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b/Sentiment Predictor for Single Actor.ipynb |
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 34, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import librosa\n", |
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"import librosa.display\n", |
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"import numpy as np\n", |
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"import matplotlib.pyplot as plt\n", |
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"import tensorflow as tf\n", |
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"from matplotlib.pyplot import specgram\n", |
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"import keras\n", |
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"from keras.preprocessing import sequence\n", |
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"from keras.models import Sequential\n", |
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"from keras.layers import Dense, Embedding\n", |
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"from keras.layers import LSTM\n", |
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"from keras.preprocessing.text import Tokenizer\n", |
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"from keras.preprocessing.sequence import pad_sequences\n", |
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"from keras.utils import to_categorical\n", |
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"from keras.layers import Input, Flatten, Dropout, Activation\n", |
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"from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n", |
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"from keras.models import Model\n", |
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"from keras.callbacks import ModelCheckpoint\n", |
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"from sklearn.metrics import confusion_matrix" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 35, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from keras import regularizers" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 36, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 37, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import librosa\n", |
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"from librosa import display\n", |
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"\n", |
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"data, sampling_rate = librosa.load('E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Audio_Speech_Actors_01-24\\\\Actor_01\\\\03-01-05-02-01-02-01.wav')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 38, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/plain": [ |
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"<matplotlib.collections.PolyCollection at 0x18410e2b908>" |
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] |
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}, |
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"execution_count": 38, |
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"metadata": {}, |
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"output_type": "execute_result" |
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}, |
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{ |
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"data": { |
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"image/png": 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\n", 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"text/plain": [ |
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"<Figure size 864x288 with 1 Axes>" |
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] |
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}, |
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"metadata": { |
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"needs_background": "light" |
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}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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"%matplotlib inline\n", |
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"import os\n", |
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"import pandas as pd\n", |
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"import glob \n", |
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"\n", |
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"plt.figure(figsize=(12, 4))\n", |
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"librosa.display.waveplot(data, sr=sampling_rate)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 39, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stderr", |
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105 |
"output_type": "stream", |
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"text": [ |
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"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\ipykernel_launcher.py:7: WavFileWarning: Chunk (non-data) not understood, skipping it.\n", |
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" import sys\n" |
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] |
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}, |
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{ |
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"data": { |
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113 |
"image/png": 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\n", 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"text/plain": [ |
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"<Figure size 432x288 with 1 Axes>" |
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116 |
] |
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}, |
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"metadata": { |
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"needs_background": "light" |
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}, |
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"output_type": "display_data" |
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} |
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], |
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"source": [ |
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125 |
"import matplotlib.pyplot as plt\n", |
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126 |
"import scipy.io.wavfile\n", |
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127 |
"import numpy as np\n", |
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"import sys\n", |
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"\n", |
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"\n", |
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"sr,x = scipy.io.wavfile.read('E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Audio_Speech_Actors_01-24\\\\Actor_01\\\\03-01-05-02-01-02-01.wav')\n", |
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"\n", |
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"## Parameters: 10ms step, 30ms window\n", |
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134 |
"nstep = int(sr * 0.01)\n", |
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135 |
"nwin = int(sr * 0.03)\n", |
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136 |
"nfft = nwin\n", |
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"\n", |
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138 |
"window = np.hamming(nwin)\n", |
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"\n", |
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|
140 |
"## will take windows x[n1:n2]. generate\n", |
|
|
141 |
"## and loop over n2 such that all frames\n", |
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142 |
"## fit within the waveform\n", |
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143 |
"nn = range(nwin, len(x), nstep)\n", |
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"\n", |
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145 |
"X = np.zeros( (len(nn), nfft//2) )\n", |
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"\n", |
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147 |
"for i,n in enumerate(nn):\n", |
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148 |
" xseg = x[n-nwin:n]\n", |
|
|
149 |
" z = np.fft.fft(window * xseg, nfft)\n", |
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|
150 |
" X[i,:] = np.log(np.abs(z[:nfft//2]))\n", |
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151 |
"\n", |
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152 |
"plt.imshow(X.T, interpolation='nearest',\n", |
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153 |
" origin='lower',\n", |
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154 |
" aspect='auto')\n", |
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155 |
"\n", |
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156 |
"plt.show()" |
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157 |
] |
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158 |
}, |
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159 |
{ |
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|
160 |
"cell_type": "code", |
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161 |
"execution_count": 40, |
|
|
162 |
"metadata": {}, |
|
|
163 |
"outputs": [ |
|
|
164 |
{ |
|
|
165 |
"name": "stdout", |
|
|
166 |
"output_type": "stream", |
|
|
167 |
"text": [ |
|
|
168 |
"--- Data loaded. Loading time: 2.525601387023926 seconds ---\n" |
|
|
169 |
] |
|
|
170 |
} |
|
|
171 |
], |
|
|
172 |
"source": [ |
|
|
173 |
"import time\n", |
|
|
174 |
"\n", |
|
|
175 |
"path = 'E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Audio_Speech_Actors_01-24\\\\Actor_01'\n", |
|
|
176 |
"lst = []\n", |
|
|
177 |
"\n", |
|
|
178 |
"start_time = time.time()\n", |
|
|
179 |
"\n", |
|
|
180 |
"for subdir, dirs, files in os.walk(path):\n", |
|
|
181 |
" for file in files:\n", |
|
|
182 |
" try:\n", |
|
|
183 |
" #Load librosa array, obtain mfcss, store the file and the mcss information in a new array\n", |
|
|
184 |
" X, sample_rate = librosa.load(os.path.join(subdir,file), res_type='kaiser_fast')\n", |
|
|
185 |
" mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0) \n", |
|
|
186 |
" # The instruction below converts the labels (from 1 to 8) to a series from 0 to 7\n", |
|
|
187 |
" # This is because our predictor needs to start from 0 otherwise it will try to predict also 0.\n", |
|
|
188 |
" file = int(file[7:8]) - 1 \n", |
|
|
189 |
" arr = mfccs, file\n", |
|
|
190 |
" lst.append(arr)\n", |
|
|
191 |
" # If the file is not valid, skip it\n", |
|
|
192 |
" except ValueError:\n", |
|
|
193 |
" continue\n", |
|
|
194 |
"\n", |
|
|
195 |
"print(\"--- Data loaded. Loading time: %s seconds ---\" % (time.time() - start_time))" |
|
|
196 |
] |
|
|
197 |
}, |
|
|
198 |
{ |
|
|
199 |
"cell_type": "code", |
|
|
200 |
"execution_count": 41, |
|
|
201 |
"metadata": {}, |
|
|
202 |
"outputs": [], |
|
|
203 |
"source": [ |
|
|
204 |
"# Creating X and y: zip makes a list of all the first elements, and a list of all the second elements.\n", |
|
|
205 |
"X, y = zip(*lst)" |
|
|
206 |
] |
|
|
207 |
}, |
|
|
208 |
{ |
|
|
209 |
"cell_type": "code", |
|
|
210 |
"execution_count": 42, |
|
|
211 |
"metadata": {}, |
|
|
212 |
"outputs": [ |
|
|
213 |
{ |
|
|
214 |
"data": { |
|
|
215 |
"text/plain": [ |
|
|
216 |
"((60, 40), (60,))" |
|
|
217 |
] |
|
|
218 |
}, |
|
|
219 |
"execution_count": 42, |
|
|
220 |
"metadata": {}, |
|
|
221 |
"output_type": "execute_result" |
|
|
222 |
} |
|
|
223 |
], |
|
|
224 |
"source": [ |
|
|
225 |
"import numpy as np\n", |
|
|
226 |
"X = np.asarray(X)\n", |
|
|
227 |
"y = np.asarray(y)\n", |
|
|
228 |
"\n", |
|
|
229 |
"\n", |
|
|
230 |
"X.shape, y.shape" |
|
|
231 |
] |
|
|
232 |
}, |
|
|
233 |
{ |
|
|
234 |
"cell_type": "code", |
|
|
235 |
"execution_count": 43, |
|
|
236 |
"metadata": {}, |
|
|
237 |
"outputs": [], |
|
|
238 |
"source": [ |
|
|
239 |
"# Saving joblib files to not load them again with the loop above\n", |
|
|
240 |
"\n", |
|
|
241 |
"import joblib\n", |
|
|
242 |
"\n", |
|
|
243 |
"X_name = 'X.joblib'\n", |
|
|
244 |
"y_name = 'y.joblib'\n", |
|
|
245 |
"save_dir = 'E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Saved_Models'\n", |
|
|
246 |
"\n", |
|
|
247 |
"savedX = joblib.dump(X, os.path.join(save_dir, X_name))\n", |
|
|
248 |
"savedy = joblib.dump(y, os.path.join(save_dir, y_name))\n" |
|
|
249 |
] |
|
|
250 |
}, |
|
|
251 |
{ |
|
|
252 |
"cell_type": "code", |
|
|
253 |
"execution_count": 44, |
|
|
254 |
"metadata": {}, |
|
|
255 |
"outputs": [], |
|
|
256 |
"source": [ |
|
|
257 |
"# Loading saved models\n", |
|
|
258 |
"\n", |
|
|
259 |
"X = joblib.load('E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Saved_Models\\\\X.joblib')\n", |
|
|
260 |
"y = joblib.load('E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Saved_Models\\\\y.joblib')" |
|
|
261 |
] |
|
|
262 |
}, |
|
|
263 |
{ |
|
|
264 |
"cell_type": "markdown", |
|
|
265 |
"metadata": {}, |
|
|
266 |
"source": [ |
|
|
267 |
"## Decision Tree Model" |
|
|
268 |
] |
|
|
269 |
}, |
|
|
270 |
{ |
|
|
271 |
"cell_type": "code", |
|
|
272 |
"execution_count": 45, |
|
|
273 |
"metadata": {}, |
|
|
274 |
"outputs": [], |
|
|
275 |
"source": [ |
|
|
276 |
"from sklearn.model_selection import train_test_split\n", |
|
|
277 |
"\n", |
|
|
278 |
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=11)" |
|
|
279 |
] |
|
|
280 |
}, |
|
|
281 |
{ |
|
|
282 |
"cell_type": "code", |
|
|
283 |
"execution_count": 46, |
|
|
284 |
"metadata": {}, |
|
|
285 |
"outputs": [], |
|
|
286 |
"source": [ |
|
|
287 |
"from sklearn.tree import DecisionTreeClassifier" |
|
|
288 |
] |
|
|
289 |
}, |
|
|
290 |
{ |
|
|
291 |
"cell_type": "code", |
|
|
292 |
"execution_count": 47, |
|
|
293 |
"metadata": {}, |
|
|
294 |
"outputs": [], |
|
|
295 |
"source": [ |
|
|
296 |
"dtree = DecisionTreeClassifier()" |
|
|
297 |
] |
|
|
298 |
}, |
|
|
299 |
{ |
|
|
300 |
"cell_type": "code", |
|
|
301 |
"execution_count": 48, |
|
|
302 |
"metadata": {}, |
|
|
303 |
"outputs": [ |
|
|
304 |
{ |
|
|
305 |
"data": { |
|
|
306 |
"text/plain": [ |
|
|
307 |
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", |
|
|
308 |
" max_features=None, max_leaf_nodes=None,\n", |
|
|
309 |
" min_impurity_decrease=0.0, min_impurity_split=None,\n", |
|
|
310 |
" min_samples_leaf=1, min_samples_split=2,\n", |
|
|
311 |
" min_weight_fraction_leaf=0.0, presort=False,\n", |
|
|
312 |
" random_state=None, splitter='best')" |
|
|
313 |
] |
|
|
314 |
}, |
|
|
315 |
"execution_count": 48, |
|
|
316 |
"metadata": {}, |
|
|
317 |
"output_type": "execute_result" |
|
|
318 |
} |
|
|
319 |
], |
|
|
320 |
"source": [ |
|
|
321 |
"dtree.fit(X_train, y_train)" |
|
|
322 |
] |
|
|
323 |
}, |
|
|
324 |
{ |
|
|
325 |
"cell_type": "code", |
|
|
326 |
"execution_count": 49, |
|
|
327 |
"metadata": {}, |
|
|
328 |
"outputs": [], |
|
|
329 |
"source": [ |
|
|
330 |
"predictions = dtree.predict(X_test)" |
|
|
331 |
] |
|
|
332 |
}, |
|
|
333 |
{ |
|
|
334 |
"cell_type": "code", |
|
|
335 |
"execution_count": 50, |
|
|
336 |
"metadata": {}, |
|
|
337 |
"outputs": [ |
|
|
338 |
{ |
|
|
339 |
"name": "stdout", |
|
|
340 |
"output_type": "stream", |
|
|
341 |
"text": [ |
|
|
342 |
" precision recall f1-score support\n", |
|
|
343 |
"\n", |
|
|
344 |
" 0 0.00 0.00 0.00 1\n", |
|
|
345 |
" 1 0.33 0.33 0.33 3\n", |
|
|
346 |
" 2 0.00 0.00 0.00 1\n", |
|
|
347 |
" 3 1.00 0.33 0.50 3\n", |
|
|
348 |
" 4 0.00 0.00 0.00 0\n", |
|
|
349 |
" 5 0.50 0.25 0.33 4\n", |
|
|
350 |
" 6 0.00 0.00 0.00 2\n", |
|
|
351 |
" 7 0.00 0.00 0.00 4\n", |
|
|
352 |
"\n", |
|
|
353 |
" accuracy 0.17 18\n", |
|
|
354 |
" macro avg 0.23 0.11 0.15 18\n", |
|
|
355 |
"weighted avg 0.33 0.17 0.21 18\n", |
|
|
356 |
"\n" |
|
|
357 |
] |
|
|
358 |
}, |
|
|
359 |
{ |
|
|
360 |
"name": "stderr", |
|
|
361 |
"output_type": "stream", |
|
|
362 |
"text": [ |
|
|
363 |
"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n", |
|
|
364 |
" 'precision', 'predicted', average, warn_for)\n", |
|
|
365 |
"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\sklearn\\metrics\\classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n", |
|
|
366 |
" 'recall', 'true', average, warn_for)\n" |
|
|
367 |
] |
|
|
368 |
} |
|
|
369 |
], |
|
|
370 |
"source": [ |
|
|
371 |
"from sklearn.metrics import classification_report,confusion_matrix\n", |
|
|
372 |
"print(classification_report(y_test,predictions))" |
|
|
373 |
] |
|
|
374 |
}, |
|
|
375 |
{ |
|
|
376 |
"cell_type": "markdown", |
|
|
377 |
"metadata": {}, |
|
|
378 |
"source": [ |
|
|
379 |
"## Random Forest" |
|
|
380 |
] |
|
|
381 |
}, |
|
|
382 |
{ |
|
|
383 |
"cell_type": "code", |
|
|
384 |
"execution_count": 51, |
|
|
385 |
"metadata": {}, |
|
|
386 |
"outputs": [], |
|
|
387 |
"source": [ |
|
|
388 |
"from sklearn.ensemble import RandomForestClassifier\n" |
|
|
389 |
] |
|
|
390 |
}, |
|
|
391 |
{ |
|
|
392 |
"cell_type": "code", |
|
|
393 |
"execution_count": 52, |
|
|
394 |
"metadata": {}, |
|
|
395 |
"outputs": [], |
|
|
396 |
"source": [ |
|
|
397 |
"rforest = RandomForestClassifier(criterion=\"gini\", max_depth=10, max_features=\"log2\", \n", |
|
|
398 |
" max_leaf_nodes = 100, min_samples_leaf = 3, min_samples_split = 20, \n", |
|
|
399 |
" n_estimators= 22000, random_state= 5)" |
|
|
400 |
] |
|
|
401 |
}, |
|
|
402 |
{ |
|
|
403 |
"cell_type": "code", |
|
|
404 |
"execution_count": 53, |
|
|
405 |
"metadata": {}, |
|
|
406 |
"outputs": [ |
|
|
407 |
{ |
|
|
408 |
"data": { |
|
|
409 |
"text/plain": [ |
|
|
410 |
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", |
|
|
411 |
" max_depth=10, max_features='log2', max_leaf_nodes=100,\n", |
|
|
412 |
" min_impurity_decrease=0.0, min_impurity_split=None,\n", |
|
|
413 |
" min_samples_leaf=3, min_samples_split=20,\n", |
|
|
414 |
" min_weight_fraction_leaf=0.0, n_estimators=22000,\n", |
|
|
415 |
" n_jobs=None, oob_score=False, random_state=5, verbose=0,\n", |
|
|
416 |
" warm_start=False)" |
|
|
417 |
] |
|
|
418 |
}, |
|
|
419 |
"execution_count": 53, |
|
|
420 |
"metadata": {}, |
|
|
421 |
"output_type": "execute_result" |
|
|
422 |
} |
|
|
423 |
], |
|
|
424 |
"source": [ |
|
|
425 |
"rforest.fit(X_train, y_train)" |
|
|
426 |
] |
|
|
427 |
}, |
|
|
428 |
{ |
|
|
429 |
"cell_type": "code", |
|
|
430 |
"execution_count": 54, |
|
|
431 |
"metadata": {}, |
|
|
432 |
"outputs": [], |
|
|
433 |
"source": [ |
|
|
434 |
"predictions = rforest.predict(X_test)" |
|
|
435 |
] |
|
|
436 |
}, |
|
|
437 |
{ |
|
|
438 |
"cell_type": "code", |
|
|
439 |
"execution_count": 55, |
|
|
440 |
"metadata": {}, |
|
|
441 |
"outputs": [ |
|
|
442 |
{ |
|
|
443 |
"name": "stdout", |
|
|
444 |
"output_type": "stream", |
|
|
445 |
"text": [ |
|
|
446 |
" precision recall f1-score support\n", |
|
|
447 |
"\n", |
|
|
448 |
" 0 0.00 0.00 0.00 1\n", |
|
|
449 |
" 1 0.43 1.00 0.60 3\n", |
|
|
450 |
" 2 0.00 0.00 0.00 1\n", |
|
|
451 |
" 3 0.00 0.00 0.00 3\n", |
|
|
452 |
" 4 0.00 0.00 0.00 0\n", |
|
|
453 |
" 5 0.00 0.00 0.00 4\n", |
|
|
454 |
" 6 0.00 0.00 0.00 2\n", |
|
|
455 |
" 7 0.00 0.00 0.00 4\n", |
|
|
456 |
"\n", |
|
|
457 |
" accuracy 0.17 18\n", |
|
|
458 |
" macro avg 0.05 0.12 0.07 18\n", |
|
|
459 |
"weighted avg 0.07 0.17 0.10 18\n", |
|
|
460 |
"\n" |
|
|
461 |
] |
|
|
462 |
}, |
|
|
463 |
{ |
|
|
464 |
"name": "stderr", |
|
|
465 |
"output_type": "stream", |
|
|
466 |
"text": [ |
|
|
467 |
"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n", |
|
|
468 |
" 'precision', 'predicted', average, warn_for)\n", |
|
|
469 |
"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\sklearn\\metrics\\classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n", |
|
|
470 |
" 'recall', 'true', average, warn_for)\n" |
|
|
471 |
] |
|
|
472 |
} |
|
|
473 |
], |
|
|
474 |
"source": [ |
|
|
475 |
"print(classification_report(y_test,predictions))" |
|
|
476 |
] |
|
|
477 |
}, |
|
|
478 |
{ |
|
|
479 |
"cell_type": "code", |
|
|
480 |
"execution_count": 56, |
|
|
481 |
"metadata": {}, |
|
|
482 |
"outputs": [], |
|
|
483 |
"source": [ |
|
|
484 |
"##Neural Network\n", |
|
|
485 |
"x_traincnn = np.expand_dims(X_train, axis=2)\n", |
|
|
486 |
"x_testcnn = np.expand_dims(X_test, axis=2)" |
|
|
487 |
] |
|
|
488 |
}, |
|
|
489 |
{ |
|
|
490 |
"cell_type": "code", |
|
|
491 |
"execution_count": 57, |
|
|
492 |
"metadata": {}, |
|
|
493 |
"outputs": [ |
|
|
494 |
{ |
|
|
