\n","model.add(\n","    LSTM(units=16,\n","         activation='relu',\n","         recurrent_activation='sigmoid',\n","         return_sequences=True,\n","         name='lstm_layer_1'))\n","model.add(\n","    LSTM(units=8,\n","         activation='relu',\n","         recurrent_activation='sigmoid',\n","         return_sequences=True, \n","         name='lstm_layer_2'))\n","model.add(Flatten())\n","model.add(Dense(units=1,\n","                activation='sigmoid', \n","                name='output'))\n","\n","model.summary()\n","\n","model.compile(optimizer='adam',\n","              loss='binary_crossentropy',\n","              metrics=[\n","                  'accuracy',\n","                  tf.keras.metrics.Recall(),\n","                  pm.F1Score(),\n","                  pm.FalsePositiveRate()\n","              ])\n","history = model.fit(x=X_train_encoded_features,\n","                    y=y_train,\n","                    batch_size=128,\n","                    epochs=200,\n","                    validation_data=(X_valid_encoded_features,\n","                                     y_valid),\n","                    verbose=1).history"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","reshape (Reshape)            (None, 5, 69)             0         \n","_________________________________________________________________\n","lstm_layer_1 (LSTM)          (None, 5, 16)             5504      \n","_________________________________________________________________\n","lstm_layer_2 (LSTM)          (None, 5, 8)              800       \n","_________________________________________________________________\n","flatten (Flatten)            (None, 40)                0         \n","_________________________________________________________________\n","output (Dense)               (None, 1)                 41        \n","=================================================================\n","Total params: 6,345\n","Trainable params: 6,345\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/200\n","102/102 [==============================] - 1s 11ms/step - loss: 0.4690 - accuracy: 0.9822 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0041 - val_loss: 0.0790 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 2/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0743 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0694 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 3/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0725 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0684 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 4/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0709 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0686 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 5/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0703 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0667 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 6/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0688 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0661 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 7/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0683 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0656 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 8/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0674 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0653 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 9/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0665 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0652 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 10/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0664 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0649 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 11/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0660 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0653 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 12/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0659 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0647 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 13/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0657 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0648 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 14/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0652 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 15/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0646 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 16/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0649 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 17/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0644 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0658 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 18/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0644 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 19/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0643 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 20/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0640 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 21/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0637 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0653 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 22/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0634 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0652 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 23/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0635 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 24/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0633 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0646 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 25/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0633 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 26/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0631 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0655 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 27/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0627 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0646 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 28/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0624 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0643 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 29/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0623 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0646 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 30/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0625 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 31/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0627 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0656 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 32/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0623 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0643 