495 |
"data": { |
|
|
496 |
"text/plain": [ |
|
|
497 |
"((42, 40, 1), (18, 40, 1))" |
|
|
498 |
] |
|
|
499 |
}, |
|
|
500 |
"execution_count": 57, |
|
|
501 |
"metadata": {}, |
|
|
502 |
"output_type": "execute_result" |
|
|
503 |
} |
|
|
504 |
], |
|
|
505 |
"source": [ |
|
|
506 |
"x_traincnn.shape, x_testcnn.shape" |
|
|
507 |
] |
|
|
508 |
}, |
|
|
509 |
{ |
|
|
510 |
"cell_type": "code", |
|
|
511 |
"execution_count": 58, |
|
|
512 |
"metadata": {}, |
|
|
513 |
"outputs": [], |
|
|
514 |
"source": [ |
|
|
515 |
"import keras\n", |
|
|
516 |
"import numpy as np\n", |
|
|
517 |
"import matplotlib.pyplot as plt\n", |
|
|
518 |
"import tensorflow as tf\n", |
|
|
519 |
"from tensorflow.python.keras import backend as k\n", |
|
|
520 |
"from tensorflow.keras.preprocessing import sequence\n", |
|
|
521 |
"from tensorflow.keras.models import Sequential\n", |
|
|
522 |
"from tensorflow.keras.layers import Dense, Embedding\n", |
|
|
523 |
"from tensorflow.keras.utils import to_categorical\n", |
|
|
524 |
"from tensorflow.keras.layers import Input, Flatten, Dropout, Activation\n", |
|
|
525 |
"from tensorflow.keras.layers import Conv1D, MaxPooling1D\n", |
|
|
526 |
"from tensorflow.keras.models import Model\n", |
|
|
527 |
"from tensorflow.keras.callbacks import ModelCheckpoint\n", |
|
|
528 |
"\n", |
|
|
529 |
"model = Sequential()\n", |
|
|
530 |
"\n", |
|
|
531 |
"model.add(Conv1D(128, 5,padding='same',\n", |
|
|
532 |
" input_shape=(40,1)))\n", |
|
|
533 |
"model.add(Activation('relu'))\n", |
|
|
534 |
"model.add(Dropout(0.1))\n", |
|
|
535 |
"model.add(MaxPooling1D(pool_size=(8)))\n", |
|
|
536 |
"model.add(Conv1D(128, 5,padding='same',))\n", |
|
|
537 |
"model.add(Activation('relu'))\n", |
|
|
538 |
"model.add(Dropout(0.1))\n", |
|
|
539 |
"model.add(Flatten())\n", |
|
|
540 |
"model.add(Dense(8))\n", |
|
|
541 |
"model.add(Activation('softmax'))\n", |
|
|
542 |
"opt = keras.optimizers.rmsprop(lr=0.00005, rho=0.9, epsilon=None, decay=0.0)" |
|
|
543 |
] |
|
|
544 |
}, |
|
|
545 |
{ |
|
|
546 |
"cell_type": "code", |
|
|
547 |
"execution_count": 59, |
|
|
548 |
"metadata": {}, |
|
|
549 |
"outputs": [ |
|
|
550 |
{ |
|
|
551 |
"name": "stdout", |
|
|
552 |
"output_type": "stream", |
|
|
553 |
"text": [ |
|
|
554 |
"Model: \"sequential_1\"\n", |
|
|
555 |
"_________________________________________________________________\n", |
|
|
556 |
"Layer (type) Output Shape Param # \n", |
|
|
557 |
"=================================================================\n", |
|
|
558 |
"conv1d_2 (Conv1D) (None, 40, 128) 768 \n", |
|
|
559 |
"_________________________________________________________________\n", |
|
|
560 |
"activation_3 (Activation) (None, 40, 128) 0 \n", |
|
|
561 |
"_________________________________________________________________\n", |
|
|
562 |
"dropout_2 (Dropout) (None, 40, 128) 0 \n", |
|
|
563 |
"_________________________________________________________________\n", |
|
|
564 |
"max_pooling1d_1 (MaxPooling1 (None, 5, 128) 0 \n", |
|
|
565 |
"_________________________________________________________________\n", |
|
|
566 |
"conv1d_3 (Conv1D) (None, 5, 128) 82048 \n", |
|
|
567 |
"_________________________________________________________________\n", |
|
|
568 |
"activation_4 (Activation) (None, 5, 128) 0 \n", |
|
|
569 |
"_________________________________________________________________\n", |
|
|
570 |
"dropout_3 (Dropout) (None, 5, 128) 0 \n", |
|
|
571 |
"_________________________________________________________________\n", |
|
|
572 |
"flatten_1 (Flatten) (None, 640) 0 \n", |
|
|
573 |
"_________________________________________________________________\n", |
|
|
574 |
"dense_1 (Dense) (None, 8) 5128 \n", |
|
|
575 |
"_________________________________________________________________\n", |
|
|
576 |
"activation_5 (Activation) (None, 8) 0 \n", |
|
|
577 |
"=================================================================\n", |
|
|
578 |
"Total params: 87,944\n", |
|
|
579 |
"Trainable params: 87,944\n", |
|
|
580 |
"Non-trainable params: 0\n", |
|
|
581 |
"_________________________________________________________________\n" |
|
|
582 |
] |
|
|
583 |
} |
|
|
584 |
], |
|
|
585 |
"source": [ |
|
|
586 |
"model.summary()" |
|
|
587 |
] |
|
|
588 |
}, |
|
|
589 |
{ |
|
|
590 |
"cell_type": "code", |
|
|
591 |
"execution_count": 61, |
|
|
592 |
"metadata": {}, |
|
|
593 |
"outputs": [], |
|
|
594 |
"source": [ |
|
|
595 |
"model.compile(loss='sparse_categorical_crossentropy',\n", |
|
|
596 |
" optimizer='adam',\n", |
|
|
597 |
" metrics=['accuracy'])" |
|
|
598 |
] |
|
|
599 |
}, |
|
|
600 |
{ |
|
|
601 |
"cell_type": "code", |
|
|
602 |
"execution_count": 62, |
|
|
603 |
"metadata": {}, |
|
|
604 |
"outputs": [ |
|
|
605 |
{ |
|
|
606 |
"name": "stdout", |
|
|
607 |
"output_type": "stream", |
|
|
608 |
"text": [ |
|
|
609 |
"Train on 42 samples, validate on 18 samples\n", |
|
|
610 |
"Epoch 1/1000\n", |
|
|
611 |
"42/42 [==============================] - 2s 50ms/sample - loss: 16.8266 - accuracy: 0.1905 - val_loss: 17.0780 - val_accuracy: 0.1667\n", |
|
|
612 |
"Epoch 2/1000\n", |
|
|
613 |
"42/42 [==============================] - 0s 569us/sample - loss: 17.3586 - accuracy: 0.1190 - val_loss: 15.0258 - val_accuracy: 0.0000e+00\n", |
|
|
614 |
"Epoch 3/1000\n", |
|
|
615 |
"42/42 [==============================] - 0s 549us/sample - loss: 15.1622 - accuracy: 0.0476 - val_loss: 10.4946 - val_accuracy: 0.2222\n", |
|
|
616 |
"Epoch 4/1000\n", |
|
|
617 |
"42/42 [==============================] - 0s 570us/sample - loss: 10.9094 - accuracy: 0.0714 - val_loss: 6.6860 - val_accuracy: 0.1667\n", |
|
|
618 |
"Epoch 5/1000\n", |
|
|
619 |
"42/42 [==============================] - 0s 564us/sample - loss: 9.5741 - accuracy: 0.2143 - val_loss: 5.5836 - val_accuracy: 0.1667\n", |
|
|
620 |
"Epoch 6/1000\n", |
|
|
621 |
"42/42 [==============================] - 0s 522us/sample - loss: 7.9397 - accuracy: 0.1190 - val_loss: 4.3999 - val_accuracy: 0.1111\n", |
|
|
622 |
"Epoch 7/1000\n", |
|
|
623 |
"42/42 [==============================] - 0s 522us/sample - loss: 6.9984 - accuracy: 0.1667 - val_loss: 2.5061 - val_accuracy: 0.3889\n", |
|
|
624 |
"Epoch 8/1000\n", |
|
|
625 |
"42/42 [==============================] - 0s 543us/sample - loss: 5.8718 - accuracy: 0.2381 - val_loss: 4.7310 - val_accuracy: 0.3889\n", |
|
|
626 |
"Epoch 9/1000\n", |
|
|
627 |
"42/42 [==============================] - 0s 570us/sample - loss: 6.3738 - accuracy: 0.1905 - val_loss: 3.5837 - val_accuracy: 0.0556\n", |
|
|
628 |
"Epoch 10/1000\n", |
|
|
629 |
"42/42 [==============================] - 0s 538us/sample - loss: 5.2923 - accuracy: 0.1190 - val_loss: 2.8802 - val_accuracy: 0.2778\n", |
|
|
630 |
"Epoch 11/1000\n", |
|
|
631 |
"42/42 [==============================] - 0s 546us/sample - loss: 6.1291 - accuracy: 0.1190 - val_loss: 3.5806 - val_accuracy: 0.1111\n", |
|
|
632 |
"Epoch 12/1000\n", |
|
|
633 |
"42/42 [==============================] - 0s 546us/sample - loss: 4.5922 - accuracy: 0.1905 - val_loss: 2.8017 - val_accuracy: 0.0556\n", |
|
|
634 |
"Epoch 13/1000\n", |
|
|
635 |
"42/42 [==============================] - 0s 499us/sample - loss: 5.2955 - accuracy: 0.1667 - val_loss: 2.0272 - val_accuracy: 0.2778\n", |
|
|
636 |
"Epoch 14/1000\n", |
|
|
637 |
"42/42 [==============================] - 0s 522us/sample - loss: 3.8005 - accuracy: 0.1905 - val_loss: 2.2476 - val_accuracy: 0.2222\n", |
|
|
638 |
"Epoch 15/1000\n", |
|
|
639 |
"42/42 [==============================] - 0s 499us/sample - loss: 3.4318 - accuracy: 0.1429 - val_loss: 2.5107 - val_accuracy: 0.1667\n", |
|
|
640 |
"Epoch 16/1000\n", |
|
|
641 |
"42/42 [==============================] - 0s 522us/sample - loss: 3.8409 - accuracy: 0.2619 - val_loss: 2.8869 - val_accuracy: 0.2222\n", |
|
|
642 |
"Epoch 17/1000\n", |
|
|
643 |
"42/42 [==============================] - 0s 546us/sample - loss: 3.5375 - accuracy: 0.2619 - val_loss: 2.7447 - val_accuracy: 0.2222\n", |
|
|
644 |
"Epoch 18/1000\n", |
|
|
645 |
"42/42 [==============================] - 0s 500us/sample - loss: 3.7140 - accuracy: 0.2381 - val_loss: 3.9172 - val_accuracy: 0.2222\n", |
|
|
646 |
"Epoch 19/1000\n", |
|
|
647 |
"42/42 [==============================] - 0s 499us/sample - loss: 4.1464 - accuracy: 0.2143 - val_loss: 4.0752 - val_accuracy: 0.1667\n", |
|
|
648 |
"Epoch 20/1000\n", |
|
|
649 |
"42/42 [==============================] - 0s 522us/sample - loss: 3.2471 - accuracy: 0.3571 - val_loss: 2.4354 - val_accuracy: 0.2778\n", |
|
|
650 |
"Epoch 21/1000\n", |
|
|
651 |
"42/42 [==============================] - 0s 499us/sample - loss: 3.4584 - accuracy: 0.1905 - val_loss: 2.1967 - val_accuracy: 0.3333\n", |
|
|
652 |
"Epoch 22/1000\n", |
|
|
653 |
"42/42 [==============================] - 0s 522us/sample - loss: 3.8053 - accuracy: 0.2143 - val_loss: 2.1711 - val_accuracy: 0.1667\n", |
|
|
654 |
"Epoch 23/1000\n", |
|
|
655 |
"42/42 [==============================] - 0s 522us/sample - loss: 2.8152 - accuracy: 0.3333 - val_loss: 2.1879 - val_accuracy: 0.2222\n", |
|
|
656 |
"Epoch 24/1000\n", |
|
|
657 |
"42/42 [==============================] - 0s 499us/sample - loss: 2.7592 - accuracy: 0.2381 - val_loss: 2.4282 - val_accuracy: 0.1111\n", |
|
|
658 |
"Epoch 25/1000\n", |
|
|
659 |
"42/42 [==============================] - 0s 522us/sample - loss: 2.9619 - accuracy: 0.2143 - val_loss: 2.1060 - val_accuracy: 0.2222\n", |
|
|
660 |
"Epoch 26/1000\n", |
|
|
661 |
"42/42 [==============================] - 0s 522us/sample - loss: 2.5271 - accuracy: 0.2143 - val_loss: 2.1026 - val_accuracy: 0.3889\n", |
|
|
662 |
"Epoch 27/1000\n", |
|
|
663 |
"42/42 [==============================] - 0s 524us/sample - loss: 2.4636 - accuracy: 0.3571 - val_loss: 1.5638 - val_accuracy: 0.5000\n", |
|
|
664 |
"Epoch 28/1000\n", |
|
|
665 |
"42/42 [==============================] - 0s 547us/sample - loss: 2.3624 - accuracy: 0.3810 - val_loss: 1.7554 - val_accuracy: 0.3889\n", |
|
|
666 |
"Epoch 29/1000\n", |
|
|
667 |
"42/42 [==============================] - 0s 546us/sample - loss: 2.3298 - accuracy: 0.3095 - val_loss: 1.8435 - val_accuracy: 0.3333\n", |
|
|
668 |
"Epoch 30/1000\n", |
|
|
669 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.9348 - accuracy: 0.3095 - val_loss: 1.6136 - val_accuracy: 0.4444\n", |
|
|
670 |
"Epoch 31/1000\n", |
|
|
671 |
"42/42 [==============================] - 0s 499us/sample - loss: 2.1053 - accuracy: 0.2619 - val_loss: 1.4550 - val_accuracy: 0.4444\n", |
|
|
672 |
"Epoch 32/1000\n", |
|
|
673 |
"42/42 [==============================] - 0s 546us/sample - loss: 1.9278 - accuracy: 0.4286 - val_loss: 1.5467 - val_accuracy: 0.5000\n", |
|
|
674 |
"Epoch 33/1000\n", |
|
|
675 |
"42/42 [==============================] - 0s 505us/sample - loss: 1.8345 - accuracy: 0.4048 - val_loss: 2.1797 - val_accuracy: 0.3333\n", |
|
|
676 |
"Epoch 34/1000\n", |
|
|
677 |
"42/42 [==============================] - 0s 546us/sample - loss: 2.0305 - accuracy: 0.3333 - val_loss: 2.0248 - val_accuracy: 0.4444\n", |
|
|
678 |
"Epoch 35/1000\n", |
|
|
679 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.9326 - accuracy: 0.3810 - val_loss: 1.9436 - val_accuracy: 0.3333\n", |
|
|
680 |
"Epoch 36/1000\n", |
|
|
681 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.7684 - accuracy: 0.4048 - val_loss: 1.5609 - val_accuracy: 0.4444\n", |
|
|
682 |
"Epoch 37/1000\n", |
|
|
683 |
"42/42 [==============================] - 0s 522us/sample - loss: 2.0104 - accuracy: 0.3571 - val_loss: 1.4836 - val_accuracy: 0.4444\n", |
|
|
684 |
"Epoch 38/1000\n", |
|
|
685 |
"42/42 [==============================] - 0s 509us/sample - loss: 1.8930 - accuracy: 0.3333 - val_loss: 1.8343 - val_accuracy: 0.6111\n", |
|
|
686 |
"Epoch 39/1000\n", |
|
|
687 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.8329 - accuracy: 0.4286 - val_loss: 2.0576 - val_accuracy: 0.3333\n", |
|
|
688 |
"Epoch 40/1000\n", |
|
|
689 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.6953 - accuracy: 0.4762 - val_loss: 1.7646 - val_accuracy: 0.3333\n", |
|
|
690 |
"Epoch 41/1000\n", |
|
|
691 |
"42/42 [==============================] - 0s 475us/sample - loss: 1.6919 - accuracy: 0.4524 - val_loss: 1.4386 - val_accuracy: 0.6111\n", |
|
|
692 |
"Epoch 42/1000\n", |
|
|
693 |
"42/42 [==============================] - 0s 474us/sample - loss: 1.6491 - accuracy: 0.3810 - val_loss: 1.3977 - val_accuracy: 0.5556\n", |
|
|
694 |
"Epoch 43/1000\n", |
|
|
695 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.6970 - accuracy: 0.3810 - val_loss: 1.6752 - val_accuracy: 0.4444\n", |
|
|
696 |
"Epoch 44/1000\n", |
|
|
697 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.6027 - accuracy: 0.4286 - val_loss: 1.6268 - val_accuracy: 0.3889\n", |
|
|
698 |
"Epoch 45/1000\n", |
|
|
699 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.6700 - accuracy: 0.4048 - val_loss: 1.5225 - val_accuracy: 0.4444\n", |
|
|
700 |
"Epoch 46/1000\n", |
|
|
701 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.5452 - accuracy: 0.3333 - val_loss: 1.5682 - val_accuracy: 0.4444\n", |
|
|
702 |
"Epoch 47/1000\n", |
|
|
703 |
"42/42 [==============================] - 0s 546us/sample - loss: 1.4730 - accuracy: 0.3810 - val_loss: 1.4717 - val_accuracy: 0.3889\n", |
|
|
704 |
"Epoch 48/1000\n", |
|
|
705 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.6470 - accuracy: 0.4286 - val_loss: 1.5751 - val_accuracy: 0.3889\n", |
|
|
706 |
"Epoch 49/1000\n", |
|
|
707 |
"42/42 [==============================] - 0s 502us/sample - loss: 1.5668 - accuracy: 0.4286 - val_loss: 1.4089 - val_accuracy: 0.5000\n", |
|
|
708 |
"Epoch 50/1000\n", |
|
|
709 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.3410 - accuracy: 0.4524 - val_loss: 1.3377 - val_accuracy: 0.5000\n", |
|
|
710 |
"Epoch 51/1000\n", |
|
|
711 |
"42/42 [==============================] - 0s 475us/sample - loss: 1.4110 - accuracy: 0.4286 - val_loss: 1.4397 - val_accuracy: 0.5556\n", |
|
|
712 |
"Epoch 52/1000\n", |
|
|
713 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.4218 - accuracy: 0.5000 - val_loss: 1.4943 - val_accuracy: 0.3889\n", |
|
|
714 |
"Epoch 53/1000\n", |
|
|
715 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.2684 - accuracy: 0.5238 - val_loss: 1.3571 - val_accuracy: 0.6111\n", |
|
|
716 |
"Epoch 54/1000\n", |
|
|
717 |
"42/42 [==============================] - 0s 474us/sample - loss: 1.3788 - accuracy: 0.5476 - val_loss: 1.2860 - val_accuracy: 0.6111\n", |
|
|
718 |
"Epoch 55/1000\n", |
|
|
719 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.3532 - accuracy: 0.4762 - val_loss: 1.5348 - val_accuracy: 0.5000\n", |
|
|
720 |
"Epoch 56/1000\n" |
|
|
721 |
] |
|
|
722 |
}, |
|
|
723 |
{ |
|
|
724 |
"name": "stdout", |
|
|
725 |
"output_type": "stream", |
|
|
726 |
"text": [ |
|
|
727 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.3708 - accuracy: 0.5714 - val_loss: 1.2765 - val_accuracy: 0.6111\n", |
|
|
728 |
"Epoch 57/1000\n", |
|
|
729 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.4338 - accuracy: 0.5000 - val_loss: 1.3468 - val_accuracy: 0.5556\n", |
|
|
730 |
"Epoch 58/1000\n", |
|
|
731 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.4093 - accuracy: 0.5714 - val_loss: 1.6422 - val_accuracy: 0.3889\n", |
|
|
732 |
"Epoch 59/1000\n", |
|
|
733 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.2424 - accuracy: 0.5476 - val_loss: 1.3783 - val_accuracy: 0.5556\n", |
|
|
734 |
"Epoch 60/1000\n", |
|
|
735 |
"42/42 [==============================] - 0s 476us/sample - loss: 1.3198 - accuracy: 0.5000 - val_loss: 1.3894 - val_accuracy: 0.2778\n", |
|
|
736 |
"Epoch 61/1000\n", |
|
|
737 |
"42/42 [==============================] - 0s 475us/sample - loss: 1.2352 - accuracy: 0.4762 - val_loss: 1.6174 - val_accuracy: 0.4444\n", |
|
|
738 |
"Epoch 62/1000\n", |
|
|
739 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.1687 - accuracy: 0.6190 - val_loss: 1.5043 - val_accuracy: 0.5556\n", |
|
|
740 |
"Epoch 63/1000\n", |
|
|
741 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.2531 - accuracy: 0.5476 - val_loss: 1.4356 - val_accuracy: 0.5556\n", |
|
|
742 |
"Epoch 64/1000\n", |
|
|
743 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.3530 - accuracy: 0.4524 - val_loss: 1.4294 - val_accuracy: 0.5000\n", |
|
|
744 |
"Epoch 65/1000\n", |
|
|
745 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.1638 - accuracy: 0.5714 - val_loss: 1.5653 - val_accuracy: 0.4444\n", |
|
|
746 |
"Epoch 66/1000\n", |
|
|
747 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.2599 - accuracy: 0.4286 - val_loss: 1.4372 - val_accuracy: 0.4444\n", |
|
|
748 |
"Epoch 67/1000\n", |
|
|
749 |
"42/42 [==============================] - 0s 534us/sample - loss: 1.1854 - accuracy: 0.5238 - val_loss: 1.2849 - val_accuracy: 0.6111\n", |
|
|
750 |
"Epoch 68/1000\n", |
|
|
751 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.1208 - accuracy: 0.5952 - val_loss: 1.3461 - val_accuracy: 0.5556\n", |
|
|
752 |
"Epoch 69/1000\n", |
|
|
753 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.1763 - accuracy: 0.5714 - val_loss: 1.5024 - val_accuracy: 0.4444\n", |
|
|
754 |
"Epoch 70/1000\n", |
|
|
755 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.9834 - accuracy: 0.7381 - val_loss: 1.3609 - val_accuracy: 0.5556\n", |
|
|
756 |
"Epoch 71/1000\n", |
|
|
757 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.9459 - accuracy: 0.6429 - val_loss: 1.2951 - val_accuracy: 0.6111\n", |
|
|
758 |
"Epoch 72/1000\n", |
|
|
759 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.1571 - accuracy: 0.6190 - val_loss: 1.2985 - val_accuracy: 0.6111\n", |
|
|
760 |
"Epoch 73/1000\n", |
|
|
761 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.2052 - accuracy: 0.5000 - val_loss: 1.3227 - val_accuracy: 0.3889\n", |
|
|
762 |
"Epoch 74/1000\n", |
|
|
763 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.0881 - accuracy: 0.5476 - val_loss: 1.3341 - val_accuracy: 0.7778\n", |
|
|
764 |
"Epoch 75/1000\n", |
|
|
765 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.8642 - accuracy: 0.7381 - val_loss: 1.3610 - val_accuracy: 0.5556\n", |
|
|
766 |
"Epoch 76/1000\n", |
|
|
767 |
"42/42 [==============================] - 0s 522us/sample - loss: 1.1425 - accuracy: 0.6667 - val_loss: 1.3434 - val_accuracy: 0.5556\n", |
|
|
768 |
"Epoch 77/1000\n", |
|
|
769 |
"42/42 [==============================] - 0s 499us/sample - loss: 1.0876 - accuracy: 0.6429 - val_loss: 1.2067 - val_accuracy: 0.5556\n", |
|
|
770 |
"Epoch 78/1000\n", |
|
|
771 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.9382 - accuracy: 0.6429 - val_loss: 1.2852 - val_accuracy: 0.4444\n", |
|
|
772 |
"Epoch 79/1000\n", |
|
|
773 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.1331 - accuracy: 0.5000 - val_loss: 1.3295 - val_accuracy: 0.6111\n", |
|
|
774 |
"Epoch 80/1000\n", |
|
|
775 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.8755 - accuracy: 0.6667 - val_loss: 1.3785 - val_accuracy: 0.4444\n", |
|
|
776 |
"Epoch 81/1000\n", |
|
|
777 |
"42/42 [==============================] - 0s 498us/sample - loss: 1.0149 - accuracy: 0.6429 - val_loss: 1.2789 - val_accuracy: 0.6111\n", |
|
|
778 |
"Epoch 82/1000\n", |
|
|
779 |
"42/42 [==============================] - 0s 494us/sample - loss: 1.0701 - accuracy: 0.6429 - val_loss: 1.2478 - val_accuracy: 0.6667\n", |
|
|
780 |
"Epoch 83/1000\n", |
|
|
781 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.8065 - accuracy: 0.7143 - val_loss: 1.3094 - val_accuracy: 0.5556\n", |
|
|
782 |
"Epoch 84/1000\n", |
|
|
783 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.9520 - accuracy: 0.6429 - val_loss: 1.2710 - val_accuracy: 0.6111\n", |
|
|
784 |
"Epoch 85/1000\n", |
|
|
785 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.9161 - accuracy: 0.6905 - val_loss: 1.2435 - val_accuracy: 0.6111\n", |
|
|
786 |
"Epoch 86/1000\n", |
|
|
787 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.7968 - accuracy: 0.7143 - val_loss: 1.3438 - val_accuracy: 0.5556\n", |
|
|
788 |
"Epoch 87/1000\n", |
|
|
789 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.7942 - accuracy: 0.7857 - val_loss: 1.3111 - val_accuracy: 0.6111\n", |
|
|
790 |
"Epoch 88/1000\n", |
|
|
791 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.8617 - accuracy: 0.7143 - val_loss: 1.2988 - val_accuracy: 0.7222\n", |
|
|
792 |
"Epoch 89/1000\n", |
|
|
793 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.9575 - accuracy: 0.6667 - val_loss: 1.5028 - val_accuracy: 0.5556\n", |
|
|
794 |
"Epoch 90/1000\n", |
|
|
795 |
"42/42 [==============================] - 0s 493us/sample - loss: 0.9668 - accuracy: 0.5952 - val_loss: 1.4931 - val_accuracy: 0.5556\n", |
|
|
796 |
"Epoch 91/1000\n", |
|
|
797 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.8176 - accuracy: 0.6667 - val_loss: 1.2910 - val_accuracy: 0.6111\n", |
|
|
798 |
"Epoch 92/1000\n", |
|
|
799 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.8502 - accuracy: 0.6667 - val_loss: 1.2679 - val_accuracy: 0.6111\n", |
|
|
800 |
"Epoch 93/1000\n", |
|
|
801 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.8648 - accuracy: 0.6429 - val_loss: 1.3818 - val_accuracy: 0.5556\n", |
|
|
802 |
"Epoch 94/1000\n", |
|
|
803 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.7963 - accuracy: 0.6905 - val_loss: 1.4288 - val_accuracy: 0.5556\n", |
|
|
804 |
"Epoch 95/1000\n", |
|
|
805 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.7602 - accuracy: 0.6190 - val_loss: 1.3318 - val_accuracy: 0.5556\n", |
|
|
806 |
"Epoch 96/1000\n", |
|
|
807 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.8982 - accuracy: 0.6190 - val_loss: 1.3177 - val_accuracy: 0.6111\n", |
|
|
808 |
"Epoch 97/1000\n", |
|
|
809 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.7505 - accuracy: 0.7381 - val_loss: 1.2973 - val_accuracy: 0.7778\n", |
|
|
810 |
"Epoch 98/1000\n", |
|
|
811 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.8469 - accuracy: 0.7143 - val_loss: 1.3842 - val_accuracy: 0.5556\n", |
|
|
812 |
"Epoch 99/1000\n", |
|
|
813 |
"42/42 [==============================] - 0s 524us/sample - loss: 0.7213 - accuracy: 0.7857 - val_loss: 1.3662 - val_accuracy: 0.5556\n", |
|
|
814 |
"Epoch 100/1000\n", |
|
|
815 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.7243 - accuracy: 0.7619 - val_loss: 1.3434 - val_accuracy: 0.6111\n", |
|
|
816 |
"Epoch 101/1000\n", |
|
|
817 |
"42/42 [==============================] - 0s 526us/sample - loss: 0.6614 - accuracy: 0.8095 - val_loss: 1.3186 - val_accuracy: 0.6111\n", |
|
|
818 |
"Epoch 102/1000\n", |
|
|
819 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.5414 - accuracy: 0.8810 - val_loss: 1.3502 - val_accuracy: 0.5556\n", |
|
|
820 |
"Epoch 103/1000\n", |
|
|
821 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.7590 - accuracy: 0.7619 - val_loss: 1.5070 - val_accuracy: 0.5556\n", |
|
|
822 |
"Epoch 104/1000\n", |
|
|
823 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.6901 - accuracy: 0.7619 - val_loss: 1.5282 - val_accuracy: 0.6111\n", |
|
|
824 |
"Epoch 105/1000\n", |
|
|
825 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.7882 - accuracy: 0.8333 - val_loss: 1.5221 - val_accuracy: 0.6111\n", |
|
|
826 |
"Epoch 106/1000\n", |
|
|
827 |
"42/42 [==============================] - 0s 506us/sample - loss: 0.7534 - accuracy: 0.7381 - val_loss: 1.3149 - val_accuracy: 0.6111\n", |
|
|
828 |
"Epoch 107/1000\n", |
|
|
829 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.6212 - accuracy: 0.7381 - val_loss: 1.3208 - val_accuracy: 0.5556\n", |
|
|
830 |
"Epoch 108/1000\n", |
|
|
831 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.5400 - accuracy: 0.8095 - val_loss: 1.3552 - val_accuracy: 0.5556\n", |
|
|
832 |
"Epoch 109/1000\n", |
|
|
833 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.5943 - accuracy: 0.7857 - val_loss: 1.3896 - val_accuracy: 0.6111\n", |
|
|
834 |
"Epoch 110/1000\n", |
|
|
835 |
"42/42 [==============================] - 0s 570us/sample - loss: 0.6683 - accuracy: 0.8095 - val_loss: 1.3408 - val_accuracy: 0.7222\n", |
|
|
836 |
"Epoch 111/1000\n", |
|
|
837 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.6069 - accuracy: 0.7619 - val_loss: 1.3849 - val_accuracy: 0.6111\n" |
|
|
838 |
] |
|
|
839 |
}, |
|
|
840 |
{ |
|
|
841 |
"name": "stdout", |
|
|
842 |
"output_type": "stream", |
|
|
843 |
"text": [ |
|
|
844 |
"Epoch 112/1000\n", |
|
|
845 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.5981 - accuracy: 0.7857 - val_loss: 1.4287 - val_accuracy: 0.6111\n", |
|
|
846 |
"Epoch 113/1000\n", |
|
|
847 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.5869 - accuracy: 0.7857 - val_loss: 1.3265 - val_accuracy: 0.6667\n", |
|
|
848 |
"Epoch 114/1000\n", |
|
|
849 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.6628 - accuracy: 0.7143 - val_loss: 1.2668 - val_accuracy: 0.6111\n", |
|
|
850 |
"Epoch 115/1000\n", |
|
|
851 |
"42/42 [==============================] - 0s 547us/sample - loss: 0.5739 - accuracy: 0.8333 - val_loss: 1.2277 - val_accuracy: 0.6111\n", |
|
|
852 |
"Epoch 116/1000\n", |
|
|
853 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.5122 - accuracy: 0.7619 - val_loss: 1.3263 - val_accuracy: 0.6111\n", |
|
|
854 |
"Epoch 117/1000\n", |
|
|
855 |
"42/42 [==============================] - 0s 488us/sample - loss: 0.5271 - accuracy: 0.8333 - val_loss: 1.4577 - val_accuracy: 0.6111\n", |
|
|
856 |
"Epoch 118/1000\n", |
|
|
857 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.6416 - accuracy: 0.7619 - val_loss: 1.3266 - val_accuracy: 0.7222\n", |
|
|
858 |
"Epoch 119/1000\n", |
|
|
859 |
"42/42 [==============================] - 0s 521us/sample - loss: 0.4774 - accuracy: 0.8571 - val_loss: 1.3611 - val_accuracy: 0.6667\n", |
|
|
860 |
"Epoch 120/1000\n", |
|
|
861 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5926 - accuracy: 0.7619 - val_loss: 1.5968 - val_accuracy: 0.5556\n", |
|
|
862 |
"Epoch 121/1000\n", |
|
|
863 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5720 - accuracy: 0.7619 - val_loss: 1.5873 - val_accuracy: 0.5556\n", |
|
|
864 |
"Epoch 122/1000\n", |
|
|
865 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5887 - accuracy: 0.7857 - val_loss: 1.3660 - val_accuracy: 0.6111\n", |
|
|
866 |
"Epoch 123/1000\n", |
|
|
867 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.4295 - accuracy: 0.9048 - val_loss: 1.2827 - val_accuracy: 0.6111\n", |
|
|
868 |
"Epoch 124/1000\n", |
|
|
869 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.5541 - accuracy: 0.8095 - val_loss: 1.2845 - val_accuracy: 0.6111\n", |
|
|
870 |
"Epoch 125/1000\n", |
|
|
871 |
"42/42 [==============================] - 0s 535us/sample - loss: 0.5073 - accuracy: 0.8571 - val_loss: 1.4737 - val_accuracy: 0.6111\n", |
|
|
872 |
"Epoch 126/1000\n", |
|
|
873 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.5039 - accuracy: 0.7619 - val_loss: 1.5981 - val_accuracy: 0.6111\n", |
|
|
874 |
"Epoch 127/1000\n", |
|
|
875 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5066 - accuracy: 0.8095 - val_loss: 1.5741 - val_accuracy: 0.6667\n", |
|
|
876 |
"Epoch 128/1000\n", |
|
|
877 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.6531 - accuracy: 0.7857 - val_loss: 1.3181 - val_accuracy: 0.7222\n", |
|
|
878 |
"Epoch 129/1000\n", |
|
|
879 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.4011 - accuracy: 0.8571 - val_loss: 1.3502 - val_accuracy: 0.6111\n", |
|
|
880 |
"Epoch 130/1000\n", |
|
|
881 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5504 - accuracy: 0.8095 - val_loss: 1.3530 - val_accuracy: 0.6111\n", |
|
|
882 |
"Epoch 131/1000\n", |
|
|
883 |
"42/42 [==============================] - 0s 502us/sample - loss: 0.4450 - accuracy: 0.8571 - val_loss: 1.3671 - val_accuracy: 0.6667\n", |
|
|
884 |
"Epoch 132/1000\n", |
|
|
885 |
"42/42 [==============================] - 0s 513us/sample - loss: 0.4575 - accuracy: 0.9048 - val_loss: 1.4184 - val_accuracy: 0.7222\n", |
|
|
886 |
"Epoch 133/1000\n", |
|
|
887 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3905 - accuracy: 0.9048 - val_loss: 1.3906 - val_accuracy: 0.6667\n", |
|
|
888 |
"Epoch 134/1000\n", |
|
|
889 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3375 - accuracy: 0.8810 - val_loss: 1.3120 - val_accuracy: 0.7222\n", |
|
|
890 |
"Epoch 135/1000\n", |
|
|
891 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.4239 - accuracy: 0.8095 - val_loss: 1.3278 - val_accuracy: 0.6111\n", |
|
|
892 |
"Epoch 136/1000\n", |
|
|
893 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3923 - accuracy: 0.8810 - val_loss: 1.4095 - val_accuracy: 0.6667\n", |
|
|
894 |
"Epoch 137/1000\n", |
|
|
895 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.4219 - accuracy: 0.9048 - val_loss: 1.3947 - val_accuracy: 0.7222\n", |
|
|
896 |
"Epoch 138/1000\n", |
|
|
897 |
"42/42 [==============================] - ETA: 0s - loss: 0.2393 - accuracy: 0.93 - 0s 522us/sample - loss: 0.3436 - accuracy: 0.9048 - val_loss: 1.4442 - val_accuracy: 0.7222\n", |
|
|
898 |
"Epoch 139/1000\n", |
|
|
899 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5216 - accuracy: 0.8571 - val_loss: 1.3975 - val_accuracy: 0.5556\n", |
|
|
900 |
"Epoch 140/1000\n", |
|
|
901 |
"42/42 [==============================] - 0s 519us/sample - loss: 0.2948 - accuracy: 0.9048 - val_loss: 1.3162 - val_accuracy: 0.6111\n", |
|
|
902 |
"Epoch 141/1000\n", |
|
|
903 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.5027 - accuracy: 0.8810 - val_loss: 1.4737 - val_accuracy: 0.5556\n", |
|
|
904 |
"Epoch 142/1000\n", |
|
|
905 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.4558 - accuracy: 0.7857 - val_loss: 1.5471 - val_accuracy: 0.6667\n", |
|
|
906 |
"Epoch 143/1000\n", |
|
|
907 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.4080 - accuracy: 0.9286 - val_loss: 1.3486 - val_accuracy: 0.6111\n", |
|
|
908 |
"Epoch 144/1000\n", |
|
|
909 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.4701 - accuracy: 0.8571 - val_loss: 1.3295 - val_accuracy: 0.6111\n", |
|
|
910 |
"Epoch 145/1000\n", |
|
|
911 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.5379 - accuracy: 0.7381 - val_loss: 1.1999 - val_accuracy: 0.5556\n", |
|
|
912 |
"Epoch 146/1000\n", |
|
|
913 |
"42/42 [==============================] - 0s 531us/sample - loss: 0.4393 - accuracy: 0.7857 - val_loss: 1.3320 - val_accuracy: 0.6111\n", |
|
|
914 |
"Epoch 147/1000\n", |
|
|
915 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3524 - accuracy: 0.8333 - val_loss: 1.3963 - val_accuracy: 0.6667\n", |
|
|
916 |
"Epoch 148/1000\n", |
|
|
917 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3058 - accuracy: 0.9048 - val_loss: 1.4654 - val_accuracy: 0.7222\n", |
|
|
918 |
"Epoch 149/1000\n", |
|
|
919 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3391 - accuracy: 0.9286 - val_loss: 1.5196 - val_accuracy: 0.6667\n", |
|
|
920 |
"Epoch 150/1000\n", |
|
|
921 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.3815 - accuracy: 0.8571 - val_loss: 1.3109 - val_accuracy: 0.7222\n", |
|
|
922 |
"Epoch 151/1000\n", |
|
|
923 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2462 - accuracy: 0.9762 - val_loss: 1.1905 - val_accuracy: 0.6667\n", |
|
|
924 |
"Epoch 152/1000\n", |
|
|
925 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.2604 - accuracy: 0.9286 - val_loss: 1.2270 - val_accuracy: 0.5556\n", |
|
|
926 |
"Epoch 153/1000\n", |
|
|
927 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.4417 - accuracy: 0.8333 - val_loss: 1.3705 - val_accuracy: 0.6667\n", |
|
|
928 |
"Epoch 154/1000\n", |
|
|
929 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.3130 - accuracy: 0.8571 - val_loss: 1.4149 - val_accuracy: 0.6111\n", |
|
|
930 |
"Epoch 155/1000\n", |
|
|
931 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3387 - accuracy: 0.8810 - val_loss: 1.2840 - val_accuracy: 0.7222\n", |
|
|
932 |
"Epoch 156/1000\n", |
|
|
933 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.3904 - accuracy: 0.8810 - val_loss: 1.2917 - val_accuracy: 0.6667\n", |
|
|
934 |
"Epoch 157/1000\n", |
|
|
935 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2901 - accuracy: 0.8810 - val_loss: 1.4299 - val_accuracy: 0.7222\n", |
|
|
936 |
"Epoch 158/1000\n", |
|
|
937 |
"42/42 [==============================] - 0s 502us/sample - loss: 0.2498 - accuracy: 1.0000 - val_loss: 1.6236 - val_accuracy: 0.6111\n", |
|
|
938 |
"Epoch 159/1000\n", |
|
|
939 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.3456 - accuracy: 0.9048 - val_loss: 1.4452 - val_accuracy: 0.6111\n", |
|
|
940 |
"Epoch 160/1000\n", |
|
|
941 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.3220 - accuracy: 0.9286 - val_loss: 1.2209 - val_accuracy: 0.6111\n", |
|
|
942 |
"Epoch 161/1000\n", |
|
|
943 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3639 - accuracy: 0.9286 - val_loss: 1.2428 - val_accuracy: 0.6111\n", |
|
|
944 |
"Epoch 162/1000\n", |
|
|
945 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.3468 - accuracy: 0.8810 - val_loss: 1.3792 - val_accuracy: 0.6111\n", |
|
|
946 |
"Epoch 163/1000\n", |
|
|
947 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2670 - accuracy: 0.9286 - val_loss: 1.3368 - val_accuracy: 0.6667\n", |
|
|
948 |
"Epoch 164/1000\n", |
|
|
949 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.3264 - accuracy: 0.9048 - val_loss: 1.4322 - val_accuracy: 0.6667\n", |
|
|
950 |
"Epoch 165/1000\n", |
|
|
951 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2164 - accuracy: 0.9762 - val_loss: 1.5711 - val_accuracy: 0.6111\n", |
|
|
952 |
"Epoch 166/1000\n", |
|
|
953 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2636 - accuracy: 0.8810 - val_loss: 1.4074 - val_accuracy: 0.6667\n", |
|
|
954 |
"Epoch 167/1000\n" |
|
|
955 |
] |
|
|
956 |
}, |
|
|
957 |
{ |
|
|
958 |
"name": "stdout", |
|
|
959 |
"output_type": "stream", |
|
|
960 |
"text": [ |
|
|
961 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.2109 - accuracy: 0.9524 - val_loss: 1.2906 - val_accuracy: 0.6667\n", |
|
|
962 |
"Epoch 168/1000\n", |
|
|
963 |
"42/42 [==============================] - 0s 545us/sample - loss: 0.2772 - accuracy: 0.9524 - val_loss: 1.2159 - val_accuracy: 0.6667\n", |
|
|
964 |
"Epoch 169/1000\n", |
|
|
965 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2826 - accuracy: 0.9762 - val_loss: 1.3507 - val_accuracy: 0.6111\n", |
|
|
966 |
"Epoch 170/1000\n", |
|
|
967 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2746 - accuracy: 0.9048 - val_loss: 1.4001 - val_accuracy: 0.6667\n", |
|
|
968 |
"Epoch 171/1000\n", |
|
|
969 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.2954 - accuracy: 0.9048 - val_loss: 1.3653 - val_accuracy: 0.6667\n", |
|
|
970 |
"Epoch 172/1000\n", |
|
|
971 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.2657 - accuracy: 0.9286 - val_loss: 1.4886 - val_accuracy: 0.7222\n", |
|
|
972 |
"Epoch 173/1000\n", |
|
|
973 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2520 - accuracy: 0.9286 - val_loss: 1.5264 - val_accuracy: 0.7222\n", |
|
|
974 |
"Epoch 174/1000\n", |
|
|
975 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2114 - accuracy: 0.9762 - val_loss: 1.6685 - val_accuracy: 0.5556\n", |
|
|
976 |
"Epoch 175/1000\n", |
|
|
977 |
"42/42 [==============================] - 0s 507us/sample - loss: 0.2784 - accuracy: 0.8810 - val_loss: 1.4847 - val_accuracy: 0.5556\n", |
|
|
978 |
"Epoch 176/1000\n", |
|
|
979 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.2897 - accuracy: 0.9048 - val_loss: 1.2644 - val_accuracy: 0.6111\n", |
|
|
980 |
"Epoch 177/1000\n", |
|
|
981 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.2964 - accuracy: 0.9524 - val_loss: 1.2995 - val_accuracy: 0.6111\n", |
|
|
982 |
"Epoch 178/1000\n", |
|
|
983 |
"42/42 [==============================] - 0s 504us/sample - loss: 0.1934 - accuracy: 0.9286 - val_loss: 1.4055 - val_accuracy: 0.6667\n", |
|
|
984 |
"Epoch 179/1000\n", |
|
|
985 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2750 - accuracy: 0.9286 - val_loss: 1.3978 - val_accuracy: 0.7222\n", |
|
|
986 |
"Epoch 180/1000\n", |
|
|
987 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2430 - accuracy: 0.9524 - val_loss: 1.4521 - val_accuracy: 0.6111\n", |
|
|
988 |
"Epoch 181/1000\n", |
|
|
989 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.2541 - accuracy: 0.9048 - val_loss: 1.5047 - val_accuracy: 0.6667\n", |
|
|
990 |
"Epoch 182/1000\n", |
|
|
991 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2577 - accuracy: 0.9048 - val_loss: 1.5987 - val_accuracy: 0.6111\n", |
|
|
992 |
"Epoch 183/1000\n", |
|
|
993 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.2987 - accuracy: 0.9048 - val_loss: 1.4942 - val_accuracy: 0.7222\n", |
|
|
994 |
"Epoch 184/1000\n", |
|
|
995 |
"42/42 [==============================] - 0s 525us/sample - loss: 0.2072 - accuracy: 0.9524 - val_loss: 1.4459 - val_accuracy: 0.6667\n", |
|
|
996 |
"Epoch 185/1000\n", |
|
|
997 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2000 - accuracy: 0.9762 - val_loss: 1.4378 - val_accuracy: 0.6667\n", |
|
|
998 |
"Epoch 186/1000\n", |
|
|
999 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1821 - accuracy: 0.9524 - val_loss: 1.4571 - val_accuracy: 0.6667\n", |
|
|
1000 |
"Epoch 187/1000\n", |
|
|
1001 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1727 - accuracy: 0.9762 - val_loss: 1.4329 - val_accuracy: 0.6667\n", |
|
|
1002 |
"Epoch 188/1000\n", |
|
|
1003 |
"42/42 [==============================] - 0s 519us/sample - loss: 0.3053 - accuracy: 0.8810 - val_loss: 1.4808 - val_accuracy: 0.6111\n", |
|
|
1004 |
"Epoch 189/1000\n", |
|
|
1005 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2105 - accuracy: 0.9286 - val_loss: 1.4716 - val_accuracy: 0.6667\n", |
|
|
1006 |
"Epoch 190/1000\n", |
|
|
1007 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.2599 - accuracy: 0.9048 - val_loss: 1.4933 - val_accuracy: 0.6667\n", |
|
|
1008 |
"Epoch 191/1000\n", |
|
|
1009 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2099 - accuracy: 0.9286 - val_loss: 1.4783 - val_accuracy: 0.6111\n", |
|
|
1010 |
"Epoch 192/1000\n", |
|
|
1011 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.2162 - accuracy: 0.9524 - val_loss: 1.6410 - val_accuracy: 0.6111\n", |
|
|
1012 |
"Epoch 193/1000\n", |
|
|
1013 |
"42/42 [==============================] - 0s 510us/sample - loss: 0.1764 - accuracy: 0.9524 - val_loss: 1.7848 - val_accuracy: 0.6667\n", |
|
|
1014 |
"Epoch 194/1000\n", |
|
|
1015 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.3305 - accuracy: 0.8095 - val_loss: 1.5658 - val_accuracy: 0.6667\n", |
|
|
1016 |
"Epoch 195/1000\n", |
|
|
1017 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.2349 - accuracy: 0.9048 - val_loss: 1.4072 - val_accuracy: 0.6111\n", |
|
|
1018 |
"Epoch 196/1000\n", |
|
|
1019 |
"42/42 [==============================] - 0s 503us/sample - loss: 0.2535 - accuracy: 0.9286 - val_loss: 1.4303 - val_accuracy: 0.7778\n", |
|
|
1020 |
"Epoch 197/1000\n", |
|
|
1021 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2106 - accuracy: 0.9524 - val_loss: 1.8046 - val_accuracy: 0.6111\n", |
|
|
1022 |
"Epoch 198/1000\n", |
|
|
1023 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.2991 - accuracy: 0.8571 - val_loss: 1.5661 - val_accuracy: 0.6111\n", |
|
|
1024 |
"Epoch 199/1000\n", |
|
|
1025 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1100 - accuracy: 1.0000 - val_loss: 1.2853 - val_accuracy: 0.6111\n", |
|
|
1026 |
"Epoch 200/1000\n", |
|
|
1027 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.3683 - accuracy: 0.8810 - val_loss: 1.2990 - val_accuracy: 0.6111\n", |
|
|
1028 |
"Epoch 201/1000\n", |
|
|
1029 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.2548 - accuracy: 0.9048 - val_loss: 1.6909 - val_accuracy: 0.6111\n", |
|
|
1030 |
"Epoch 202/1000\n", |
|
|
1031 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.2357 - accuracy: 0.9286 - val_loss: 1.7610 - val_accuracy: 0.6111\n", |
|
|
1032 |
"Epoch 203/1000\n", |
|
|
1033 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.2139 - accuracy: 0.9524 - val_loss: 1.5678 - val_accuracy: 0.7222\n", |
|
|
1034 |
"Epoch 204/1000\n", |
|
|
1035 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.2450 - accuracy: 0.9048 - val_loss: 1.2424 - val_accuracy: 0.6111\n", |
|
|
1036 |
"Epoch 205/1000\n", |
|
|
1037 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1888 - accuracy: 0.9524 - val_loss: 1.2910 - val_accuracy: 0.7222\n", |
|
|
1038 |
"Epoch 206/1000\n", |
|
|
1039 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1993 - accuracy: 0.9048 - val_loss: 1.5787 - val_accuracy: 0.6667\n", |
|
|
1040 |
"Epoch 207/1000\n", |
|
|
1041 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.2399 - accuracy: 0.8810 - val_loss: 1.6135 - val_accuracy: 0.7222\n", |
|
|
1042 |
"Epoch 208/1000\n", |
|
|
1043 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.1629 - accuracy: 0.9524 - val_loss: 1.6286 - val_accuracy: 0.6667\n", |
|
|
1044 |
"Epoch 209/1000\n", |
|
|
1045 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1837 - accuracy: 0.9524 - val_loss: 1.6169 - val_accuracy: 0.6667\n", |