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 33/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0620 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0673 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 34/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0618 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0648 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 35/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0619 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 36/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0618 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0659 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 37/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0620 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0640 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 38/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0616 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0675 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 39/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0619 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0674 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 40/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0615 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0640 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 41/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0608 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0642 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 42/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0613 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 43/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0611 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0647 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 44/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0613 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 45/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0608 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 46/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0607 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0637 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 47/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0605 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0642 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 48/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0607 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0649 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 49/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0608 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0638 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 50/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0607 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0639 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 51/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0605 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 52/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0598 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0643 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 53/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0599 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0638 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 54/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0599 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0638 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 55/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0601 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0645 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 56/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0602 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0643 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 57/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0597 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0635 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 58/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0593 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0637 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 59/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0592 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0657 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 60/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0597 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0635 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 61/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0593 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0642 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 62/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0592 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 63/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0596 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 64/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0589 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0635 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 65/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0588 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0632 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 66/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0589 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0631 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 67/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0589 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0640 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 68/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0585 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0631 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 69/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0592 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0636 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 70/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0591 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0632 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 71/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0595 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0642 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 72/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0584 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 73/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0584 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 74/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0579 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0649 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 75/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0580 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 76/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0578 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 77/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0576 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0626 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 78/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0579 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0631 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 79/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0583 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 80/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0576 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0642 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 81/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0580 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 82/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0572 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0637 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 83/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0586 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0624 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 84/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0569 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0658 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 85/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0582 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0630 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 86/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0571 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 87/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0575 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 88/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0572 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0640 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 89/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0570 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 90/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0567 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0630 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 91/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0566 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0635 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 92/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0575 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 93/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0575 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 94/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0568 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 95/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0565 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0626 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 96/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0568 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0625 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 97/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0561 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0624 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 98/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0561 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0630 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 99/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0563 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0632 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 100/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0562 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 101/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0558 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 102/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0558 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0673 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 103/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0564 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0641 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 104/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0563 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 7.7985e-05 - val_loss: 0.0688 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 105/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0557 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 106/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0551 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0632 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 107/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0555 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 108/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0555 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 0.0000e+00 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 109/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0554 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 7.7985e-05 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 110/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0550 - accuracy: 0.9862 - recall: 0.0056 - f1_score: 0.0110 - false_positive_rate: 7.7985e-05 - val_loss: 0.0627 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 111/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0548 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 7.7985e-05 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 112/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0554 - accuracy: 0.9862 - recall: 0.0000e+00 - f1_score: 0.0000e+00 - false_positive_rate: 7.7985e-05 - val_loss: 0.0642 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 113/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0547 - accuracy: 0.9862 - recall: 0.0056 - f1_score: 0.0110 - false_positive_rate: 7.7985e-05 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 114/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0543 - accuracy: 0.9862 - recall: 0.0056 - f1_score: 0.0110 - false_positive_rate: 7.7985e-05 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 115/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0539 - accuracy: 0.9862 - recall: 0.0056 - f1_score: 0.0110 - false_positive_rate: 7.7985e-05 - val_loss: 0.0661 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 116/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0551 - accuracy: 0.9862 - recall: 0.0056 - f1_score: 0.0110 - false_positive_rate: 7.7985e-05 - val_loss: 0.0626 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 117/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0539 - accuracy: 0.9862 - recall: 0.0056 - f1_score: 0.0110 - false_positive_rate: 7.7985e-05 - val_loss: 0.0628 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 118/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0540 - accuracy: 0.9863 - recall: 0.0112 - f1_score: 0.0220 - false_positive_rate: 7.7985e-05 - val_loss: 0.0644 - val_accuracy: 0.9868 - val_recall: 0.0000e+00 - val_f1_score: 0.0000e+00 - val_false_positive_rate: 0.0000e+00\n","Epoch 119/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0540 - accuracy: 0.9863 - recall: 0.0112 - f1_score: 0.0220 - false_positive_rate: 7.7985e-05 - val_loss: 0.0628 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 120/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0537 - accuracy: 0.9863 - recall: 0.0112 - f1_score: 0.0220 - false_positive_rate: 7.7985e-05 - val_loss: 0.0648 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 121/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0538 - accuracy: 0.9863 - recall: 0.0112 - f1_score: 0.0220 - false_positive_rate: 7.7985e-05 - val_loss: 0.0648 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 122/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0542 - accuracy: 0.9864 - recall: 0.0168 - f1_score: 0.0328 - false_positive_rate: 7.7985e-05 - val_loss: 0.0630 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 123/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0534 - accuracy: 0.9864 - recall: 0.0168 - f1_score: 0.0328 - false_positive_rate: 7.7985e-05 - val_loss: 0.0635 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 124/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0539 - accuracy: 0.9863 - recall: 0.0112 - f1_score: 0.0220 - false_positive_rate: 7.7985e-05 - val_loss: 0.0629 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 125/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0530 - accuracy: 0.9864 - recall: 0.0168 - f1_score: 0.0328 - false_positive_rate: 7.7985e-05 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 126/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0530 - accuracy: 0.9862 - recall: 0.0112 - f1_score: 0.0217 - false_positive_rate: 2.3395e-04 - val_loss: 0.0684 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 127/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0531 - accuracy: 0.9862 - recall: 0.0168 - f1_score: 0.0324 - false_positive_rate: 2.3395e-04 - val_loss: 0.0641 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 128/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0534 - accuracy: 0.9863 - recall: 0.0168 - f1_score: 0.0326 - false_positive_rate: 1.5597e-04 - val_loss: 0.0628 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 129/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0528 - accuracy: 0.9863 - recall: 0.0223 - f1_score: 0.0430 - false_positive_rate: 2.3395e-04 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 130/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0527 - accuracy: 0.9863 - recall: 0.0168 - f1_score: 0.0326 - false_positive_rate: 1.5597e-04 - val_loss: 0.0705 - val_accuracy: 0.9871 - val_recall: 0.0233 - val_f1_score: 0.0455 - val_false_positive_rate: 0.0000e+00\n","Epoch 131/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0526 - accuracy: 0.9864 - recall: 0.0279 - f1_score: 0.0535 - false_positive_rate: 2.3395e-04 - val_loss: 0.0670 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 132/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0526 - accuracy: 0.9863 - recall: 0.0223 - f1_score: 0.0430 - false_positive_rate: 2.3395e-04 - val_loss: 0.0646 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 133/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0520 - accuracy: 0.9864 - recall: 0.0223 - f1_score: 0.0432 - false_positive_rate: 1.5597e-04 - val_loss: 0.0643 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 134/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0523 - accuracy: 0.9862 - recall: 0.0168 - f1_score: 0.0324 - false_positive_rate: 2.3395e-04 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 135/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0517 - accuracy: 0.9862 - recall: 0.0168 - f1_score: 0.0324 - false_positive_rate: 2.3395e-04 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 136/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0517 - accuracy: 0.9864 - recall: 0.0279 - f1_score: 0.0535 - false_positive_rate: 2.3395e-04 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 137/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0518 - accuracy: 0.9862 - recall: 0.0223 - f1_score: 0.0428 - false_positive_rate: 3.1194e-04 - val_loss: 0.0648 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 138/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0516 - accuracy: 0.9865 - recall: 0.0279 - f1_score: 0.0538 - false_positive_rate: 1.5597e-04 - val_loss: 0.0634 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 139/200\n","102/102 [==============================] - 1s 10ms/step - loss: 0.0521 - accuracy: 0.9862 - recall: 0.0168 - f1_score: 0.0324 - false_positive_rate: 2.3395e-04 - val_loss: 0.0638 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 140/200\n","102/102 [==============================] - 1s 11ms/step - loss: 0.0538 - accuracy: 0.9863 - recall: 0.0168 - f1_score: 0.0326 - false_positive_rate: 1.5597e-04 - val_loss: 0.0623 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 141/200\n","102/102 [==============================] - 1s 10ms/step - loss: 0.0516 - accuracy: 0.9864 - recall: 0.0223 - f1_score: 0.0432 - false_positive_rate: 1.5597e-04 - val_loss: 0.0623 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 142/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0507 - accuracy: 0.9865 - recall: 0.0279 - f1_score: 0.0538 - false_positive_rate: 1.5597e-04 - val_loss: 0.0629 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 143/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0512 - accuracy: 0.9865 - recall: 0.0279 - f1_score: 0.0538 - false_positive_rate: 1.5597e-04 - val_loss: 0.0632 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 144/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0508 - accuracy: 0.9863 - recall: 0.0168 - f1_score: 0.0326 - false_positive_rate: 1.5597e-04 - val_loss: 0.0633 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 145/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0513 - accuracy: 0.9863 - recall: 0.0279 - f1_score: 0.0532 - false_positive_rate: 3.1194e-04 - val_loss: 0.0636 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 146/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0510 - accuracy: 0.9865 - recall: 0.0335 - f1_score: 0.0642 - false_positive_rate: 1.5597e-04 - val_loss: 0.0636 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 147/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0499 - accuracy: 0.9863 - recall: 0.0223 - f1_score: 0.0430 - false_positive_rate: 2.3395e-04 - val_loss: 0.0653 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 148/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0501 - accuracy: 0.9865 - recall: 0.0279 - f1_score: 0.0538 - false_positive_rate: 1.5597e-04 - val_loss: 0.0643 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 149/200\n","102/102 [==============================] - 1s 10ms/step - loss: 0.0501 - accuracy: 0.9865 - recall: 0.0391 - f1_score: 0.0741 - false_positive_rate: 2.3395e-04 - val_loss: 0.0651 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 