|
|
1046 |
"Epoch 210/1000\n", |
|
|
1047 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1663 - accuracy: 0.9286 - val_loss: 1.4026 - val_accuracy: 0.7222\n", |
|
|
1048 |
"Epoch 211/1000\n", |
|
|
1049 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1736 - accuracy: 0.9762 - val_loss: 1.4212 - val_accuracy: 0.6667\n", |
|
|
1050 |
"Epoch 212/1000\n", |
|
|
1051 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1526 - accuracy: 0.9524 - val_loss: 1.5088 - val_accuracy: 0.6667\n", |
|
|
1052 |
"Epoch 213/1000\n", |
|
|
1053 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1848 - accuracy: 0.9524 - val_loss: 1.4536 - val_accuracy: 0.6667\n", |
|
|
1054 |
"Epoch 214/1000\n", |
|
|
1055 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1586 - accuracy: 0.9524 - val_loss: 1.4667 - val_accuracy: 0.6667\n", |
|
|
1056 |
"Epoch 215/1000\n", |
|
|
1057 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1498 - accuracy: 0.9524 - val_loss: 1.3854 - val_accuracy: 0.6667\n", |
|
|
1058 |
"Epoch 216/1000\n", |
|
|
1059 |
"42/42 [==============================] - 0s 497us/sample - loss: 0.1508 - accuracy: 0.9762 - val_loss: 1.5091 - val_accuracy: 0.6667\n", |
|
|
1060 |
"Epoch 217/1000\n", |
|
|
1061 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1196 - accuracy: 0.9762 - val_loss: 1.6418 - val_accuracy: 0.6667\n", |
|
|
1062 |
"Epoch 218/1000\n", |
|
|
1063 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1698 - accuracy: 0.9762 - val_loss: 1.5422 - val_accuracy: 0.6667\n", |
|
|
1064 |
"Epoch 219/1000\n", |
|
|
1065 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1564 - accuracy: 0.9762 - val_loss: 1.3993 - val_accuracy: 0.6667\n", |
|
|
1066 |
"Epoch 220/1000\n", |
|
|
1067 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1170 - accuracy: 1.0000 - val_loss: 1.4528 - val_accuracy: 0.6667\n", |
|
|
1068 |
"Epoch 221/1000\n", |
|
|
1069 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1398 - accuracy: 0.9762 - val_loss: 1.4157 - val_accuracy: 0.7222\n", |
|
|
1070 |
"Epoch 222/1000\n" |
|
|
1071 |
] |
|
|
1072 |
}, |
|
|
1073 |
{ |
|
|
1074 |
"name": "stdout", |
|
|
1075 |
"output_type": "stream", |
|
|
1076 |
"text": [ |
|
|
1077 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1779 - accuracy: 0.9286 - val_loss: 1.3852 - val_accuracy: 0.6667\n", |
|
|
1078 |
"Epoch 223/1000\n", |
|
|
1079 |
"42/42 [==============================] - 0s 519us/sample - loss: 0.1423 - accuracy: 0.9286 - val_loss: 1.5236 - val_accuracy: 0.6667\n", |
|
|
1080 |
"Epoch 224/1000\n", |
|
|
1081 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.1498 - accuracy: 0.9524 - val_loss: 1.6143 - val_accuracy: 0.7222\n", |
|
|
1082 |
"Epoch 225/1000\n", |
|
|
1083 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.1108 - accuracy: 0.9762 - val_loss: 1.5991 - val_accuracy: 0.7222\n", |
|
|
1084 |
"Epoch 226/1000\n", |
|
|
1085 |
"42/42 [==============================] - 0s 503us/sample - loss: 0.1224 - accuracy: 0.9762 - val_loss: 1.4466 - val_accuracy: 0.7222\n", |
|
|
1086 |
"Epoch 227/1000\n", |
|
|
1087 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1121 - accuracy: 0.9762 - val_loss: 1.4024 - val_accuracy: 0.7222\n", |
|
|
1088 |
"Epoch 228/1000\n", |
|
|
1089 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.1111 - accuracy: 1.0000 - val_loss: 1.4997 - val_accuracy: 0.7222\n", |
|
|
1090 |
"Epoch 229/1000\n", |
|
|
1091 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1449 - accuracy: 0.9524 - val_loss: 1.4687 - val_accuracy: 0.7222\n", |
|
|
1092 |
"Epoch 230/1000\n", |
|
|
1093 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1451 - accuracy: 0.9762 - val_loss: 1.3025 - val_accuracy: 0.7222\n", |
|
|
1094 |
"Epoch 231/1000\n", |
|
|
1095 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1367 - accuracy: 0.9762 - val_loss: 1.3195 - val_accuracy: 0.7222\n", |
|
|
1096 |
"Epoch 232/1000\n", |
|
|
1097 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1176 - accuracy: 1.0000 - val_loss: 1.3335 - val_accuracy: 0.7222\n", |
|
|
1098 |
"Epoch 233/1000\n", |
|
|
1099 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.1419 - accuracy: 1.0000 - val_loss: 1.4469 - val_accuracy: 0.7222\n", |
|
|
1100 |
"Epoch 234/1000\n", |
|
|
1101 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.1429 - accuracy: 0.9762 - val_loss: 1.4108 - val_accuracy: 0.7222\n", |
|
|
1102 |
"Epoch 235/1000\n", |
|
|
1103 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1602 - accuracy: 0.9524 - val_loss: 1.4446 - val_accuracy: 0.7222\n", |
|
|
1104 |
"Epoch 236/1000\n", |
|
|
1105 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0859 - accuracy: 1.0000 - val_loss: 1.5244 - val_accuracy: 0.7222\n", |
|
|
1106 |
"Epoch 237/1000\n", |
|
|
1107 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.1656 - accuracy: 0.9286 - val_loss: 1.3852 - val_accuracy: 0.6667\n", |
|
|
1108 |
"Epoch 238/1000\n", |
|
|
1109 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1231 - accuracy: 1.0000 - val_loss: 1.3210 - val_accuracy: 0.7222\n", |
|
|
1110 |
"Epoch 239/1000\n", |
|
|
1111 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1510 - accuracy: 0.9524 - val_loss: 1.4147 - val_accuracy: 0.7222\n", |
|
|
1112 |
"Epoch 240/1000\n", |
|
|
1113 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1310 - accuracy: 0.9762 - val_loss: 1.4147 - val_accuracy: 0.7222\n", |
|
|
1114 |
"Epoch 241/1000\n", |
|
|
1115 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1734 - accuracy: 0.9524 - val_loss: 1.3937 - val_accuracy: 0.7222\n", |
|
|
1116 |
"Epoch 242/1000\n", |
|
|
1117 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1379 - accuracy: 0.9762 - val_loss: 1.4777 - val_accuracy: 0.6667\n", |
|
|
1118 |
"Epoch 243/1000\n", |
|
|
1119 |
"42/42 [==============================] - 0s 558us/sample - loss: 0.0989 - accuracy: 0.9762 - val_loss: 1.4941 - val_accuracy: 0.7222\n", |
|
|
1120 |
"Epoch 244/1000\n", |
|
|
1121 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0969 - accuracy: 1.0000 - val_loss: 1.4370 - val_accuracy: 0.6667\n", |
|
|
1122 |
"Epoch 245/1000\n", |
|
|
1123 |
"42/42 [==============================] - 0s 547us/sample - loss: 0.1201 - accuracy: 0.9762 - val_loss: 1.5204 - val_accuracy: 0.6667\n", |
|
|
1124 |
"Epoch 246/1000\n", |
|
|
1125 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0931 - accuracy: 1.0000 - val_loss: 1.6991 - val_accuracy: 0.6667\n", |
|
|
1126 |
"Epoch 247/1000\n", |
|
|
1127 |
"42/42 [==============================] - 0s 514us/sample - loss: 0.0992 - accuracy: 1.0000 - val_loss: 1.6141 - val_accuracy: 0.6667\n", |
|
|
1128 |
"Epoch 248/1000\n", |
|
|
1129 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1349 - accuracy: 0.9762 - val_loss: 1.1670 - val_accuracy: 0.6667\n", |
|
|
1130 |
"Epoch 249/1000\n", |
|
|
1131 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1599 - accuracy: 0.9762 - val_loss: 1.1888 - val_accuracy: 0.6111\n", |
|
|
1132 |
"Epoch 250/1000\n", |
|
|
1133 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1162 - accuracy: 0.9286 - val_loss: 1.5540 - val_accuracy: 0.6667\n", |
|
|
1134 |
"Epoch 251/1000\n", |
|
|
1135 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1271 - accuracy: 0.9524 - val_loss: 1.6082 - val_accuracy: 0.6111\n", |
|
|
1136 |
"Epoch 252/1000\n", |
|
|
1137 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1675 - accuracy: 0.9286 - val_loss: 1.2768 - val_accuracy: 0.6667\n", |
|
|
1138 |
"Epoch 253/1000\n", |
|
|
1139 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1417 - accuracy: 0.9762 - val_loss: 1.1588 - val_accuracy: 0.6111\n", |
|
|
1140 |
"Epoch 254/1000\n", |
|
|
1141 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1877 - accuracy: 0.9048 - val_loss: 1.3617 - val_accuracy: 0.7222\n", |
|
|
1142 |
"Epoch 255/1000\n", |
|
|
1143 |
"42/42 [==============================] - 0s 524us/sample - loss: 0.1299 - accuracy: 0.9524 - val_loss: 1.6291 - val_accuracy: 0.6667\n", |
|
|
1144 |
"Epoch 256/1000\n", |
|
|
1145 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.1530 - accuracy: 0.9762 - val_loss: 1.7222 - val_accuracy: 0.6667\n", |
|
|
1146 |
"Epoch 257/1000\n", |
|
|
1147 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1267 - accuracy: 0.9286 - val_loss: 1.6395 - val_accuracy: 0.6667\n", |
|
|
1148 |
"Epoch 258/1000\n", |
|
|
1149 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0947 - accuracy: 1.0000 - val_loss: 1.6049 - val_accuracy: 0.5556\n", |
|
|
1150 |
"Epoch 259/1000\n", |
|
|
1151 |
"42/42 [==============================] - 0s 520us/sample - loss: 0.2062 - accuracy: 0.9286 - val_loss: 1.4510 - val_accuracy: 0.6667\n", |
|
|
1152 |
"Epoch 260/1000\n", |
|
|
1153 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1668 - accuracy: 0.9524 - val_loss: 1.5908 - val_accuracy: 0.6111\n", |
|
|
1154 |
"Epoch 261/1000\n", |
|
|
1155 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.1318 - accuracy: 0.9048 - val_loss: 1.3348 - val_accuracy: 0.7222\n", |
|
|
1156 |
"Epoch 262/1000\n", |
|
|
1157 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1603 - accuracy: 1.0000 - val_loss: 1.4055 - val_accuracy: 0.7222\n", |
|
|
1158 |
"Epoch 263/1000\n", |
|
|
1159 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1467 - accuracy: 0.9524 - val_loss: 1.5465 - val_accuracy: 0.7222\n", |
|
|
1160 |
"Epoch 264/1000\n", |
|
|
1161 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0949 - accuracy: 0.9762 - val_loss: 1.6372 - val_accuracy: 0.7222\n", |
|
|
1162 |
"Epoch 265/1000\n", |
|
|
1163 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1996 - accuracy: 0.8810 - val_loss: 1.4999 - val_accuracy: 0.6667\n", |
|
|
1164 |
"Epoch 266/1000\n", |
|
|
1165 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1067 - accuracy: 1.0000 - val_loss: 1.3938 - val_accuracy: 0.6667\n", |
|
|
1166 |
"Epoch 267/1000\n", |
|
|
1167 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.1867 - accuracy: 0.9286 - val_loss: 1.6732 - val_accuracy: 0.7222\n", |
|
|
1168 |
"Epoch 268/1000\n", |
|
|
1169 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1200 - accuracy: 0.9524 - val_loss: 1.7128 - val_accuracy: 0.6667\n", |
|
|
1170 |
"Epoch 269/1000\n", |
|
|
1171 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.1006 - accuracy: 0.9524 - val_loss: 1.4875 - val_accuracy: 0.7222\n", |
|
|
1172 |
"Epoch 270/1000\n", |
|
|
1173 |
"42/42 [==============================] - 0s 516us/sample - loss: 0.0750 - accuracy: 1.0000 - val_loss: 1.4007 - val_accuracy: 0.6667\n", |
|
|
1174 |
"Epoch 271/1000\n", |
|
|
1175 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1192 - accuracy: 0.9524 - val_loss: 1.4796 - val_accuracy: 0.6667\n", |
|
|
1176 |
"Epoch 272/1000\n", |
|
|
1177 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0914 - accuracy: 1.0000 - val_loss: 1.5058 - val_accuracy: 0.6667\n", |
|
|
1178 |
"Epoch 273/1000\n", |
|
|
1179 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1203 - accuracy: 0.9524 - val_loss: 1.3460 - val_accuracy: 0.7222\n", |
|
|
1180 |
"Epoch 274/1000\n", |
|
|
1181 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0708 - accuracy: 0.9762 - val_loss: 1.3768 - val_accuracy: 0.7222\n", |
|
|
1182 |
"Epoch 275/1000\n", |
|
|
1183 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0723 - accuracy: 1.0000 - val_loss: 1.4940 - val_accuracy: 0.7222\n", |
|
|
1184 |
"Epoch 276/1000\n", |
|
|
1185 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0918 - accuracy: 0.9762 - val_loss: 1.5523 - val_accuracy: 0.7222\n", |
|
|
1186 |
"Epoch 277/1000\n" |
|
|
1187 |
] |
|
|
1188 |
}, |
|
|
1189 |
{ |
|
|
1190 |
"name": "stdout", |
|
|
1191 |
"output_type": "stream", |
|
|
1192 |
"text": [ |
|
|
1193 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1082 - accuracy: 0.9524 - val_loss: 1.6517 - val_accuracy: 0.7222\n", |
|
|
1194 |
"Epoch 278/1000\n", |
|
|
1195 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1558 - accuracy: 0.9286 - val_loss: 1.5854 - val_accuracy: 0.6667\n", |
|
|
1196 |
"Epoch 279/1000\n", |
|
|
1197 |
"42/42 [==============================] - 0s 490us/sample - loss: 0.1054 - accuracy: 0.9524 - val_loss: 1.5319 - val_accuracy: 0.7222\n", |
|
|
1198 |
"Epoch 280/1000\n", |
|
|
1199 |
"42/42 [==============================] - 0s 487us/sample - loss: 0.1254 - accuracy: 0.9762 - val_loss: 1.4912 - val_accuracy: 0.7222\n", |
|
|
1200 |
"Epoch 281/1000\n", |
|
|
1201 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0600 - accuracy: 1.0000 - val_loss: 1.5421 - val_accuracy: 0.7222\n", |
|
|
1202 |
"Epoch 282/1000\n", |
|
|
1203 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0776 - accuracy: 1.0000 - val_loss: 1.5047 - val_accuracy: 0.7222\n", |
|
|
1204 |
"Epoch 283/1000\n", |
|
|
1205 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0656 - accuracy: 1.0000 - val_loss: 1.5413 - val_accuracy: 0.7222\n", |
|
|
1206 |
"Epoch 284/1000\n", |
|
|
1207 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0999 - accuracy: 0.9762 - val_loss: 1.6474 - val_accuracy: 0.6667\n", |
|
|
1208 |
"Epoch 285/1000\n", |
|
|
1209 |
"42/42 [==============================] - 0s 501us/sample - loss: 0.1050 - accuracy: 0.9762 - val_loss: 1.5402 - val_accuracy: 0.7222\n", |
|
|
1210 |
"Epoch 286/1000\n", |
|
|
1211 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0666 - accuracy: 1.0000 - val_loss: 1.3149 - val_accuracy: 0.7222\n", |
|
|
1212 |
"Epoch 287/1000\n", |
|
|
1213 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0692 - accuracy: 1.0000 - val_loss: 1.2903 - val_accuracy: 0.7222\n", |
|
|
1214 |
"Epoch 288/1000\n", |
|
|
1215 |
"42/42 [==============================] - 0s 525us/sample - loss: 0.0701 - accuracy: 1.0000 - val_loss: 1.3754 - val_accuracy: 0.7222\n", |
|
|
1216 |
"Epoch 289/1000\n", |
|
|
1217 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0735 - accuracy: 0.9762 - val_loss: 1.5043 - val_accuracy: 0.7222\n", |
|
|
1218 |
"Epoch 290/1000\n", |
|
|
1219 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0944 - accuracy: 0.9762 - val_loss: 1.5417 - val_accuracy: 0.6667\n", |
|
|
1220 |
"Epoch 291/1000\n", |
|
|
1221 |
"42/42 [==============================] - 0s 513us/sample - loss: 0.0809 - accuracy: 0.9762 - val_loss: 1.6663 - val_accuracy: 0.6667\n", |
|
|
1222 |
"Epoch 292/1000\n", |
|
|
1223 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0718 - accuracy: 0.9762 - val_loss: 1.6269 - val_accuracy: 0.7222\n", |
|
|
1224 |
"Epoch 293/1000\n", |
|
|
1225 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0472 - accuracy: 1.0000 - val_loss: 1.5935 - val_accuracy: 0.7222\n", |
|
|
1226 |
"Epoch 294/1000\n", |
|
|
1227 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0652 - accuracy: 1.0000 - val_loss: 1.4567 - val_accuracy: 0.7222\n", |
|
|
1228 |
"Epoch 295/1000\n", |
|
|
1229 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0990 - accuracy: 0.9762 - val_loss: 1.2497 - val_accuracy: 0.7222\n", |
|
|
1230 |
"Epoch 296/1000\n", |
|
|
1231 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0720 - accuracy: 1.0000 - val_loss: 1.3439 - val_accuracy: 0.6667\n", |
|
|
1232 |
"Epoch 297/1000\n", |
|
|
1233 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.0887 - accuracy: 0.9762 - val_loss: 1.7301 - val_accuracy: 0.6667\n", |
|
|
1234 |
"Epoch 298/1000\n", |
|
|
1235 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0942 - accuracy: 0.9762 - val_loss: 1.9684 - val_accuracy: 0.6667\n", |
|
|
1236 |
"Epoch 299/1000\n", |
|
|
1237 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0797 - accuracy: 0.9762 - val_loss: 1.8131 - val_accuracy: 0.7222\n", |
|
|
1238 |
"Epoch 300/1000\n", |
|
|
1239 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.0495 - accuracy: 1.0000 - val_loss: 1.5969 - val_accuracy: 0.7222\n", |
|
|
1240 |
"Epoch 301/1000\n", |
|
|
1241 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1521 - accuracy: 0.9524 - val_loss: 1.5805 - val_accuracy: 0.7778\n", |
|
|
1242 |
"Epoch 302/1000\n", |
|
|
1243 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1157 - accuracy: 0.9524 - val_loss: 1.6520 - val_accuracy: 0.7222\n", |
|
|
1244 |
"Epoch 303/1000\n", |
|
|
1245 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.1329 - accuracy: 0.9524 - val_loss: 1.8121 - val_accuracy: 0.7222\n", |
|
|
1246 |
"Epoch 304/1000\n", |
|
|
1247 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0779 - accuracy: 0.9524 - val_loss: 1.5364 - val_accuracy: 0.6111\n", |
|
|
1248 |
"Epoch 305/1000\n", |
|
|
1249 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1103 - accuracy: 0.9762 - val_loss: 1.4288 - val_accuracy: 0.6111\n", |
|
|
1250 |
"Epoch 306/1000\n", |
|
|
1251 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1065 - accuracy: 0.9524 - val_loss: 1.4064 - val_accuracy: 0.6667\n", |
|
|
1252 |
"Epoch 307/1000\n", |
|
|
1253 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0552 - accuracy: 1.0000 - val_loss: 1.6579 - val_accuracy: 0.7222\n", |
|
|
1254 |
"Epoch 308/1000\n", |
|
|
1255 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1076 - accuracy: 0.9762 - val_loss: 1.9321 - val_accuracy: 0.6667\n", |
|
|
1256 |
"Epoch 309/1000\n", |
|
|
1257 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0710 - accuracy: 1.0000 - val_loss: 1.8186 - val_accuracy: 0.6667\n", |
|
|
1258 |
"Epoch 310/1000\n", |
|
|
1259 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0832 - accuracy: 0.9762 - val_loss: 1.6370 - val_accuracy: 0.6667\n", |
|
|
1260 |
"Epoch 311/1000\n", |
|
|
1261 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0478 - accuracy: 1.0000 - val_loss: 1.5288 - val_accuracy: 0.6667\n", |
|
|
1262 |
"Epoch 312/1000\n", |
|
|
1263 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.1230 - accuracy: 0.9524 - val_loss: 1.6373 - val_accuracy: 0.7222\n", |
|
|
1264 |
"Epoch 313/1000\n", |
|
|
1265 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0750 - accuracy: 1.0000 - val_loss: 1.9761 - val_accuracy: 0.6667\n", |
|
|
1266 |
"Epoch 314/1000\n", |
|
|
1267 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0660 - accuracy: 0.9762 - val_loss: 1.9529 - val_accuracy: 0.6667\n", |
|
|
1268 |
"Epoch 315/1000\n", |
|
|
1269 |
"42/42 [==============================] - 0s 493us/sample - loss: 0.1080 - accuracy: 0.9286 - val_loss: 1.4661 - val_accuracy: 0.7222\n", |
|
|
1270 |
"Epoch 316/1000\n", |
|
|
1271 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1096 - accuracy: 0.9524 - val_loss: 1.4815 - val_accuracy: 0.6111\n", |
|
|
1272 |
"Epoch 317/1000\n", |
|
|
1273 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0523 - accuracy: 1.0000 - val_loss: 1.6877 - val_accuracy: 0.7222\n", |
|
|
1274 |
"Epoch 318/1000\n", |
|
|
1275 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0461 - accuracy: 1.0000 - val_loss: 1.9495 - val_accuracy: 0.6111\n", |
|
|
1276 |
"Epoch 319/1000\n", |
|
|
1277 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.1071 - accuracy: 0.9762 - val_loss: 1.7865 - val_accuracy: 0.7222\n", |
|
|
1278 |
"Epoch 320/1000\n", |
|
|
1279 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0501 - accuracy: 0.9762 - val_loss: 1.4970 - val_accuracy: 0.7222\n", |
|
|
1280 |
"Epoch 321/1000\n", |
|
|
1281 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.1015 - accuracy: 0.9524 - val_loss: 1.5159 - val_accuracy: 0.5556\n", |
|
|
1282 |
"Epoch 322/1000\n", |
|
|
1283 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0907 - accuracy: 0.9762 - val_loss: 1.6434 - val_accuracy: 0.6667\n", |
|
|
1284 |
"Epoch 323/1000\n", |
|
|
1285 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0618 - accuracy: 1.0000 - val_loss: 1.9597 - val_accuracy: 0.6667\n", |
|
|
1286 |
"Epoch 324/1000\n", |
|
|
1287 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0873 - accuracy: 0.9762 - val_loss: 1.8291 - val_accuracy: 0.6667\n", |
|
|
1288 |
"Epoch 325/1000\n", |
|
|
1289 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0843 - accuracy: 0.9762 - val_loss: 1.4405 - val_accuracy: 0.7222\n", |
|
|
1290 |
"Epoch 326/1000\n", |
|
|
1291 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0837 - accuracy: 0.9762 - val_loss: 1.3664 - val_accuracy: 0.7222\n", |
|
|
1292 |
"Epoch 327/1000\n", |
|
|
1293 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0663 - accuracy: 1.0000 - val_loss: 1.4514 - val_accuracy: 0.7222\n", |
|
|
1294 |
"Epoch 328/1000\n", |
|
|
1295 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0451 - accuracy: 1.0000 - val_loss: 1.5999 - val_accuracy: 0.7222\n", |
|
|
1296 |
"Epoch 329/1000\n", |
|
|
1297 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0339 - accuracy: 1.0000 - val_loss: 1.6782 - val_accuracy: 0.7222\n", |
|
|
1298 |
"Epoch 330/1000\n", |
|
|
1299 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0690 - accuracy: 0.9762 - val_loss: 1.6621 - val_accuracy: 0.7222\n", |
|
|
1300 |
"Epoch 331/1000\n", |
|
|
1301 |
"42/42 [==============================] - 0s 528us/sample - loss: 0.0567 - accuracy: 1.0000 - val_loss: 1.4858 - val_accuracy: 0.7222\n", |
|
|
1302 |
"Epoch 332/1000\n" |
|
|
1303 |
] |
|
|
1304 |
}, |
|
|
1305 |
{ |
|
|
1306 |
"name": "stdout", |
|
|
1307 |
"output_type": "stream", |
|
|
1308 |
"text": [ |
|
|
1309 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0783 - accuracy: 0.9762 - val_loss: 1.4978 - val_accuracy: 0.7222\n", |
|
|
1310 |
"Epoch 333/1000\n", |
|
|
1311 |
"42/42 [==============================] - 0s 516us/sample - loss: 0.0659 - accuracy: 0.9762 - val_loss: 1.4622 - val_accuracy: 0.7222\n", |
|
|
1312 |
"Epoch 334/1000\n", |
|
|
1313 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0771 - accuracy: 0.9762 - val_loss: 1.4272 - val_accuracy: 0.7222\n", |
|
|
1314 |
"Epoch 335/1000\n", |
|
|
1315 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0563 - accuracy: 1.0000 - val_loss: 1.3124 - val_accuracy: 0.7222\n", |
|
|
1316 |
"Epoch 336/1000\n", |
|
|
1317 |
"42/42 [==============================] - 0s 477us/sample - loss: 0.0508 - accuracy: 1.0000 - val_loss: 1.3203 - val_accuracy: 0.6667\n", |
|
|
1318 |
"Epoch 337/1000\n", |
|
|
1319 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0658 - accuracy: 0.9762 - val_loss: 1.5193 - val_accuracy: 0.6667\n", |
|
|
1320 |
"Epoch 338/1000\n", |
|
|
1321 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0948 - accuracy: 0.9524 - val_loss: 1.6475 - val_accuracy: 0.7222\n", |
|
|
1322 |
"Epoch 339/1000\n", |
|
|
1323 |
"42/42 [==============================] - 0s 478us/sample - loss: 0.0680 - accuracy: 0.9762 - val_loss: 1.7764 - val_accuracy: 0.7222\n", |
|
|
1324 |
"Epoch 340/1000\n", |
|
|
1325 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0474 - accuracy: 1.0000 - val_loss: 1.6899 - val_accuracy: 0.7222\n", |
|
|
1326 |
"Epoch 341/1000\n", |
|
|
1327 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0461 - accuracy: 1.0000 - val_loss: 1.5043 - val_accuracy: 0.7222\n", |
|
|
1328 |
"Epoch 342/1000\n", |
|
|
1329 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0707 - accuracy: 0.9762 - val_loss: 1.4001 - val_accuracy: 0.7222\n", |
|
|
1330 |
"Epoch 343/1000\n", |
|
|
1331 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0663 - accuracy: 1.0000 - val_loss: 1.3488 - val_accuracy: 0.7222\n", |
|
|
1332 |
"Epoch 344/1000\n", |
|
|
1333 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0749 - accuracy: 0.9762 - val_loss: 1.4232 - val_accuracy: 0.7222\n", |
|
|
1334 |
"Epoch 345/1000\n", |
|
|
1335 |
"42/42 [==============================] - 0s 504us/sample - loss: 0.0556 - accuracy: 1.0000 - val_loss: 1.5679 - val_accuracy: 0.7222\n", |
|
|
1336 |
"Epoch 346/1000\n", |
|
|
1337 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0409 - accuracy: 1.0000 - val_loss: 1.8648 - val_accuracy: 0.6667\n", |
|
|
1338 |
"Epoch 347/1000\n", |
|
|
1339 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0646 - accuracy: 0.9762 - val_loss: 1.8979 - val_accuracy: 0.6667\n", |
|
|
1340 |
"Epoch 348/1000\n", |
|
|
1341 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0722 - accuracy: 0.9762 - val_loss: 1.6598 - val_accuracy: 0.7222\n", |
|
|
1342 |
"Epoch 349/1000\n", |
|
|
1343 |
"42/42 [==============================] - 0s 516us/sample - loss: 0.0734 - accuracy: 0.9762 - val_loss: 1.5452 - val_accuracy: 0.7222\n", |
|
|
1344 |
"Epoch 350/1000\n", |
|
|
1345 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0392 - accuracy: 1.0000 - val_loss: 1.7702 - val_accuracy: 0.6111\n", |
|
|
1346 |
"Epoch 351/1000\n", |
|
|
1347 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.1130 - accuracy: 0.9762 - val_loss: 1.8857 - val_accuracy: 0.6111\n", |
|
|
1348 |
"Epoch 352/1000\n", |
|
|
1349 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0295 - accuracy: 1.0000 - val_loss: 1.8419 - val_accuracy: 0.7222\n", |
|
|
1350 |
"Epoch 353/1000\n", |
|
|
1351 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0629 - accuracy: 0.9762 - val_loss: 1.8847 - val_accuracy: 0.7222\n", |
|
|
1352 |
"Epoch 354/1000\n", |
|
|
1353 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0748 - accuracy: 0.9762 - val_loss: 1.8579 - val_accuracy: 0.7222\n", |
|
|
1354 |
"Epoch 355/1000\n", |
|
|
1355 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0325 - accuracy: 1.0000 - val_loss: 1.7283 - val_accuracy: 0.7222\n", |
|
|
1356 |
"Epoch 356/1000\n", |
|
|
1357 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0446 - accuracy: 1.0000 - val_loss: 1.5731 - val_accuracy: 0.7222\n", |
|
|
1358 |
"Epoch 357/1000\n", |
|
|
1359 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0693 - accuracy: 0.9762 - val_loss: 1.4469 - val_accuracy: 0.7222\n", |
|
|
1360 |
"Epoch 358/1000\n", |
|
|
1361 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0623 - accuracy: 0.9762 - val_loss: 1.4579 - val_accuracy: 0.7222\n", |
|
|
1362 |
"Epoch 359/1000\n", |
|
|
1363 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0522 - accuracy: 1.0000 - val_loss: 1.4974 - val_accuracy: 0.7222\n", |
|
|
1364 |
"Epoch 360/1000\n", |
|
|
1365 |
"42/42 [==============================] - 0s 502us/sample - loss: 0.0612 - accuracy: 1.0000 - val_loss: 1.5117 - val_accuracy: 0.7222\n", |
|
|
1366 |
"Epoch 361/1000\n", |
|
|
1367 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0268 - accuracy: 1.0000 - val_loss: 1.5376 - val_accuracy: 0.7222\n", |
|
|
1368 |
"Epoch 362/1000\n", |
|
|
1369 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0347 - accuracy: 1.0000 - val_loss: 1.5808 - val_accuracy: 0.7222\n", |
|
|
1370 |
"Epoch 363/1000\n", |
|
|
1371 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0518 - accuracy: 1.0000 - val_loss: 1.5186 - val_accuracy: 0.7222\n", |
|
|
1372 |
"Epoch 364/1000\n", |
|
|
1373 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0445 - accuracy: 0.9762 - val_loss: 1.5488 - val_accuracy: 0.7222\n", |
|
|
1374 |
"Epoch 365/1000\n", |
|
|
1375 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0295 - accuracy: 1.0000 - val_loss: 1.5401 - val_accuracy: 0.7222\n", |
|
|
1376 |
"Epoch 366/1000\n", |
|
|
1377 |
"42/42 [==============================] - 0s 513us/sample - loss: 0.0636 - accuracy: 0.9762 - val_loss: 1.5107 - val_accuracy: 0.7222\n", |
|
|
1378 |
"Epoch 367/1000\n", |
|
|
1379 |
"42/42 [==============================] - 0s 688us/sample - loss: 0.0248 - accuracy: 1.0000 - val_loss: 1.5374 - val_accuracy: 0.7222\n", |
|
|
1380 |
"Epoch 368/1000\n", |
|
|
1381 |
"42/42 [==============================] - 0s 617us/sample - loss: 0.0338 - accuracy: 1.0000 - val_loss: 1.5436 - val_accuracy: 0.7222\n", |
|
|
1382 |
"Epoch 369/1000\n", |
|
|
1383 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0371 - accuracy: 1.0000 - val_loss: 1.4079 - val_accuracy: 0.7222\n", |
|
|
1384 |
"Epoch 370/1000\n", |
|
|
1385 |
"42/42 [==============================] - 0s 482us/sample - loss: 0.0414 - accuracy: 0.9762 - val_loss: 1.4443 - val_accuracy: 0.7222\n", |
|
|
1386 |
"Epoch 371/1000\n", |
|
|
1387 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0506 - accuracy: 1.0000 - val_loss: 1.4049 - val_accuracy: 0.7222\n", |
|
|
1388 |
"Epoch 372/1000\n", |
|
|
1389 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0153 - accuracy: 1.0000 - val_loss: 1.3405 - val_accuracy: 0.7222\n", |
|
|
1390 |
"Epoch 373/1000\n", |
|
|
1391 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0334 - accuracy: 1.0000 - val_loss: 1.3608 - val_accuracy: 0.7222\n", |
|
|
1392 |
"Epoch 374/1000\n", |
|
|
1393 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0325 - accuracy: 1.0000 - val_loss: 1.4290 - val_accuracy: 0.7222\n", |
|
|
1394 |
"Epoch 375/1000\n", |
|
|
1395 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0665 - accuracy: 0.9762 - val_loss: 1.3479 - val_accuracy: 0.7222\n", |
|
|
1396 |
"Epoch 376/1000\n", |
|
|
1397 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0298 - accuracy: 1.0000 - val_loss: 1.1752 - val_accuracy: 0.7222\n", |
|
|
1398 |
"Epoch 377/1000\n", |
|
|
1399 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0666 - accuracy: 0.9762 - val_loss: 1.2153 - val_accuracy: 0.7222\n", |
|
|
1400 |
"Epoch 378/1000\n", |
|
|
1401 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0345 - accuracy: 1.0000 - val_loss: 1.4213 - val_accuracy: 0.7222\n", |
|
|
1402 |
"Epoch 379/1000\n", |
|
|
1403 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.0463 - accuracy: 1.0000 - val_loss: 1.6233 - val_accuracy: 0.7222\n", |
|
|
1404 |
"Epoch 380/1000\n", |
|
|
1405 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0279 - accuracy: 1.0000 - val_loss: 1.6568 - val_accuracy: 0.7222\n", |
|
|
1406 |
"Epoch 381/1000\n", |
|
|
1407 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0431 - accuracy: 1.0000 - val_loss: 1.5743 - val_accuracy: 0.7222\n", |
|
|
1408 |
"Epoch 382/1000\n", |
|
|
1409 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0257 - accuracy: 1.0000 - val_loss: 1.4257 - val_accuracy: 0.7222\n", |
|
|
1410 |
"Epoch 383/1000\n", |
|
|
1411 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0297 - accuracy: 1.0000 - val_loss: 1.3915 - val_accuracy: 0.7222\n", |
|
|
1412 |
"Epoch 384/1000\n", |
|
|
1413 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0316 - accuracy: 1.0000 - val_loss: 1.4750 - val_accuracy: 0.7222\n", |
|
|
1414 |
"Epoch 385/1000\n", |
|
|
1415 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0388 - accuracy: 1.0000 - val_loss: 1.4356 - val_accuracy: 0.7222\n", |
|
|
1416 |
"Epoch 386/1000\n", |
|
|
1417 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0368 - accuracy: 1.0000 - val_loss: 1.4135 - val_accuracy: 0.7222\n", |
|
|
1418 |
"Epoch 387/1000\n" |
|
|
1419 |
] |
|
|
1420 |
}, |
|
|
1421 |
{ |
|
|
1422 |
"name": "stdout", |
|
|
1423 |
"output_type": "stream", |
|
|
1424 |
"text": [ |
|
|
1425 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0430 - accuracy: 1.0000 - val_loss: 1.5791 - val_accuracy: 0.7222\n", |
|
|
1426 |
"Epoch 388/1000\n", |
|
|
1427 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.0408 - accuracy: 0.9762 - val_loss: 1.6045 - val_accuracy: 0.7222\n", |
|
|
1428 |
"Epoch 389/1000\n", |
|
|
1429 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0507 - accuracy: 0.9762 - val_loss: 1.4142 - val_accuracy: 0.7222\n", |
|
|
1430 |
"Epoch 390/1000\n", |
|
|
1431 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0235 - accuracy: 1.0000 - val_loss: 1.3127 - val_accuracy: 0.6667\n", |
|
|
1432 |
"Epoch 391/1000\n", |
|
|
1433 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0620 - accuracy: 0.9762 - val_loss: 1.5154 - val_accuracy: 0.7222\n", |
|
|
1434 |
"Epoch 392/1000\n", |
|
|
1435 |
"42/42 [==============================] - 0s 521us/sample - loss: 0.0378 - accuracy: 1.0000 - val_loss: 1.7587 - val_accuracy: 0.7222\n", |
|
|
1436 |
"Epoch 393/1000\n", |
|
|
1437 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0374 - accuracy: 1.0000 - val_loss: 1.7443 - val_accuracy: 0.7222\n", |
|
|
1438 |
"Epoch 394/1000\n", |
|
|
1439 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0540 - accuracy: 0.9524 - val_loss: 1.6416 - val_accuracy: 0.7222\n", |
|
|
1440 |
"Epoch 395/1000\n", |
|
|
1441 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0199 - accuracy: 1.0000 - val_loss: 1.5438 - val_accuracy: 0.7222\n", |
|
|
1442 |
"Epoch 396/1000\n", |
|
|
1443 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0291 - accuracy: 1.0000 - val_loss: 1.5330 - val_accuracy: 0.7222\n", |
|
|
1444 |
"Epoch 397/1000\n", |
|
|
1445 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0309 - accuracy: 1.0000 - val_loss: 1.5111 - val_accuracy: 0.7222\n", |
|
|
1446 |
"Epoch 398/1000\n", |
|
|
1447 |
"42/42 [==============================] - 0s 482us/sample - loss: 0.0292 - accuracy: 1.0000 - val_loss: 1.5092 - val_accuracy: 0.7222\n", |
|
|
1448 |
"Epoch 399/1000\n", |
|
|
1449 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0379 - accuracy: 0.9762 - val_loss: 1.3535 - val_accuracy: 0.7222\n", |
|
|
1450 |
"Epoch 400/1000\n", |
|
|
1451 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0506 - accuracy: 1.0000 - val_loss: 1.2670 - val_accuracy: 0.6111\n", |
|
|
1452 |
"Epoch 401/1000\n", |
|
|
1453 |
"42/42 [==============================] - 0s 488us/sample - loss: 0.0551 - accuracy: 1.0000 - val_loss: 1.5281 - val_accuracy: 0.7222\n", |
|
|
1454 |
"Epoch 402/1000\n", |
|
|
1455 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0162 - accuracy: 1.0000 - val_loss: 1.8960 - val_accuracy: 0.6667\n", |
|
|
1456 |
"Epoch 403/1000\n", |
|
|
1457 |
"42/42 [==============================] - 0s 497us/sample - loss: 0.0301 - accuracy: 1.0000 - val_loss: 1.9940 - val_accuracy: 0.6667\n", |
|
|
1458 |
"Epoch 404/1000\n", |
|
|
1459 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0289 - accuracy: 1.0000 - val_loss: 1.8318 - val_accuracy: 0.6667\n", |
|
|
1460 |
"Epoch 405/1000\n", |
|
|
1461 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0418 - accuracy: 1.0000 - val_loss: 1.6173 - val_accuracy: 0.7222\n", |
|
|
1462 |
"Epoch 406/1000\n", |
|
|
1463 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0415 - accuracy: 1.0000 - val_loss: 1.5044 - val_accuracy: 0.6667\n", |
|
|
1464 |
"Epoch 407/1000\n", |
|
|
1465 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0195 - accuracy: 1.0000 - val_loss: 1.4275 - val_accuracy: 0.6667\n", |
|
|
1466 |
"Epoch 408/1000\n", |
|
|
1467 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0239 - accuracy: 1.0000 - val_loss: 1.4591 - val_accuracy: 0.6667\n", |
|
|
1468 |
"Epoch 409/1000\n", |
|
|
1469 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0252 - accuracy: 1.0000 - val_loss: 1.5394 - val_accuracy: 0.6667\n", |
|
|
1470 |
"Epoch 410/1000\n", |
|
|
1471 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0283 - accuracy: 1.0000 - val_loss: 1.5715 - val_accuracy: 0.6667\n", |
|
|
1472 |
"Epoch 411/1000\n", |
|
|
1473 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0251 - accuracy: 1.0000 - val_loss: 1.4999 - val_accuracy: 0.6667\n", |
|
|
1474 |
"Epoch 412/1000\n", |
|
|
1475 |
"42/42 [==============================] - 0s 515us/sample - loss: 0.0272 - accuracy: 1.0000 - val_loss: 1.4527 - val_accuracy: 0.6667\n", |
|
|
1476 |
"Epoch 413/1000\n", |
|
|
1477 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0231 - accuracy: 1.0000 - val_loss: 1.4394 - val_accuracy: 0.6667\n", |
|
|
1478 |
"Epoch 414/1000\n", |
|
|
1479 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0278 - accuracy: 1.0000 - val_loss: 1.6125 - val_accuracy: 0.7222\n", |
|
|
1480 |
"Epoch 415/1000\n", |
|
|
1481 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0293 - accuracy: 1.0000 - val_loss: 1.7535 - val_accuracy: 0.7222\n", |
|
|
1482 |
"Epoch 416/1000\n", |
|
|
1483 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0215 - accuracy: 1.0000 - val_loss: 1.7031 - val_accuracy: 0.7222\n", |
|
|
1484 |
"Epoch 417/1000\n", |
|
|
1485 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0425 - accuracy: 0.9762 - val_loss: 1.7287 - val_accuracy: 0.6667\n", |
|
|
1486 |
"Epoch 418/1000\n", |
|
|
1487 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0195 - accuracy: 1.0000 - val_loss: 1.7400 - val_accuracy: 0.6667\n", |
|
|
1488 |
"Epoch 419/1000\n", |
|
|
1489 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0266 - accuracy: 1.0000 - val_loss: 1.8098 - val_accuracy: 0.6667\n", |
|
|
1490 |
"Epoch 420/1000\n", |
|
|
1491 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0236 - accuracy: 1.0000 - val_loss: 1.9062 - val_accuracy: 0.6111\n", |
|
|
1492 |
"Epoch 421/1000\n", |
|
|
1493 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0360 - accuracy: 1.0000 - val_loss: 1.8378 - val_accuracy: 0.6111\n", |
|
|
1494 |
"Epoch 422/1000\n", |
|
|
1495 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0191 - accuracy: 1.0000 - val_loss: 1.6392 - val_accuracy: 0.7222\n", |
|
|
1496 |
"Epoch 423/1000\n", |
|
|
1497 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0304 - accuracy: 1.0000 - val_loss: 1.5162 - val_accuracy: 0.7222\n", |
|
|
1498 |
"Epoch 424/1000\n", |
|
|
1499 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0100 - accuracy: 1.0000 - val_loss: 1.5426 - val_accuracy: 0.6667\n", |
|
|
1500 |
"Epoch 425/1000\n", |
|
|
1501 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0313 - accuracy: 1.0000 - val_loss: 1.6118 - val_accuracy: 0.6667\n", |
|
|
1502 |
"Epoch 426/1000\n", |
|
|
1503 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0591 - accuracy: 0.9762 - val_loss: 1.9203 - val_accuracy: 0.7222\n", |
|
|
1504 |
"Epoch 427/1000\n", |
|
|
1505 |
"42/42 [==============================] - 0s 497us/sample - loss: 0.0525 - accuracy: 1.0000 - val_loss: 2.0855 - val_accuracy: 0.7222\n", |
|
|
1506 |
"Epoch 428/1000\n", |
|
|
1507 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0398 - accuracy: 0.9762 - val_loss: 1.8087 - val_accuracy: 0.7222\n", |
|
|
1508 |
"Epoch 429/1000\n", |
|
|
1509 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0387 - accuracy: 1.0000 - val_loss: 1.5941 - val_accuracy: 0.6667\n", |
|
|
1510 |
"Epoch 430/1000\n", |
|
|
1511 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0480 - accuracy: 1.0000 - val_loss: 1.4452 - val_accuracy: 0.6667\n", |
|
|
1512 |
"Epoch 431/1000\n", |
|
|
1513 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0317 - accuracy: 1.0000 - val_loss: 1.3611 - val_accuracy: 0.6667\n", |
|
|
1514 |
"Epoch 432/1000\n", |
|
|
1515 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0284 - accuracy: 1.0000 - val_loss: 1.3693 - val_accuracy: 0.6667\n", |
|
|
1516 |
"Epoch 433/1000\n", |
|
|
1517 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0292 - accuracy: 1.0000 - val_loss: 1.4462 - val_accuracy: 0.6667\n", |
|
|
1518 |
"Epoch 434/1000\n", |
|
|
1519 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0199 - accuracy: 1.0000 - val_loss: 1.5306 - val_accuracy: 0.6667\n", |
|
|
1520 |
"Epoch 435/1000\n", |
|
|
1521 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0297 - accuracy: 1.0000 - val_loss: 1.6574 - val_accuracy: 0.6667\n", |
|
|
1522 |
"Epoch 436/1000\n", |
|
|
1523 |
"42/42 [==============================] - 0s 497us/sample - loss: 0.0239 - accuracy: 1.0000 - val_loss: 1.5770 - val_accuracy: 0.6667\n", |
|
|
1524 |
"Epoch 437/1000\n", |
|
|
1525 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0220 - accuracy: 1.0000 - val_loss: 1.5255 - val_accuracy: 0.6667\n", |
|
|
1526 |
"Epoch 438/1000\n", |
|
|
1527 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0118 - accuracy: 1.0000 - val_loss: 1.5344 - val_accuracy: 0.6667\n", |
|
|
1528 |
"Epoch 439/1000\n", |
|
|
1529 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0175 - accuracy: 1.0000 - val_loss: 1.4632 - val_accuracy: 0.7222\n", |
|
|
1530 |
"Epoch 440/1000\n", |
|
|
1531 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0202 - accuracy: 1.0000 - val_loss: 1.4301 - val_accuracy: 0.7222\n", |
|
|
1532 |
"Epoch 441/1000\n", |
|
|
1533 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0240 - accuracy: 1.0000 - val_loss: 1.4701 - val_accuracy: 0.6667\n", |
|
|
1534 |
"Epoch 442/1000\n" |
|
|
1535 |
] |
|
|
1536 |
}, |
|
|
1537 |
{ |
|
|
1538 |
"name": "stdout", |
|
|
1539 |
"output_type": "stream", |
|
|
1540 |
"text": [ |
|
|
1541 |
"42/42 [==============================] - 0s 488us/sample - loss: 0.0178 - accuracy: 1.0000 - val_loss: 1.5701 - val_accuracy: 0.6667\n", |
|
|
1542 |
"Epoch 443/1000\n", |
|
|
1543 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0282 - accuracy: 1.0000 - val_loss: 1.5822 - val_accuracy: 0.6667\n", |
|
|
1544 |
"Epoch 444/1000\n", |
|
|
1545 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0256 - accuracy: 1.0000 - val_loss: 1.5911 - val_accuracy: 0.7222\n", |
|
|
1546 |
"Epoch 445/1000\n", |
|
|
1547 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0189 - accuracy: 1.0000 - val_loss: 1.6035 - val_accuracy: 0.7222\n", |
|
|
1548 |
"Epoch 446/1000\n", |
|
|
1549 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0285 - accuracy: 1.0000 - val_loss: 1.6609 - val_accuracy: 0.7222\n", |
|
|
1550 |
"Epoch 447/1000\n", |
|
|
1551 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0344 - accuracy: 1.0000 - val_loss: 1.6280 - val_accuracy: 0.7222\n", |