150/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0509 - accuracy: 0.9864 - recall: 0.0279 - f1_score: 0.0535 - false_positive_rate: 2.3395e-04 - val_loss: 0.0621 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 151/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0495 - accuracy: 0.9864 - recall: 0.0223 - f1_score: 0.0432 - false_positive_rate: 1.5597e-04 - val_loss: 0.0636 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 152/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0498 - accuracy: 0.9864 - recall: 0.0335 - f1_score: 0.0635 - false_positive_rate: 3.1194e-04 - val_loss: 0.0625 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 153/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0496 - accuracy: 0.9866 - recall: 0.0503 - f1_score: 0.0938 - false_positive_rate: 3.1194e-04 - val_loss: 0.0631 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 154/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0488 - accuracy: 0.9865 - recall: 0.0279 - f1_score: 0.0538 - false_positive_rate: 1.5597e-04 - val_loss: 0.0631 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 155/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0494 - accuracy: 0.9865 - recall: 0.0335 - f1_score: 0.0642 - false_positive_rate: 1.5597e-04 - val_loss: 0.0732 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 156/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0495 - accuracy: 0.9865 - recall: 0.0335 - f1_score: 0.0638 - false_positive_rate: 2.3395e-04 - val_loss: 0.0643 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 157/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0490 - accuracy: 0.9866 - recall: 0.0391 - f1_score: 0.0745 - false_positive_rate: 1.5597e-04 - val_loss: 0.0629 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 158/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0491 - accuracy: 0.9867 - recall: 0.0559 - f1_score: 0.1036 - false_positive_rate: 3.1194e-04 - val_loss: 0.0624 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 159/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0482 - accuracy: 0.9865 - recall: 0.0391 - f1_score: 0.0737 - false_positive_rate: 3.1194e-04 - val_loss: 0.0629 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 160/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0478 - accuracy: 0.9868 - recall: 0.0503 - f1_score: 0.0947 - false_positive_rate: 1.5597e-04 - val_loss: 0.0650 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 161/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0495 - accuracy: 0.9865 - recall: 0.0279 - f1_score: 0.0538 - false_positive_rate: 1.5597e-04 - val_loss: 0.0651 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 162/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0495 - accuracy: 0.9867 - recall: 0.0559 - f1_score: 0.1036 - false_positive_rate: 3.1194e-04 - val_loss: 0.0625 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 163/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0494 - accuracy: 0.9867 - recall: 0.0503 - f1_score: 0.0942 - false_positive_rate: 2.3395e-04 - val_loss: 0.0692 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 164/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0487 - accuracy: 0.9868 - recall: 0.0559 - f1_score: 0.1042 - false_positive_rate: 2.3395e-04 - val_loss: 0.0632 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 165/200\n","102/102 [==============================] - 1s 9ms/step - loss: 0.0480 - accuracy: 0.9865 - recall: 0.0391 - f1_score: 0.0741 - false_positive_rate: 2.3395e-04 - val_loss: 0.0645 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 166/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0500 - accuracy: 0.9865 - recall: 0.0335 - f1_score: 0.0642 - false_positive_rate: 1.5597e-04 - val_loss: 0.0686 - val_accuracy: 0.9868 - val_recall: 0.0233 - val_f1_score: 0.0444 - val_false_positive_rate: 3.1172e-04\n","Epoch 167/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0484 - accuracy: 0.9866 - recall: 0.0447 - f1_score: 0.0842 - false_positive_rate: 2.3395e-04 - val_loss: 0.0633 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 168/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0468 - accuracy: 0.9869 - recall: 0.0615 - f1_score: 0.1146 - false_positive_rate: 1.5597e-04 - val_loss: 0.0632 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 169/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0471 - accuracy: 0.9866 - recall: 0.0391 - f1_score: 0.0745 - false_positive_rate: 1.5597e-04 - val_loss: 0.0634 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 170/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0474 - accuracy: 0.9866 - recall: 0.0447 - f1_score: 0.0842 - false_positive_rate: 2.3395e-04 - val_loss: 0.0632 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 171/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0471 - accuracy: 0.9866 - recall: 0.0447 - f1_score: 0.0842 - false_positive_rate: 2.3395e-04 - val_loss: 0.0654 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 172/200\n","102/102 [==============================] - 1s 7ms/step - loss: 0.0468 - accuracy: 0.9868 - recall: 0.0615 - f1_score: 0.1140 - false_positive_rate: 2.3395e-04 - val_loss: 0.0642 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 173/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0464 - accuracy: 0.9865 - recall: 0.0447 - f1_score: 0.0838 - false_positive_rate: 3.1194e-04 - val_loss: 0.0641 - val_accuracy: 0.9868 - val_recall: 0.0465 - val_f1_score: 0.0851 - val_false_positive_rate: 6.2344e-04\n","Epoch 174/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0466 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1231 - false_positive_rate: 3.1194e-04 - val_loss: 0.0638 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 175/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0457 - accuracy: 0.9867 - recall: 0.0503 - f1_score: 0.0942 - false_positive_rate: 2.3395e-04 - val_loss: 0.0645 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 176/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0456 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1231 - false_positive_rate: 3.1194e-04 - val_loss: 0.0659 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 177/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0456 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1231 - false_positive_rate: 3.1194e-04 - val_loss: 0.0695 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 178/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0459 - accuracy: 0.9867 - recall: 0.0615 - f1_score: 0.1128 - false_positive_rate: 3.8992e-04 - val_loss: 0.0713 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 179/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0454 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1231 - false_positive_rate: 3.1194e-04 - val_loss: 0.0643 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 180/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0450 - accuracy: 0.9868 - recall: 0.0782 - f1_score: 0.1407 - false_positive_rate: 4.6791e-04 - val_loss: 0.0680 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 181/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0451 - accuracy: 0.9869 - recall: 0.0726 - f1_score: 0.1327 - false_positive_rate: 3.1194e-04 - val_loss: 0.0668 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 182/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0446 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1231 - false_positive_rate: 3.1194e-04 - val_loss: 0.0648 - val_accuracy: 0.9859 - val_recall: 0.0465 - val_f1_score: 0.0800 - val_false_positive_rate: 0.0016\n","Epoch 183/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0448 - accuracy: 0.9868 - recall: 0.0726 - f1_score: 0.1320 - false_positive_rate: 3.8992e-04 - val_loss: 0.0677 - val_accuracy: 0.9868 - val_recall: 0.0465 - val_f1_score: 0.0851 - val_false_positive_rate: 6.2344e-04\n","Epoch 184/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0446 - accuracy: 0.9868 - recall: 0.0838 - f1_score: 0.1493 - false_positive_rate: 5.4589e-04 - val_loss: 0.0635 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 185/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0448 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1224 - false_positive_rate: 3.8992e-04 - val_loss: 0.0648 - val_accuracy: 0.9868 - val_recall: 0.0465 - val_f1_score: 0.0851 - val_false_positive_rate: 6.2344e-04\n","Epoch 186/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0436 - accuracy: 0.9867 - recall: 0.0615 - f1_score: 0.1128 - false_positive_rate: 3.8992e-04 - val_loss: 0.0732 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 187/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0440 - accuracy: 0.9868 - recall: 0.0670 - f1_score: 0.1224 - false_positive_rate: 3.8992e-04 - val_loss: 0.0669 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 188/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0444 - accuracy: 0.9870 - recall: 0.0838 - f1_score: 0.1508 - false_positive_rate: 3.8992e-04 - val_loss: 0.0647 - val_accuracy: 0.9859 - val_recall: 0.0465 - val_f1_score: 0.0800 - val_false_positive_rate: 0.0016\n","Epoch 189/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0435 - accuracy: 0.9867 - recall: 0.0726 - f1_score: 0.1307 - false_positive_rate: 5.4589e-04 - val_loss: 0.0643 - val_accuracy: 0.9865 - val_recall: 0.0465 - val_f1_score: 0.0833 - val_false_positive_rate: 9.3516e-04\n","Epoch 190/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0435 - accuracy: 0.9868 - recall: 0.0894 - f1_score: 0.1576 - false_positive_rate: 6.2388e-04 - val_loss: 0.0665 - val_accuracy: 0.9862 - val_recall: 0.0465 - val_f1_score: 0.0816 - val_false_positive_rate: 0.0012\n","Epoch 191/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0426 - accuracy: 0.9868 - recall: 0.0782 - f1_score: 0.1400 - false_positive_rate: 5.4589e-04 - val_loss: 0.0643 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 192/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0438 - accuracy: 0.9869 - recall: 0.0838 - f1_score: 0.1500 - false_positive_rate: 4.6791e-04 - val_loss: 0.0653 - val_accuracy: 0.9865 - val_recall: 0.0465 - val_f1_score: 0.0833 - val_false_positive_rate: 9.3516e-04\n","Epoch 193/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0431 - accuracy: 0.9868 - recall: 0.0782 - f1_score: 0.1400 - false_positive_rate: 5.4589e-04 - val_loss: 0.0644 - val_accuracy: 0.9865 - val_recall: 0.0465 - val_f1_score: 0.0833 - val_false_positive_rate: 9.3516e-04\n","Epoch 194/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0426 - accuracy: 0.9871 - recall: 0.0894 - f1_score: 0.1600 - false_positive_rate: 3.8992e-04 - val_loss: 0.0673 - val_accuracy: 0.9865 - val_recall: 0.0465 - val_f1_score: 0.0833 - val_false_positive_rate: 9.3516e-04\n","Epoch 195/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0430 - accuracy: 0.9870 - recall: 0.1006 - f1_score: 0.1756 - false_positive_rate: 6.2388e-04 - val_loss: 0.0726 - val_accuracy: 0.9871 - val_recall: 0.0465 - val_f1_score: 0.0870 - val_false_positive_rate: 3.1172e-04\n","Epoch 196/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0429 - accuracy: 0.9868 - recall: 0.0838 - f1_score: 0.1493 - false_positive_rate: 5.4589e-04 - val_loss: 0.0654 - val_accuracy: 0.9846 - val_recall: 0.0465 - val_f1_score: 0.0741 - val_false_positive_rate: 0.0028\n","Epoch 197/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0420 - accuracy: 0.9872 - recall: 0.1006 - f1_score: 0.1773 - false_positive_rate: 4.6791e-04 - val_loss: 0.0648 - val_accuracy: 0.9855 - val_recall: 0.0465 - val_f1_score: 0.0784 - val_false_positive_rate: 0.0019\n","Epoch 198/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0417 - accuracy: 0.9867 - recall: 0.0838 - f1_score: 0.1478 - false_positive_rate: 7.0186e-04 - val_loss: 0.0684 - val_accuracy: 0.9862 - val_recall: 0.0465 - val_f1_score: 0.0816 - val_false_positive_rate: 0.0012\n","Epoch 199/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0413 - accuracy: 0.9870 - recall: 0.0838 - f1_score: 0.1508 - false_positive_rate: 3.8992e-04 - val_loss: 0.0652 - val_accuracy: 0.9862 - val_recall: 0.0465 - val_f1_score: 0.0816 - val_false_positive_rate: 0.0012\n","Epoch 200/200\n","102/102 [==============================] - 1s 8ms/step - loss: 0.0415 - accuracy: 0.9871 - recall: 0.1117 - f1_score: 0.1923 - false_positive_rate: 7.0186e-04 - val_loss: 0.0750 - val_accuracy: 0.9868 - val_recall: 0.0465 - val_f1_score: 0.0851 - val_false_positive_rate: 6.2344e-04\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"W0Mzu_NbJWDv"},"source":["## Results"]},{"cell_type":"code","metadata":{"id":"UHQViGrkkddL","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1604175244493,"user_tz":240,"elapsed":395895,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"4a0c396e-204f-4bef-9892-6b13822b82e7"},"source":["plt, fig = sp.plot_metric(history, metric='loss', ylim=[0., 1.])\n","fig.savefig('sparse_lstm_encoder_classifier_loss.pdf', bbox_inches='tight')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"qC7rErtPk1Au","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1604175244494,"user_tz":240,"elapsed":395877,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"656f6993-6128-4018-a3d0-d95704678cd5"},"source":["plt, fig = sp.plot_metric(history, metric='f1_score', ylim=[0., 1.])\n","# fig.savefig('sparse_lstm_encoder_classifier_f1_score.pdf', bbox_inches='tight')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"opE54vVik3Xv","colab":{"base_uri":"https://localhost:8080/","height":294},"executionInfo":{"status":"ok","timestamp":1604175244715,"user_tz":240,"elapsed":396082,"user":{"displayName":"Chitta Ranjan","photoUrl":"","userId":"03244148267376825421"}},"outputId":"70c59ad3-4e27-4d34-9ba2-a7097c5165e7"},"source":["plt, fig = sp.plot_model_recall_fpr(history)\n","# fig.savefig('sparse_lstm_encoder_classifier_recall_fpr.pdf', bbox_inches='tight')"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"yzpdGd6AltBp"},"source":[""],"execution_count":null,"outputs":[]}]}