|
|
1552 |
"Epoch 448/1000\n", |
|
|
1553 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0186 - accuracy: 1.0000 - val_loss: 1.6016 - val_accuracy: 0.7222\n", |
|
|
1554 |
"Epoch 449/1000\n", |
|
|
1555 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0142 - accuracy: 1.0000 - val_loss: 1.6062 - val_accuracy: 0.7222\n", |
|
|
1556 |
"Epoch 450/1000\n", |
|
|
1557 |
"42/42 [==============================] - 0s 511us/sample - loss: 0.0207 - accuracy: 1.0000 - val_loss: 1.7060 - val_accuracy: 0.7222\n", |
|
|
1558 |
"Epoch 451/1000\n", |
|
|
1559 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0262 - accuracy: 1.0000 - val_loss: 1.6331 - val_accuracy: 0.7222\n", |
|
|
1560 |
"Epoch 452/1000\n", |
|
|
1561 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0271 - accuracy: 1.0000 - val_loss: 1.4941 - val_accuracy: 0.6667\n", |
|
|
1562 |
"Epoch 453/1000\n", |
|
|
1563 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 1.4697 - val_accuracy: 0.6667\n", |
|
|
1564 |
"Epoch 454/1000\n", |
|
|
1565 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0151 - accuracy: 1.0000 - val_loss: 1.4762 - val_accuracy: 0.6667\n", |
|
|
1566 |
"Epoch 455/1000\n", |
|
|
1567 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0578 - accuracy: 0.9762 - val_loss: 1.6451 - val_accuracy: 0.7222\n", |
|
|
1568 |
"Epoch 456/1000\n", |
|
|
1569 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0197 - accuracy: 1.0000 - val_loss: 1.9646 - val_accuracy: 0.6667\n", |
|
|
1570 |
"Epoch 457/1000\n", |
|
|
1571 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0315 - accuracy: 1.0000 - val_loss: 1.9981 - val_accuracy: 0.6667\n", |
|
|
1572 |
"Epoch 458/1000\n", |
|
|
1573 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0546 - accuracy: 0.9762 - val_loss: 1.8021 - val_accuracy: 0.7222\n", |
|
|
1574 |
"Epoch 459/1000\n", |
|
|
1575 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0254 - accuracy: 1.0000 - val_loss: 1.7340 - val_accuracy: 0.6667\n", |
|
|
1576 |
"Epoch 460/1000\n", |
|
|
1577 |
"42/42 [==============================] - 0s 497us/sample - loss: 0.0355 - accuracy: 1.0000 - val_loss: 1.6165 - val_accuracy: 0.7222\n", |
|
|
1578 |
"Epoch 461/1000\n", |
|
|
1579 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0114 - accuracy: 1.0000 - val_loss: 1.6238 - val_accuracy: 0.7222\n", |
|
|
1580 |
"Epoch 462/1000\n", |
|
|
1581 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0170 - accuracy: 1.0000 - val_loss: 1.6467 - val_accuracy: 0.7222\n", |
|
|
1582 |
"Epoch 463/1000\n", |
|
|
1583 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0454 - accuracy: 1.0000 - val_loss: 1.5361 - val_accuracy: 0.7222\n", |
|
|
1584 |
"Epoch 464/1000\n", |
|
|
1585 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0292 - accuracy: 1.0000 - val_loss: 1.3453 - val_accuracy: 0.7222\n", |
|
|
1586 |
"Epoch 465/1000\n", |
|
|
1587 |
"42/42 [==============================] - 0s 524us/sample - loss: 0.0267 - accuracy: 1.0000 - val_loss: 1.3856 - val_accuracy: 0.7778\n", |
|
|
1588 |
"Epoch 466/1000\n", |
|
|
1589 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0141 - accuracy: 1.0000 - val_loss: 1.5429 - val_accuracy: 0.7222\n", |
|
|
1590 |
"Epoch 467/1000\n", |
|
|
1591 |
"42/42 [==============================] - ETA: 0s - loss: 0.0114 - accuracy: 1.00 - 0s 499us/sample - loss: 0.0113 - accuracy: 1.0000 - val_loss: 1.7197 - val_accuracy: 0.7222\n", |
|
|
1592 |
"Epoch 468/1000\n", |
|
|
1593 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0227 - accuracy: 1.0000 - val_loss: 1.8281 - val_accuracy: 0.7222\n", |
|
|
1594 |
"Epoch 469/1000\n", |
|
|
1595 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0769 - accuracy: 0.9286 - val_loss: 1.7743 - val_accuracy: 0.7222\n", |
|
|
1596 |
"Epoch 470/1000\n", |
|
|
1597 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0241 - accuracy: 1.0000 - val_loss: 1.8644 - val_accuracy: 0.7222\n", |
|
|
1598 |
"Epoch 471/1000\n", |
|
|
1599 |
"42/42 [==============================] - 0s 506us/sample - loss: 0.0212 - accuracy: 1.0000 - val_loss: 1.9545 - val_accuracy: 0.6667\n", |
|
|
1600 |
"Epoch 472/1000\n", |
|
|
1601 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0341 - accuracy: 1.0000 - val_loss: 1.9052 - val_accuracy: 0.6667\n", |
|
|
1602 |
"Epoch 473/1000\n", |
|
|
1603 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0384 - accuracy: 1.0000 - val_loss: 1.6520 - val_accuracy: 0.7222\n", |
|
|
1604 |
"Epoch 474/1000\n", |
|
|
1605 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0223 - accuracy: 1.0000 - val_loss: 1.6092 - val_accuracy: 0.7222\n", |
|
|
1606 |
"Epoch 475/1000\n", |
|
|
1607 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0252 - accuracy: 1.0000 - val_loss: 1.7710 - val_accuracy: 0.7222\n", |
|
|
1608 |
"Epoch 476/1000\n", |
|
|
1609 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0430 - accuracy: 1.0000 - val_loss: 1.7920 - val_accuracy: 0.7222\n", |
|
|
1610 |
"Epoch 477/1000\n", |
|
|
1611 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0253 - accuracy: 1.0000 - val_loss: 1.5582 - val_accuracy: 0.7222\n", |
|
|
1612 |
"Epoch 478/1000\n", |
|
|
1613 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0156 - accuracy: 1.0000 - val_loss: 1.4623 - val_accuracy: 0.7222\n", |
|
|
1614 |
"Epoch 479/1000\n", |
|
|
1615 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0422 - accuracy: 1.0000 - val_loss: 1.5990 - val_accuracy: 0.7222\n", |
|
|
1616 |
"Epoch 480/1000\n", |
|
|
1617 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0224 - accuracy: 1.0000 - val_loss: 1.6891 - val_accuracy: 0.7222\n", |
|
|
1618 |
"Epoch 481/1000\n", |
|
|
1619 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0165 - accuracy: 1.0000 - val_loss: 1.6577 - val_accuracy: 0.7222\n", |
|
|
1620 |
"Epoch 482/1000\n", |
|
|
1621 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0167 - accuracy: 1.0000 - val_loss: 1.6635 - val_accuracy: 0.7222\n", |
|
|
1622 |
"Epoch 483/1000\n", |
|
|
1623 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0206 - accuracy: 1.0000 - val_loss: 1.5843 - val_accuracy: 0.7222\n", |
|
|
1624 |
"Epoch 484/1000\n", |
|
|
1625 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0209 - accuracy: 1.0000 - val_loss: 1.5838 - val_accuracy: 0.7222\n", |
|
|
1626 |
"Epoch 485/1000\n", |
|
|
1627 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0181 - accuracy: 1.0000 - val_loss: 1.5885 - val_accuracy: 0.7222\n", |
|
|
1628 |
"Epoch 486/1000\n", |
|
|
1629 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0313 - accuracy: 1.0000 - val_loss: 1.4435 - val_accuracy: 0.7222\n", |
|
|
1630 |
"Epoch 487/1000\n", |
|
|
1631 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0395 - accuracy: 1.0000 - val_loss: 1.2833 - val_accuracy: 0.7222\n", |
|
|
1632 |
"Epoch 488/1000\n", |
|
|
1633 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0139 - accuracy: 1.0000 - val_loss: 1.3101 - val_accuracy: 0.7222\n", |
|
|
1634 |
"Epoch 489/1000\n", |
|
|
1635 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0117 - accuracy: 1.0000 - val_loss: 1.3722 - val_accuracy: 0.7222\n", |
|
|
1636 |
"Epoch 490/1000\n", |
|
|
1637 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0231 - accuracy: 1.0000 - val_loss: 1.5117 - val_accuracy: 0.7222\n", |
|
|
1638 |
"Epoch 491/1000\n", |
|
|
1639 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0185 - accuracy: 1.0000 - val_loss: 1.6714 - val_accuracy: 0.7222\n", |
|
|
1640 |
"Epoch 492/1000\n", |
|
|
1641 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 1.7651 - val_accuracy: 0.7222\n", |
|
|
1642 |
"Epoch 493/1000\n", |
|
|
1643 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0099 - accuracy: 1.0000 - val_loss: 1.7664 - val_accuracy: 0.7222\n", |
|
|
1644 |
"Epoch 494/1000\n", |
|
|
1645 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0162 - accuracy: 1.0000 - val_loss: 1.7582 - val_accuracy: 0.7222\n", |
|
|
1646 |
"Epoch 495/1000\n", |
|
|
1647 |
"42/42 [==============================] - 0s 504us/sample - loss: 0.0189 - accuracy: 1.0000 - val_loss: 1.7375 - val_accuracy: 0.7222\n", |
|
|
1648 |
"Epoch 496/1000\n", |
|
|
1649 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0140 - accuracy: 1.0000 - val_loss: 1.7064 - val_accuracy: 0.7222\n", |
|
|
1650 |
"Epoch 497/1000\n" |
|
|
1651 |
] |
|
|
1652 |
}, |
|
|
1653 |
{ |
|
|
1654 |
"name": "stdout", |
|
|
1655 |
"output_type": "stream", |
|
|
1656 |
"text": [ |
|
|
1657 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0090 - accuracy: 1.0000 - val_loss: 1.6707 - val_accuracy: 0.7222\n", |
|
|
1658 |
"Epoch 498/1000\n", |
|
|
1659 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0227 - accuracy: 1.0000 - val_loss: 1.6875 - val_accuracy: 0.7222\n", |
|
|
1660 |
"Epoch 499/1000\n", |
|
|
1661 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0245 - accuracy: 1.0000 - val_loss: 1.5153 - val_accuracy: 0.7222\n", |
|
|
1662 |
"Epoch 500/1000\n", |
|
|
1663 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0557 - accuracy: 0.9762 - val_loss: 1.5725 - val_accuracy: 0.7222\n", |
|
|
1664 |
"Epoch 501/1000\n", |
|
|
1665 |
"42/42 [==============================] - 0s 486us/sample - loss: 0.0142 - accuracy: 1.0000 - val_loss: 1.6536 - val_accuracy: 0.7222\n", |
|
|
1666 |
"Epoch 502/1000\n", |
|
|
1667 |
"42/42 [==============================] - 0s 516us/sample - loss: 0.0259 - accuracy: 1.0000 - val_loss: 1.6586 - val_accuracy: 0.7222\n", |
|
|
1668 |
"Epoch 503/1000\n", |
|
|
1669 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0341 - accuracy: 0.9762 - val_loss: 1.4828 - val_accuracy: 0.7222\n", |
|
|
1670 |
"Epoch 504/1000\n", |
|
|
1671 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0294 - accuracy: 1.0000 - val_loss: 1.5081 - val_accuracy: 0.6111\n", |
|
|
1672 |
"Epoch 505/1000\n", |
|
|
1673 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0605 - accuracy: 0.9762 - val_loss: 1.5488 - val_accuracy: 0.7222\n", |
|
|
1674 |
"Epoch 506/1000\n", |
|
|
1675 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0128 - accuracy: 1.0000 - val_loss: 2.0103 - val_accuracy: 0.6667\n", |
|
|
1676 |
"Epoch 507/1000\n", |
|
|
1677 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0380 - accuracy: 1.0000 - val_loss: 2.0019 - val_accuracy: 0.6667\n", |
|
|
1678 |
"Epoch 508/1000\n", |
|
|
1679 |
"42/42 [==============================] - 0s 516us/sample - loss: 0.0416 - accuracy: 1.0000 - val_loss: 1.6441 - val_accuracy: 0.7222\n", |
|
|
1680 |
"Epoch 509/1000\n", |
|
|
1681 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0289 - accuracy: 1.0000 - val_loss: 1.4585 - val_accuracy: 0.6667\n", |
|
|
1682 |
"Epoch 510/1000\n", |
|
|
1683 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0479 - accuracy: 0.9762 - val_loss: 1.6894 - val_accuracy: 0.6667\n", |
|
|
1684 |
"Epoch 511/1000\n", |
|
|
1685 |
"42/42 [==============================] - 0s 517us/sample - loss: 0.0289 - accuracy: 1.0000 - val_loss: 2.1446 - val_accuracy: 0.6667\n", |
|
|
1686 |
"Epoch 512/1000\n", |
|
|
1687 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0292 - accuracy: 1.0000 - val_loss: 2.3211 - val_accuracy: 0.6667\n", |
|
|
1688 |
"Epoch 513/1000\n", |
|
|
1689 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0290 - accuracy: 1.0000 - val_loss: 2.0825 - val_accuracy: 0.6667\n", |
|
|
1690 |
"Epoch 514/1000\n", |
|
|
1691 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0341 - accuracy: 1.0000 - val_loss: 1.7141 - val_accuracy: 0.7222\n", |
|
|
1692 |
"Epoch 515/1000\n", |
|
|
1693 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0308 - accuracy: 1.0000 - val_loss: 1.7743 - val_accuracy: 0.7222\n", |
|
|
1694 |
"Epoch 516/1000\n", |
|
|
1695 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0104 - accuracy: 1.0000 - val_loss: 1.9761 - val_accuracy: 0.6667\n", |
|
|
1696 |
"Epoch 517/1000\n", |
|
|
1697 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0308 - accuracy: 1.0000 - val_loss: 1.9277 - val_accuracy: 0.6667\n", |
|
|
1698 |
"Epoch 518/1000\n", |
|
|
1699 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0251 - accuracy: 1.0000 - val_loss: 1.8559 - val_accuracy: 0.7222\n", |
|
|
1700 |
"Epoch 519/1000\n", |
|
|
1701 |
"42/42 [==============================] - 0s 514us/sample - loss: 0.0118 - accuracy: 1.0000 - val_loss: 1.8657 - val_accuracy: 0.7222\n", |
|
|
1702 |
"Epoch 520/1000\n", |
|
|
1703 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0273 - accuracy: 0.9762 - val_loss: 1.9262 - val_accuracy: 0.7222\n", |
|
|
1704 |
"Epoch 521/1000\n", |
|
|
1705 |
"42/42 [==============================] - 0s 520us/sample - loss: 0.0303 - accuracy: 1.0000 - val_loss: 1.7829 - val_accuracy: 0.7222\n", |
|
|
1706 |
"Epoch 522/1000\n", |
|
|
1707 |
"42/42 [==============================] - 0s 472us/sample - loss: 0.0088 - accuracy: 1.0000 - val_loss: 1.4863 - val_accuracy: 0.7222\n", |
|
|
1708 |
"Epoch 523/1000\n", |
|
|
1709 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0121 - accuracy: 1.0000 - val_loss: 1.3184 - val_accuracy: 0.7222\n", |
|
|
1710 |
"Epoch 524/1000\n", |
|
|
1711 |
"42/42 [==============================] - 0s 526us/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 1.2399 - val_accuracy: 0.7222\n", |
|
|
1712 |
"Epoch 525/1000\n", |
|
|
1713 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0089 - accuracy: 1.0000 - val_loss: 1.2256 - val_accuracy: 0.7222\n", |
|
|
1714 |
"Epoch 526/1000\n", |
|
|
1715 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0087 - accuracy: 1.0000 - val_loss: 1.2406 - val_accuracy: 0.7222\n", |
|
|
1716 |
"Epoch 527/1000\n", |
|
|
1717 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0179 - accuracy: 1.0000 - val_loss: 1.3230 - val_accuracy: 0.7222\n", |
|
|
1718 |
"Epoch 528/1000\n", |
|
|
1719 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0050 - accuracy: 1.0000 - val_loss: 1.4026 - val_accuracy: 0.7222\n", |
|
|
1720 |
"Epoch 529/1000\n", |
|
|
1721 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0135 - accuracy: 1.0000 - val_loss: 1.4636 - val_accuracy: 0.7222\n", |
|
|
1722 |
"Epoch 530/1000\n", |
|
|
1723 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0174 - accuracy: 1.0000 - val_loss: 1.4953 - val_accuracy: 0.7222\n", |
|
|
1724 |
"Epoch 531/1000\n", |
|
|
1725 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0140 - accuracy: 1.0000 - val_loss: 1.4945 - val_accuracy: 0.7222\n", |
|
|
1726 |
"Epoch 532/1000\n", |
|
|
1727 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0362 - accuracy: 0.9762 - val_loss: 1.6865 - val_accuracy: 0.7222\n", |
|
|
1728 |
"Epoch 533/1000\n", |
|
|
1729 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0170 - accuracy: 1.0000 - val_loss: 1.8146 - val_accuracy: 0.7222\n", |
|
|
1730 |
"Epoch 534/1000\n", |
|
|
1731 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0139 - accuracy: 1.0000 - val_loss: 1.8854 - val_accuracy: 0.7222\n", |
|
|
1732 |
"Epoch 535/1000\n", |
|
|
1733 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0077 - accuracy: 1.0000 - val_loss: 1.8935 - val_accuracy: 0.7222\n", |
|
|
1734 |
"Epoch 536/1000\n", |
|
|
1735 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0210 - accuracy: 1.0000 - val_loss: 1.9055 - val_accuracy: 0.7222\n", |
|
|
1736 |
"Epoch 537/1000\n", |
|
|
1737 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0137 - accuracy: 1.0000 - val_loss: 1.9818 - val_accuracy: 0.6667\n", |
|
|
1738 |
"Epoch 538/1000\n", |
|
|
1739 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0405 - accuracy: 1.0000 - val_loss: 1.7330 - val_accuracy: 0.7222\n", |
|
|
1740 |
"Epoch 539/1000\n", |
|
|
1741 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0135 - accuracy: 1.0000 - val_loss: 1.5253 - val_accuracy: 0.7778\n", |
|
|
1742 |
"Epoch 540/1000\n", |
|
|
1743 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0196 - accuracy: 1.0000 - val_loss: 1.5543 - val_accuracy: 0.7778\n", |
|
|
1744 |
"Epoch 541/1000\n", |
|
|
1745 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0251 - accuracy: 1.0000 - val_loss: 1.6256 - val_accuracy: 0.7778\n", |
|
|
1746 |
"Epoch 542/1000\n", |
|
|
1747 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0183 - accuracy: 1.0000 - val_loss: 1.7754 - val_accuracy: 0.7222\n", |
|
|
1748 |
"Epoch 543/1000\n", |
|
|
1749 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0194 - accuracy: 1.0000 - val_loss: 1.8367 - val_accuracy: 0.7222\n", |
|
|
1750 |
"Epoch 544/1000\n", |
|
|
1751 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0203 - accuracy: 1.0000 - val_loss: 1.7837 - val_accuracy: 0.7222\n", |
|
|
1752 |
"Epoch 545/1000\n", |
|
|
1753 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0128 - accuracy: 1.0000 - val_loss: 1.6514 - val_accuracy: 0.7222\n", |
|
|
1754 |
"Epoch 546/1000\n", |
|
|
1755 |
"42/42 [==============================] - 0s 519us/sample - loss: 0.0243 - accuracy: 1.0000 - val_loss: 1.6219 - val_accuracy: 0.7222\n", |
|
|
1756 |
"Epoch 547/1000\n", |
|
|
1757 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0153 - accuracy: 1.0000 - val_loss: 1.7224 - val_accuracy: 0.7222\n", |
|
|
1758 |
"Epoch 548/1000\n", |
|
|
1759 |
"42/42 [==============================] - 0s 511us/sample - loss: 0.0112 - accuracy: 1.0000 - val_loss: 1.7776 - val_accuracy: 0.6667\n", |
|
|
1760 |
"Epoch 549/1000\n", |
|
|
1761 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0366 - accuracy: 0.9762 - val_loss: 1.8968 - val_accuracy: 0.6667\n", |
|
|
1762 |
"Epoch 550/1000\n", |
|
|
1763 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0211 - accuracy: 1.0000 - val_loss: 1.9872 - val_accuracy: 0.6667\n", |
|
|
1764 |
"Epoch 551/1000\n", |
|
|
1765 |
"42/42 [==============================] - 0s 506us/sample - loss: 0.0250 - accuracy: 1.0000 - val_loss: 1.8677 - val_accuracy: 0.6667\n", |
|
|
1766 |
"Epoch 552/1000\n" |
|
|
1767 |
] |
|
|
1768 |
}, |
|
|
1769 |
{ |
|
|
1770 |
"name": "stdout", |
|
|
1771 |
"output_type": "stream", |
|
|
1772 |
"text": [ |
|
|
1773 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0170 - accuracy: 1.0000 - val_loss: 1.5129 - val_accuracy: 0.6667\n", |
|
|
1774 |
"Epoch 553/1000\n", |
|
|
1775 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0523 - accuracy: 0.9762 - val_loss: 1.3923 - val_accuracy: 0.6667\n", |
|
|
1776 |
"Epoch 554/1000\n", |
|
|
1777 |
"42/42 [==============================] - 0s 515us/sample - loss: 0.0127 - accuracy: 1.0000 - val_loss: 1.3206 - val_accuracy: 0.6667\n", |
|
|
1778 |
"Epoch 555/1000\n", |
|
|
1779 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0129 - accuracy: 1.0000 - val_loss: 1.3077 - val_accuracy: 0.6667\n", |
|
|
1780 |
"Epoch 556/1000\n", |
|
|
1781 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0162 - accuracy: 1.0000 - val_loss: 1.4425 - val_accuracy: 0.7222\n", |
|
|
1782 |
"Epoch 557/1000\n", |
|
|
1783 |
"42/42 [==============================] - 0s 502us/sample - loss: 0.0073 - accuracy: 1.0000 - val_loss: 1.6287 - val_accuracy: 0.7222\n", |
|
|
1784 |
"Epoch 558/1000\n", |
|
|
1785 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0160 - accuracy: 1.0000 - val_loss: 1.7290 - val_accuracy: 0.7222\n", |
|
|
1786 |
"Epoch 559/1000\n", |
|
|
1787 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0186 - accuracy: 1.0000 - val_loss: 1.6338 - val_accuracy: 0.7222\n", |
|
|
1788 |
"Epoch 560/1000\n", |
|
|
1789 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0150 - accuracy: 1.0000 - val_loss: 1.5954 - val_accuracy: 0.7222\n", |
|
|
1790 |
"Epoch 561/1000\n", |
|
|
1791 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0092 - accuracy: 1.0000 - val_loss: 1.6218 - val_accuracy: 0.7222\n", |
|
|
1792 |
"Epoch 562/1000\n", |
|
|
1793 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0166 - accuracy: 1.0000 - val_loss: 1.7047 - val_accuracy: 0.7222\n", |
|
|
1794 |
"Epoch 563/1000\n", |
|
|
1795 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0105 - accuracy: 1.0000 - val_loss: 1.8302 - val_accuracy: 0.7222\n", |
|
|
1796 |
"Epoch 564/1000\n", |
|
|
1797 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0130 - accuracy: 1.0000 - val_loss: 1.8701 - val_accuracy: 0.7222\n", |
|
|
1798 |
"Epoch 565/1000\n", |
|
|
1799 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0235 - accuracy: 1.0000 - val_loss: 1.7874 - val_accuracy: 0.7222\n", |
|
|
1800 |
"Epoch 566/1000\n", |
|
|
1801 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0170 - accuracy: 1.0000 - val_loss: 1.5857 - val_accuracy: 0.7222\n", |
|
|
1802 |
"Epoch 567/1000\n", |
|
|
1803 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0074 - accuracy: 1.0000 - val_loss: 1.3905 - val_accuracy: 0.7222\n", |
|
|
1804 |
"Epoch 568/1000\n", |
|
|
1805 |
"42/42 [==============================] - 0s 486us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.2753 - val_accuracy: 0.7222\n", |
|
|
1806 |
"Epoch 569/1000\n", |
|
|
1807 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0220 - accuracy: 1.0000 - val_loss: 1.2183 - val_accuracy: 0.7222\n", |
|
|
1808 |
"Epoch 570/1000\n", |
|
|
1809 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0110 - accuracy: 1.0000 - val_loss: 1.2636 - val_accuracy: 0.7222\n", |
|
|
1810 |
"Epoch 571/1000\n", |
|
|
1811 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0169 - accuracy: 1.0000 - val_loss: 1.4935 - val_accuracy: 0.7222\n", |
|
|
1812 |
"Epoch 572/1000\n", |
|
|
1813 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0105 - accuracy: 1.0000 - val_loss: 1.6317 - val_accuracy: 0.7222\n", |
|
|
1814 |
"Epoch 573/1000\n", |
|
|
1815 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0073 - accuracy: 1.0000 - val_loss: 1.7286 - val_accuracy: 0.7222\n", |
|
|
1816 |
"Epoch 574/1000\n", |
|
|
1817 |
"42/42 [==============================] - 0s 501us/sample - loss: 0.0108 - accuracy: 1.0000 - val_loss: 1.7019 - val_accuracy: 0.7222\n", |
|
|
1818 |
"Epoch 575/1000\n", |
|
|
1819 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0128 - accuracy: 1.0000 - val_loss: 1.6070 - val_accuracy: 0.7222\n", |
|
|
1820 |
"Epoch 576/1000\n", |
|
|
1821 |
"42/42 [==============================] - 0s 476us/sample - loss: 0.0043 - accuracy: 1.0000 - val_loss: 1.5393 - val_accuracy: 0.7222\n", |
|
|
1822 |
"Epoch 577/1000\n", |
|
|
1823 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.5040 - val_accuracy: 0.7222\n", |
|
|
1824 |
"Epoch 578/1000\n", |
|
|
1825 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.4798 - val_accuracy: 0.7222\n", |
|
|
1826 |
"Epoch 579/1000\n", |
|
|
1827 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0185 - accuracy: 1.0000 - val_loss: 1.5227 - val_accuracy: 0.7222\n", |
|
|
1828 |
"Epoch 580/1000\n", |
|
|
1829 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0050 - accuracy: 1.0000 - val_loss: 1.6025 - val_accuracy: 0.7222\n", |
|
|
1830 |
"Epoch 581/1000\n", |
|
|
1831 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 1.6589 - val_accuracy: 0.7222\n", |
|
|
1832 |
"Epoch 582/1000\n", |
|
|
1833 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0158 - accuracy: 1.0000 - val_loss: 1.6404 - val_accuracy: 0.7222\n", |
|
|
1834 |
"Epoch 583/1000\n", |
|
|
1835 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.5661 - val_accuracy: 0.7222\n", |
|
|
1836 |
"Epoch 584/1000\n", |
|
|
1837 |
"42/42 [==============================] - 0s 570us/sample - loss: 0.0058 - accuracy: 1.0000 - val_loss: 1.5443 - val_accuracy: 0.7222\n", |
|
|
1838 |
"Epoch 585/1000\n", |
|
|
1839 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 1.5536 - val_accuracy: 0.7222\n", |
|
|
1840 |
"Epoch 586/1000\n", |
|
|
1841 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.5814 - val_accuracy: 0.7222\n", |
|
|
1842 |
"Epoch 587/1000\n", |
|
|
1843 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0183 - accuracy: 1.0000 - val_loss: 1.6663 - val_accuracy: 0.7222\n", |
|
|
1844 |
"Epoch 588/1000\n", |
|
|
1845 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.8056 - val_accuracy: 0.7222\n", |
|
|
1846 |
"Epoch 589/1000\n", |
|
|
1847 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0077 - accuracy: 1.0000 - val_loss: 1.8514 - val_accuracy: 0.6667\n", |
|
|
1848 |
"Epoch 590/1000\n", |
|
|
1849 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0155 - accuracy: 1.0000 - val_loss: 1.7950 - val_accuracy: 0.7222\n", |
|
|
1850 |
"Epoch 591/1000\n", |
|
|
1851 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.6843 - val_accuracy: 0.7222\n", |
|
|
1852 |
"Epoch 592/1000\n", |
|
|
1853 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0120 - accuracy: 1.0000 - val_loss: 1.6214 - val_accuracy: 0.7222\n", |
|
|
1854 |
"Epoch 593/1000\n", |
|
|
1855 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0192 - accuracy: 1.0000 - val_loss: 1.6210 - val_accuracy: 0.7222\n", |
|
|
1856 |
"Epoch 594/1000\n", |
|
|
1857 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0105 - accuracy: 1.0000 - val_loss: 1.6539 - val_accuracy: 0.7222\n", |
|
|
1858 |
"Epoch 595/1000\n", |
|
|
1859 |
"42/42 [==============================] - 0s 488us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.6946 - val_accuracy: 0.7222\n", |
|
|
1860 |
"Epoch 596/1000\n", |
|
|
1861 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0173 - accuracy: 1.0000 - val_loss: 1.7532 - val_accuracy: 0.7222\n", |
|
|
1862 |
"Epoch 597/1000\n", |
|
|
1863 |
"42/42 [==============================] - 0s 527us/sample - loss: 0.0111 - accuracy: 1.0000 - val_loss: 1.6964 - val_accuracy: 0.6667\n", |
|
|
1864 |
"Epoch 598/1000\n", |
|
|
1865 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0148 - accuracy: 1.0000 - val_loss: 1.6022 - val_accuracy: 0.7222\n", |
|
|
1866 |
"Epoch 599/1000\n", |
|
|
1867 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 1.5252 - val_accuracy: 0.7222\n", |
|
|
1868 |
"Epoch 600/1000\n", |
|
|
1869 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0094 - accuracy: 1.0000 - val_loss: 1.5429 - val_accuracy: 0.7222\n", |
|
|
1870 |
"Epoch 601/1000\n", |
|
|
1871 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 1.6324 - val_accuracy: 0.7222\n", |
|
|
1872 |
"Epoch 602/1000\n", |
|
|
1873 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 1.7574 - val_accuracy: 0.7222\n", |
|
|
1874 |
"Epoch 603/1000\n", |
|
|
1875 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0080 - accuracy: 1.0000 - val_loss: 1.9506 - val_accuracy: 0.7222\n", |
|
|
1876 |
"Epoch 604/1000\n", |
|
|
1877 |
"42/42 [==============================] - 0s 513us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 2.0149 - val_accuracy: 0.7222\n", |
|
|
1878 |
"Epoch 605/1000\n", |
|
|
1879 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 1.9372 - val_accuracy: 0.7222\n", |
|
|
1880 |
"Epoch 606/1000\n", |
|
|
1881 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.8199 - val_accuracy: 0.7222\n", |
|
|
1882 |
"Epoch 607/1000\n" |
|
|
1883 |
] |
|
|
1884 |
}, |
|
|
1885 |
{ |
|
|
1886 |
"name": "stdout", |
|
|
1887 |
"output_type": "stream", |
|
|
1888 |
"text": [ |
|
|
1889 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0124 - accuracy: 1.0000 - val_loss: 1.6705 - val_accuracy: 0.7222\n", |
|
|
1890 |
"Epoch 608/1000\n", |
|
|
1891 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0129 - accuracy: 1.0000 - val_loss: 1.5627 - val_accuracy: 0.7222\n", |
|
|
1892 |
"Epoch 609/1000\n", |
|
|
1893 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0147 - accuracy: 1.0000 - val_loss: 1.5280 - val_accuracy: 0.7222\n", |
|
|
1894 |
"Epoch 610/1000\n", |
|
|
1895 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.0318 - accuracy: 0.9762 - val_loss: 1.4541 - val_accuracy: 0.7222\n", |
|
|
1896 |
"Epoch 611/1000\n", |
|
|
1897 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0616 - accuracy: 0.9762 - val_loss: 1.5125 - val_accuracy: 0.6667\n", |
|
|
1898 |
"Epoch 612/1000\n", |
|
|
1899 |
"42/42 [==============================] - 0s 467us/sample - loss: 0.0537 - accuracy: 0.9524 - val_loss: 1.5743 - val_accuracy: 0.7222\n", |
|
|
1900 |
"Epoch 613/1000\n", |
|
|
1901 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0075 - accuracy: 1.0000 - val_loss: 1.4203 - val_accuracy: 0.7222\n", |
|
|
1902 |
"Epoch 614/1000\n", |
|
|
1903 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0089 - accuracy: 1.0000 - val_loss: 1.3235 - val_accuracy: 0.7222\n", |
|
|
1904 |
"Epoch 615/1000\n", |
|
|
1905 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0406 - accuracy: 0.9762 - val_loss: 1.5453 - val_accuracy: 0.7222\n", |
|
|
1906 |
"Epoch 616/1000\n", |
|
|
1907 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.7298 - val_accuracy: 0.7222\n", |
|
|
1908 |
"Epoch 617/1000\n", |
|
|
1909 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0173 - accuracy: 1.0000 - val_loss: 1.8037 - val_accuracy: 0.7222\n", |
|
|
1910 |
"Epoch 618/1000\n", |
|
|
1911 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.0168 - accuracy: 1.0000 - val_loss: 1.7395 - val_accuracy: 0.7222\n", |
|
|
1912 |
"Epoch 619/1000\n", |
|
|
1913 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0151 - accuracy: 1.0000 - val_loss: 1.6783 - val_accuracy: 0.7222\n", |
|
|
1914 |
"Epoch 620/1000\n", |
|
|
1915 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 1.5844 - val_accuracy: 0.7222\n", |
|
|
1916 |
"Epoch 621/1000\n", |
|
|
1917 |
"42/42 [==============================] - 0s 485us/sample - loss: 0.0225 - accuracy: 1.0000 - val_loss: 1.6570 - val_accuracy: 0.7222\n", |
|
|
1918 |
"Epoch 622/1000\n", |
|
|
1919 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0200 - accuracy: 1.0000 - val_loss: 1.8673 - val_accuracy: 0.7222\n", |
|
|
1920 |
"Epoch 623/1000\n", |
|
|
1921 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 2.0052 - val_accuracy: 0.7222\n", |
|
|
1922 |
"Epoch 624/1000\n", |
|
|
1923 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0227 - accuracy: 1.0000 - val_loss: 1.9992 - val_accuracy: 0.7222\n", |
|
|
1924 |
"Epoch 625/1000\n", |
|
|
1925 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0121 - accuracy: 1.0000 - val_loss: 1.8718 - val_accuracy: 0.7222\n", |
|
|
1926 |
"Epoch 626/1000\n", |
|
|
1927 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0310 - accuracy: 1.0000 - val_loss: 1.4662 - val_accuracy: 0.7222\n", |
|
|
1928 |
"Epoch 627/1000\n", |
|
|
1929 |
"42/42 [==============================] - 0s 502us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.3800 - val_accuracy: 0.7222\n", |
|
|
1930 |
"Epoch 628/1000\n", |
|
|
1931 |
"42/42 [==============================] - 0s 504us/sample - loss: 0.0188 - accuracy: 1.0000 - val_loss: 1.4251 - val_accuracy: 0.7222\n", |
|
|
1932 |
"Epoch 629/1000\n", |
|
|
1933 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0082 - accuracy: 1.0000 - val_loss: 1.5202 - val_accuracy: 0.7222\n", |
|
|
1934 |
"Epoch 630/1000\n", |
|
|
1935 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0111 - accuracy: 1.0000 - val_loss: 1.5724 - val_accuracy: 0.7222\n", |
|
|
1936 |
"Epoch 631/1000\n", |
|
|
1937 |
"42/42 [==============================] - 0s 516us/sample - loss: 0.0078 - accuracy: 1.0000 - val_loss: 1.5964 - val_accuracy: 0.7222\n", |
|
|
1938 |
"Epoch 632/1000\n", |
|
|
1939 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0080 - accuracy: 1.0000 - val_loss: 1.6382 - val_accuracy: 0.7222\n", |
|
|
1940 |
"Epoch 633/1000\n", |
|
|
1941 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0148 - accuracy: 1.0000 - val_loss: 1.6382 - val_accuracy: 0.7222\n", |
|
|
1942 |
"Epoch 634/1000\n", |
|
|
1943 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0151 - accuracy: 1.0000 - val_loss: 1.6768 - val_accuracy: 0.7222\n", |
|
|
1944 |
"Epoch 635/1000\n", |
|
|
1945 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0144 - accuracy: 1.0000 - val_loss: 1.7331 - val_accuracy: 0.7222\n", |
|
|
1946 |
"Epoch 636/1000\n", |
|
|
1947 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0111 - accuracy: 1.0000 - val_loss: 1.7414 - val_accuracy: 0.7222\n", |
|
|
1948 |
"Epoch 637/1000\n", |
|
|
1949 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0166 - accuracy: 1.0000 - val_loss: 1.6492 - val_accuracy: 0.7222\n", |
|
|
1950 |
"Epoch 638/1000\n", |
|
|
1951 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0046 - accuracy: 1.0000 - val_loss: 1.5875 - val_accuracy: 0.7222\n", |
|
|
1952 |
"Epoch 639/1000\n", |
|
|
1953 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0162 - accuracy: 1.0000 - val_loss: 1.4475 - val_accuracy: 0.7222\n", |
|
|
1954 |
"Epoch 640/1000\n", |
|
|
1955 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0147 - accuracy: 1.0000 - val_loss: 1.3676 - val_accuracy: 0.7222\n", |
|
|
1956 |
"Epoch 641/1000\n", |
|
|
1957 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 1.3965 - val_accuracy: 0.6667\n", |
|
|
1958 |
"Epoch 642/1000\n", |
|
|
1959 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0136 - accuracy: 1.0000 - val_loss: 1.4324 - val_accuracy: 0.7222\n", |
|
|
1960 |
"Epoch 643/1000\n", |
|
|
1961 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0052 - accuracy: 1.0000 - val_loss: 1.5868 - val_accuracy: 0.7222\n", |
|
|
1962 |
"Epoch 644/1000\n", |
|
|
1963 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0174 - accuracy: 1.0000 - val_loss: 1.7449 - val_accuracy: 0.7222\n", |
|
|
1964 |
"Epoch 645/1000\n", |
|
|
1965 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.8754 - val_accuracy: 0.7222\n", |
|
|
1966 |
"Epoch 646/1000\n", |
|
|
1967 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0077 - accuracy: 1.0000 - val_loss: 1.9516 - val_accuracy: 0.7222\n", |
|
|
1968 |
"Epoch 647/1000\n", |
|
|
1969 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0187 - accuracy: 1.0000 - val_loss: 1.8594 - val_accuracy: 0.7222\n", |
|
|
1970 |
"Epoch 648/1000\n", |
|
|
1971 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0065 - accuracy: 1.0000 - val_loss: 1.7436 - val_accuracy: 0.7222\n", |
|
|
1972 |
"Epoch 649/1000\n", |
|
|
1973 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0130 - accuracy: 1.0000 - val_loss: 1.6485 - val_accuracy: 0.7222\n", |
|
|
1974 |
"Epoch 650/1000\n", |
|
|
1975 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0181 - accuracy: 1.0000 - val_loss: 1.5528 - val_accuracy: 0.7222\n", |
|
|
1976 |
"Epoch 651/1000\n", |
|
|
1977 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0111 - accuracy: 1.0000 - val_loss: 1.4427 - val_accuracy: 0.7222\n", |
|
|
1978 |
"Epoch 652/1000\n", |
|
|
1979 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0115 - accuracy: 1.0000 - val_loss: 1.4310 - val_accuracy: 0.7778\n", |
|
|
1980 |
"Epoch 653/1000\n", |
|
|
1981 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0156 - accuracy: 1.0000 - val_loss: 1.6274 - val_accuracy: 0.7222\n", |
|
|
1982 |
"Epoch 654/1000\n", |
|
|
1983 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0167 - accuracy: 1.0000 - val_loss: 1.8721 - val_accuracy: 0.7222\n", |
|
|
1984 |
"Epoch 655/1000\n", |
|
|
1985 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0175 - accuracy: 1.0000 - val_loss: 1.8476 - val_accuracy: 0.7222\n", |
|
|
1986 |
"Epoch 656/1000\n", |
|
|
1987 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0090 - accuracy: 1.0000 - val_loss: 1.6647 - val_accuracy: 0.7222\n", |
|
|
1988 |
"Epoch 657/1000\n", |
|
|
1989 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.0204 - accuracy: 1.0000 - val_loss: 1.4780 - val_accuracy: 0.7222\n", |
|
|
1990 |
"Epoch 658/1000\n", |
|
|
1991 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0108 - accuracy: 1.0000 - val_loss: 1.3878 - val_accuracy: 0.7778\n", |
|
|
1992 |
"Epoch 659/1000\n", |
|
|
1993 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 1.4178 - val_accuracy: 0.7778\n", |
|
|
1994 |
"Epoch 660/1000\n", |
|
|
1995 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0071 - accuracy: 1.0000 - val_loss: 1.4752 - val_accuracy: 0.7778\n", |
|
|
1996 |
"Epoch 661/1000\n", |
|
|
1997 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 1.5344 - val_accuracy: 0.7778\n", |
|
|
1998 |
"Epoch 662/1000\n" |
|
|
1999 |
] |
|
|
2000 |
}, |
|
|
2001 |
{ |
|
|
2002 |
"name": "stdout", |
|
|
2003 |
"output_type": "stream", |
|
|
2004 |
"text": [ |
|
|
2005 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0206 - accuracy: 1.0000 - val_loss: 1.5791 - val_accuracy: 0.7778\n", |
|
|
2006 |
"Epoch 663/1000\n", |
|
|
2007 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.8243 - val_accuracy: 0.7222\n", |
|
|
2008 |
"Epoch 664/1000\n", |
|
|
2009 |
"42/42 [==============================] - 0s 501us/sample - loss: 0.0180 - accuracy: 1.0000 - val_loss: 1.8792 - val_accuracy: 0.6667\n", |
|
|
2010 |
"Epoch 665/1000\n", |
|
|
2011 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0088 - accuracy: 1.0000 - val_loss: 1.5814 - val_accuracy: 0.6667\n", |
|
|
2012 |
"Epoch 666/1000\n", |
|
|
2013 |
"42/42 [==============================] - 0s 519us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.3163 - val_accuracy: 0.6667\n", |
|
|
2014 |
"Epoch 667/1000\n", |
|
|
2015 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 1.1735 - val_accuracy: 0.6667\n", |
|
|
2016 |
"Epoch 668/1000\n", |
|
|
2017 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.1108 - val_accuracy: 0.6667\n", |
|
|
2018 |
"Epoch 669/1000\n", |
|
|
2019 |
"42/42 [==============================] - 0s 517us/sample - loss: 0.0075 - accuracy: 1.0000 - val_loss: 1.1734 - val_accuracy: 0.7222\n", |
|
|
2020 |
"Epoch 670/1000\n", |
|
|
2021 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0157 - accuracy: 1.0000 - val_loss: 1.2488 - val_accuracy: 0.7222\n", |
|
|
2022 |
"Epoch 671/1000\n", |
|
|
2023 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.3575 - val_accuracy: 0.7222\n", |
|
|
2024 |
"Epoch 672/1000\n", |
|
|
2025 |
"42/42 [==============================] - 0s 506us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.4691 - val_accuracy: 0.6667\n", |
|
|
2026 |
"Epoch 673/1000\n", |
|
|
2027 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 1.5783 - val_accuracy: 0.6667\n", |
|
|
2028 |
"Epoch 674/1000\n", |
|
|
2029 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 1.6785 - val_accuracy: 0.6667\n", |
|
|
2030 |
"Epoch 675/1000\n", |
|
|
2031 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.7220 - val_accuracy: 0.7222\n", |
|
|
2032 |
"Epoch 676/1000\n", |
|
|
2033 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0038 - accuracy: 1.0000 - val_loss: 1.7529 - val_accuracy: 0.7222\n", |
|
|
2034 |
"Epoch 677/1000\n", |
|
|
2035 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 1.7071 - val_accuracy: 0.7222\n", |
|
|
2036 |
"Epoch 678/1000\n", |
|
|
2037 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0260 - accuracy: 0.9762 - val_loss: 1.5443 - val_accuracy: 0.7222\n", |
|
|
2038 |
"Epoch 679/1000\n", |
|
|
2039 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 1.4808 - val_accuracy: 0.7222\n", |
|
|
2040 |
"Epoch 680/1000\n", |
|
|
2041 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0572 - accuracy: 0.9762 - val_loss: 1.5780 - val_accuracy: 0.7222\n", |
|
|
2042 |
"Epoch 681/1000\n", |
|
|
2043 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.5178 - val_accuracy: 0.7222\n", |
|
|
2044 |
"Epoch 682/1000\n", |
|
|
2045 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 1.5853 - val_accuracy: 0.7222\n", |
|
|
2046 |
"Epoch 683/1000\n", |
|
|
2047 |
"42/42 [==============================] - 0s 547us/sample - loss: 0.0306 - accuracy: 1.0000 - val_loss: 1.6619 - val_accuracy: 0.7778\n", |
|
|
2048 |
"Epoch 684/1000\n", |
|
|
2049 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0105 - accuracy: 1.0000 - val_loss: 1.7911 - val_accuracy: 0.7222\n", |
|
|
2050 |
"Epoch 685/1000\n", |
|
|
2051 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.0958 - accuracy: 0.9524 - val_loss: 1.4905 - val_accuracy: 0.7222\n", |
|
|
2052 |
"Epoch 686/1000\n", |
|
|
2053 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0177 - accuracy: 1.0000 - val_loss: 1.2817 - val_accuracy: 0.6667\n", |
|
|
2054 |
"Epoch 687/1000\n", |
|
|
2055 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0485 - accuracy: 0.9762 - val_loss: 1.2613 - val_accuracy: 0.6667\n", |
|
|
2056 |
"Epoch 688/1000\n", |
|
|
2057 |
"42/42 [==============================] - 0s 489us/sample - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.4677 - val_accuracy: 0.7222\n", |
|
|
2058 |
"Epoch 689/1000\n", |
|
|
2059 |
"42/42 [==============================] - 0s 545us/sample - loss: 0.0164 - accuracy: 1.0000 - val_loss: 1.7160 - val_accuracy: 0.7222\n", |
|
|
2060 |
"Epoch 690/1000\n", |
|
|
2061 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0316 - accuracy: 1.0000 - val_loss: 1.6507 - val_accuracy: 0.7222\n", |
|
|
2062 |
"Epoch 691/1000\n", |
|
|
2063 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0307 - accuracy: 1.0000 - val_loss: 1.6348 - val_accuracy: 0.7222\n", |
|
|
2064 |
"Epoch 692/1000\n", |
|
|
2065 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0198 - accuracy: 1.0000 - val_loss: 1.7474 - val_accuracy: 0.7222\n", |
|
|
2066 |
"Epoch 693/1000\n", |
|
|
2067 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.9097 - val_accuracy: 0.7222\n", |
|
|
2068 |
"Epoch 694/1000\n", |
|
|
2069 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0089 - accuracy: 1.0000 - val_loss: 1.9898 - val_accuracy: 0.7222\n", |
|
|
2070 |
"Epoch 695/1000\n", |
|
|
2071 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0077 - accuracy: 1.0000 - val_loss: 1.9726 - val_accuracy: 0.7222\n", |
|
|
2072 |
"Epoch 696/1000\n", |
|
|
2073 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0135 - accuracy: 1.0000 - val_loss: 1.9087 - val_accuracy: 0.7222\n", |
|
|
2074 |
"Epoch 697/1000\n", |
|
|
2075 |
"42/42 [==============================] - 0s 486us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 1.8554 - val_accuracy: 0.7222\n", |
|
|
2076 |
"Epoch 698/1000\n", |
|
|
2077 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.8064 - val_accuracy: 0.7222\n", |
|
|
2078 |
"Epoch 699/1000\n", |
|
|
2079 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0137 - accuracy: 1.0000 - val_loss: 1.6524 - val_accuracy: 0.7222\n", |
|
|
2080 |
"Epoch 700/1000\n", |
|
|
2081 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0200 - accuracy: 1.0000 - val_loss: 1.5041 - val_accuracy: 0.7222\n", |
|
|
2082 |
"Epoch 701/1000\n", |
|
|
2083 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0197 - accuracy: 1.0000 - val_loss: 1.4974 - val_accuracy: 0.7222\n", |
|
|
2084 |
"Epoch 702/1000\n", |
|
|
2085 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0106 - accuracy: 1.0000 - val_loss: 1.5216 - val_accuracy: 0.7222\n", |
|
|
2086 |
"Epoch 703/1000\n", |
|
|
2087 |
"42/42 [==============================] - 0s 501us/sample - loss: 0.0071 - accuracy: 1.0000 - val_loss: 1.5473 - val_accuracy: 0.7222\n", |
|
|
2088 |
"Epoch 704/1000\n", |
|
|
2089 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0221 - accuracy: 1.0000 - val_loss: 1.4593 - val_accuracy: 0.7222\n", |
|
|
2090 |
"Epoch 705/1000\n", |
|
|
2091 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0072 - accuracy: 1.0000 - val_loss: 1.4022 - val_accuracy: 0.7222\n", |
|
|
2092 |
"Epoch 706/1000\n", |
|
|
2093 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 1.4135 - val_accuracy: 0.7222\n", |
|
|
2094 |
"Epoch 707/1000\n", |
|
|
2095 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0368 - accuracy: 0.9762 - val_loss: 1.4300 - val_accuracy: 0.7222\n", |
|
|
2096 |
"Epoch 708/1000\n", |
|
|
2097 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0078 - accuracy: 1.0000 - val_loss: 1.5547 - val_accuracy: 0.6667\n", |
|
|
2098 |
"Epoch 709/1000\n", |
|
|
2099 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 1.6830 - val_accuracy: 0.6667\n", |
|
|
2100 |
"Epoch 710/1000\n", |
|
|
2101 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0102 - accuracy: 1.0000 - val_loss: 1.8006 - val_accuracy: 0.7222\n", |
|
|
2102 |
"Epoch 711/1000\n", |
|
|
2103 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0245 - accuracy: 1.0000 - val_loss: 1.9890 - val_accuracy: 0.7222\n", |
|
|
2104 |
"Epoch 712/1000\n", |
|
|
2105 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0149 - accuracy: 1.0000 - val_loss: 2.0844 - val_accuracy: 0.7222\n", |
|
|
2106 |
"Epoch 713/1000\n", |
|
|
2107 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0274 - accuracy: 1.0000 - val_loss: 1.8323 - val_accuracy: 0.7222\n", |
|
|
2108 |
"Epoch 714/1000\n", |
|
|
2109 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.0041 - accuracy: 1.0000 - val_loss: 1.5676 - val_accuracy: 0.7222\n", |
|
|
2110 |
"Epoch 715/1000\n", |
|
|
2111 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.0095 - accuracy: 1.0000 - val_loss: 1.4506 - val_accuracy: 0.7778\n", |
|
|
2112 |
"Epoch 716/1000\n", |
|
|
2113 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0132 - accuracy: 1.0000 - val_loss: 1.4458 - val_accuracy: 0.7778\n", |
|
|
2114 |
"Epoch 717/1000\n" |
|
|
2115 |
] |
|
|
2116 |
}, |
|
|
2117 |
{ |
|
|
2118 |
"name": "stdout", |
|
|
2119 |
"output_type": "stream", |
|
|
2120 |
"text": [ |
|
|
2121 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0527 - accuracy: 0.9524 - val_loss: 1.8175 - val_accuracy: 0.7222\n", |
|
|
2122 |
"Epoch 718/1000\n", |
|
|
2123 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0133 - accuracy: 1.0000 - val_loss: 2.0525 - val_accuracy: 0.7222\n", |
|
|
2124 |
"Epoch 719/1000\n", |
|
|
2125 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0244 - accuracy: 0.9762 - val_loss: 2.1828 - val_accuracy: 0.7222\n", |
|
|
2126 |
"Epoch 720/1000\n", |
|
|
2127 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 2.2259 - val_accuracy: 0.7222\n", |
|
|
2128 |
"Epoch 721/1000\n", |
|
|
2129 |
"42/42 [==============================] - 0s 488us/sample - loss: 0.0118 - accuracy: 1.0000 - val_loss: 2.2351 - val_accuracy: 0.7222\n", |
|
|
2130 |
"Epoch 722/1000\n", |
|
|
2131 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0291 - accuracy: 0.9762 - val_loss: 1.9597 - val_accuracy: 0.7222\n", |
|
|
2132 |
"Epoch 723/1000\n", |
|
|
2133 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0041 - accuracy: 1.0000 - val_loss: 1.6513 - val_accuracy: 0.7222\n", |
|
|
2134 |
"Epoch 724/1000\n", |
|
|
2135 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0255 - accuracy: 1.0000 - val_loss: 1.6510 - val_accuracy: 0.7222\n", |
|
|
2136 |
"Epoch 725/1000\n", |
|
|
2137 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0103 - accuracy: 1.0000 - val_loss: 2.0215 - val_accuracy: 0.7222\n", |
|
|
2138 |
"Epoch 726/1000\n", |
|
|
2139 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0077 - accuracy: 1.0000 - val_loss: 2.2696 - val_accuracy: 0.6667\n", |
|
|
2140 |
"Epoch 727/1000\n", |
|
|
2141 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0131 - accuracy: 1.0000 - val_loss: 2.2089 - val_accuracy: 0.6667\n", |
|
|
2142 |
"Epoch 728/1000\n", |
|
|
2143 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0156 - accuracy: 1.0000 - val_loss: 1.9754 - val_accuracy: 0.7222\n", |
|
|
2144 |
"Epoch 729/1000\n", |
|
|
2145 |
"42/42 [==============================] - 0s 515us/sample - loss: 0.0230 - accuracy: 0.9762 - val_loss: 1.7600 - val_accuracy: 0.7222\n", |
|
|
2146 |
"Epoch 730/1000\n", |
|
|
2147 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 1.6390 - val_accuracy: 0.7222\n", |
|
|
2148 |
"Epoch 731/1000\n", |
|
|
2149 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0169 - accuracy: 1.0000 - val_loss: 1.5902 - val_accuracy: 0.7222\n", |
|
|
2150 |
"Epoch 732/1000\n", |
|
|
2151 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 1.6225 - val_accuracy: 0.7222\n", |
|
|
2152 |
"Epoch 733/1000\n", |
|
|
2153 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0091 - accuracy: 1.0000 - val_loss: 1.6918 - val_accuracy: 0.7222\n", |
|
|
2154 |
"Epoch 734/1000\n", |
|
|
2155 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.7428 - val_accuracy: 0.7222\n", |
|
|
2156 |
"Epoch 735/1000\n", |
|
|
2157 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0104 - accuracy: 1.0000 - val_loss: 1.7624 - val_accuracy: 0.7222\n", |
|
|
2158 |
"Epoch 736/1000\n", |
|
|
2159 |
"42/42 [==============================] - ETA: 0s - loss: 0.0116 - accuracy: 1.00 - 0s 522us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.7870 - val_accuracy: 0.7222\n", |
|
|
2160 |
"Epoch 737/1000\n", |
|
|
2161 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 1.7712 - val_accuracy: 0.7222\n", |
|
|
2162 |
"Epoch 738/1000\n", |
|
|
2163 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 1.7542 - val_accuracy: 0.7222\n", |
|
|
2164 |
"Epoch 739/1000\n", |
|
|
2165 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0065 - accuracy: 1.0000 - val_loss: 1.7599 - val_accuracy: 0.7222\n", |
|
|
2166 |
"Epoch 740/1000\n", |
|
|
2167 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 1.7891 - val_accuracy: 0.7222\n", |
|
|
2168 |
"Epoch 741/1000\n", |
|
|
2169 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.8112 - val_accuracy: 0.7222\n", |
|
|
2170 |
"Epoch 742/1000\n", |
|
|
2171 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.8216 - val_accuracy: 0.7222\n", |
|
|
2172 |
"Epoch 743/1000\n", |
|
|
2173 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0113 - accuracy: 1.0000 - val_loss: 1.7721 - val_accuracy: 0.7222\n", |
|
|
2174 |
"Epoch 744/1000\n", |
|
|
2175 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0045 - accuracy: 1.0000 - val_loss: 1.6875 - val_accuracy: 0.7222\n", |
|
|
2176 |
"Epoch 745/1000\n", |
|
|
2177 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0063 - accuracy: 1.0000 - val_loss: 1.6387 - val_accuracy: 0.7222\n", |
|
|
2178 |
"Epoch 746/1000\n", |
|
|
2179 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.6142 - val_accuracy: 0.7222\n", |
|
|
2180 |
"Epoch 747/1000\n", |
|
|
2181 |
"42/42 [==============================] - 0s 497us/sample - loss: 0.0134 - accuracy: 1.0000 - val_loss: 1.6501 - val_accuracy: 0.7222\n", |
|
|
2182 |
"Epoch 748/1000\n", |
|
|
2183 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0071 - accuracy: 1.0000 - val_loss: 1.8220 - val_accuracy: 0.7222\n", |
|
|
2184 |
"Epoch 749/1000\n", |
|
|
2185 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0061 - accuracy: 1.0000 - val_loss: 1.8991 - val_accuracy: 0.7222\n", |
|
|
2186 |
"Epoch 750/1000\n", |
|
|
2187 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.0059 - accuracy: 1.0000 - val_loss: 1.8385 - val_accuracy: 0.7222\n", |
|
|
2188 |
"Epoch 751/1000\n", |
|
|
2189 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0065 - accuracy: 1.0000 - val_loss: 1.7636 - val_accuracy: 0.7222\n", |
|
|
2190 |
"Epoch 752/1000\n", |
|
|
2191 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 1.6664 - val_accuracy: 0.7222\n", |
|
|
2192 |
"Epoch 753/1000\n", |
|
|
2193 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0071 - accuracy: 1.0000 - val_loss: 1.5649 - val_accuracy: 0.7222\n", |
|
|
2194 |
"Epoch 754/1000\n", |
|
|
2195 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 1.4417 - val_accuracy: 0.7222\n", |
|
|
2196 |
"Epoch 755/1000\n", |
|
|
2197 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 1.4134 - val_accuracy: 0.7222\n", |
|
|
2198 |
"Epoch 756/1000\n", |
|
|
2199 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.4586 - val_accuracy: 0.7222\n", |
|
|
2200 |
"Epoch 757/1000\n", |
|
|
2201 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0064 - accuracy: 1.0000 - val_loss: 1.5393 - val_accuracy: 0.7222\n", |
|
|
2202 |
"Epoch 758/1000\n", |
|
|
2203 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0072 - accuracy: 1.0000 - val_loss: 1.5542 - val_accuracy: 0.7222\n", |
|
|
2204 |
"Epoch 759/1000\n", |
|
|
2205 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.5475 - val_accuracy: 0.7222\n", |
|
|
2206 |
"Epoch 760/1000\n", |
|
|
2207 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.5504 - val_accuracy: 0.7222\n", |
|
|
2208 |
"Epoch 761/1000\n", |
|
|
2209 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.5716 - val_accuracy: 0.7222\n", |
|
|
2210 |
"Epoch 762/1000\n", |
|
|
2211 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.6020 - val_accuracy: 0.7222\n", |
|
|
2212 |
"Epoch 763/1000\n", |
|
|
2213 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0032 - accuracy: 1.0000 - val_loss: 1.6273 - val_accuracy: 0.7222\n", |
|
|
2214 |
"Epoch 764/1000\n", |
|
|
2215 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.6089 - val_accuracy: 0.7222\n", |
|
|
2216 |
"Epoch 765/1000\n", |
|
|
2217 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.6089 - val_accuracy: 0.7222\n", |
|
|
2218 |
"Epoch 766/1000\n", |
|
|
2219 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.6218 - val_accuracy: 0.7222\n", |
|
|
2220 |
"Epoch 767/1000\n", |
|
|
2221 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.6124 - val_accuracy: 0.7222\n", |
|
|
2222 |
"Epoch 768/1000\n", |
|
|
2223 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.6221 - val_accuracy: 0.7222\n", |
|
|
2224 |
"Epoch 769/1000\n", |
|
|
2225 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.6401 - val_accuracy: 0.7222\n", |
|
|
2226 |
"Epoch 770/1000\n", |
|
|
2227 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0035 - accuracy: 1.0000 - val_loss: 1.6647 - val_accuracy: 0.7222\n", |
|
|
2228 |
"Epoch 771/1000\n", |
|
|
2229 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.6620 - val_accuracy: 0.7222\n", |
|
|
2230 |
"Epoch 772/1000\n" |
|
|
2231 |
] |
|
|
2232 |
}, |
|
|
2233 |
{ |
|
|
2234 |
"name": "stdout", |
|
|
2235 |
"output_type": "stream", |
|
|
2236 |
"text": [ |
|
|
2237 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.6417 - val_accuracy: 0.7222\n", |
|
|
2238 |
"Epoch 773/1000\n", |
|
|
2239 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.6230 - val_accuracy: 0.7222\n", |
|
|
2240 |
"Epoch 774/1000\n", |
|
|
2241 |
"42/42 [==============================] - 0s 506us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.5892 - val_accuracy: 0.7222\n", |
|
|
2242 |
"Epoch 775/1000\n", |
|
|
2243 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0080 - accuracy: 1.0000 - val_loss: 1.6609 - val_accuracy: 0.7222\n", |
|
|
2244 |
"Epoch 776/1000\n", |
|
|
2245 |
"42/42 [==============================] - 0s 489us/sample - loss: 0.0055 - accuracy: 1.0000 - val_loss: 1.7973 - val_accuracy: 0.7222\n", |
|
|
2246 |
"Epoch 777/1000\n", |
|
|
2247 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.8991 - val_accuracy: 0.7222\n", |
|
|
2248 |
"Epoch 778/1000\n", |
|
|
2249 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 1.9974 - val_accuracy: 0.7222\n", |
|
|
2250 |
"Epoch 779/1000\n", |
|
|
2251 |
"42/42 [==============================] - 0s 493us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.0746 - val_accuracy: 0.7222\n", |
|
|
2252 |
"Epoch 780/1000\n", |
|
|
2253 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 2.0393 - val_accuracy: 0.7222\n", |
|
|
2254 |
"Epoch 781/1000\n", |
|
|
2255 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0057 - accuracy: 1.0000 - val_loss: 1.9537 - val_accuracy: 0.7222\n", |
|
|
2256 |
"Epoch 782/1000\n", |
|
|
2257 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.8136 - val_accuracy: 0.7222\n", |
|
|
2258 |
"Epoch 783/1000\n", |
|
|
2259 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.7148 - val_accuracy: 0.7222\n", |
|
|
2260 |
"Epoch 784/1000\n", |
|
|
2261 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.6582 - val_accuracy: 0.7222\n", |
|
|
2262 |
"Epoch 785/1000\n", |
|
|
2263 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 1.6206 - val_accuracy: 0.7222\n", |
|
|
2264 |
"Epoch 786/1000\n", |
|
|
2265 |
"42/42 [==============================] - 0s 472us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.6107 - val_accuracy: 0.7222\n", |
|
|
2266 |
"Epoch 787/1000\n", |
|
|
2267 |
"42/42 [==============================] - 0s 529us/sample - loss: 0.0133 - accuracy: 1.0000 - val_loss: 1.7969 - val_accuracy: 0.7222\n", |
|
|
2268 |
"Epoch 788/1000\n", |
|
|
2269 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 2.0288 - val_accuracy: 0.7222\n", |
|
|
2270 |
"Epoch 789/1000\n", |
|
|
2271 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 2.0980 - val_accuracy: 0.7222\n", |
|
|
2272 |
"Epoch 790/1000\n", |
|
|
2273 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 2.0019 - val_accuracy: 0.6667\n", |
|
|
2274 |
"Epoch 791/1000\n", |
|
|
2275 |
"42/42 [==============================] - 0s 515us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.8641 - val_accuracy: 0.7222\n", |
|
|
2276 |
"Epoch 792/1000\n", |
|
|
2277 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0296 - accuracy: 0.9762 - val_loss: 1.4046 - val_accuracy: 0.7222\n", |
|
|
2278 |
"Epoch 793/1000\n", |
|
|
2279 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 1.2419 - val_accuracy: 0.7222\n", |
|
|
2280 |
"Epoch 794/1000\n", |
|
|
2281 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0107 - accuracy: 1.0000 - val_loss: 1.2941 - val_accuracy: 0.7222\n", |
|
|
2282 |
"Epoch 795/1000\n", |
|
|
2283 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0070 - accuracy: 1.0000 - val_loss: 1.4694 - val_accuracy: 0.7222\n", |
|
|
2284 |
"Epoch 796/1000\n", |
|
|
2285 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0093 - accuracy: 1.0000 - val_loss: 1.6420 - val_accuracy: 0.7222\n", |
|
|
2286 |
"Epoch 797/1000\n", |
|
|
2287 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0043 - accuracy: 1.0000 - val_loss: 1.7633 - val_accuracy: 0.7222\n", |
|
|
2288 |
"Epoch 798/1000\n", |
|
|
2289 |
"42/42 [==============================] - 0s 531us/sample - loss: 0.0052 - accuracy: 1.0000 - val_loss: 1.8632 - val_accuracy: 0.7222\n", |
|
|
2290 |
"Epoch 799/1000\n", |
|
|
2291 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.9822 - val_accuracy: 0.7222\n", |
|
|
2292 |
"Epoch 800/1000\n", |
|
|
2293 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0032 - accuracy: 1.0000 - val_loss: 2.0332 - val_accuracy: 0.7222\n", |
|
|
2294 |
"Epoch 801/1000\n", |
|
|
2295 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 2.0692 - val_accuracy: 0.7222\n", |
|
|
2296 |
"Epoch 802/1000\n", |
|
|
2297 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0132 - accuracy: 1.0000 - val_loss: 1.9239 - val_accuracy: 0.7222\n", |
|
|
2298 |
"Epoch 803/1000\n", |
|
|
2299 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.7568 - val_accuracy: 0.7222\n", |
|
|
2300 |
"Epoch 804/1000\n", |
|
|
2301 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.6659 - val_accuracy: 0.7778\n", |
|
|
2302 |
"Epoch 805/1000\n", |
|
|
2303 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.6020 - val_accuracy: 0.7778\n", |
|
|
2304 |
"Epoch 806/1000\n", |
|
|
2305 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.5679 - val_accuracy: 0.7778\n", |
|
|
2306 |
"Epoch 807/1000\n", |
|
|
2307 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.5622 - val_accuracy: 0.7778\n", |
|
|
2308 |
"Epoch 808/1000\n", |
|
|
2309 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0040 - accuracy: 1.0000 - val_loss: 1.5808 - val_accuracy: 0.7222\n", |
|
|
2310 |
"Epoch 809/1000\n", |
|
|
2311 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.5941 - val_accuracy: 0.7222\n", |
|
|
2312 |
"Epoch 810/1000\n", |
|
|
2313 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.6079 - val_accuracy: 0.7222\n", |
|
|
2314 |
"Epoch 811/1000\n", |
|
|
2315 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.5777 - val_accuracy: 0.7222\n", |
|
|
2316 |
"Epoch 812/1000\n", |
|
|
2317 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0036 - accuracy: 1.0000 - val_loss: 1.5791 - val_accuracy: 0.7222\n", |
|
|
2318 |
"Epoch 813/1000\n", |
|
|
2319 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.6995 - val_accuracy: 0.7222\n", |
|
|
2320 |
"Epoch 814/1000\n", |
|
|
2321 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.8933 - val_accuracy: 0.7222\n", |
|
|
2322 |
"Epoch 815/1000\n", |
|
|
2323 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0173 - accuracy: 1.0000 - val_loss: 1.9038 - val_accuracy: 0.7222\n", |
|
|
2324 |
"Epoch 816/1000\n", |
|
|
2325 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0048 - accuracy: 1.0000 - val_loss: 1.6785 - val_accuracy: 0.7222\n", |
|
|
2326 |
"Epoch 817/1000\n", |
|
|
2327 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0099 - accuracy: 1.0000 - val_loss: 1.5958 - val_accuracy: 0.7222\n", |
|
|
2328 |
"Epoch 818/1000\n", |
|
|
2329 |
"42/42 [==============================] - 0s 490us/sample - loss: 0.0036 - accuracy: 1.0000 - val_loss: 1.6455 - val_accuracy: 0.7222\n", |
|
|
2330 |
"Epoch 819/1000\n", |
|
|
2331 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0041 - accuracy: 1.0000 - val_loss: 1.7055 - val_accuracy: 0.7222\n", |
|
|
2332 |
"Epoch 820/1000\n", |
|
|
2333 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0062 - accuracy: 1.0000 - val_loss: 1.7650 - val_accuracy: 0.7222\n", |
|
|
2334 |
"Epoch 821/1000\n", |
|
|
2335 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0079 - accuracy: 1.0000 - val_loss: 1.7693 - val_accuracy: 0.7222\n", |
|
|
2336 |
"Epoch 822/1000\n", |
|
|
2337 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0048 - accuracy: 1.0000 - val_loss: 1.7417 - val_accuracy: 0.7222\n", |
|
|
2338 |
"Epoch 823/1000\n", |
|
|
2339 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.7779 - val_accuracy: 0.7222\n", |
|
|
2340 |
"Epoch 824/1000\n", |
|
|
2341 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0061 - accuracy: 1.0000 - val_loss: 1.8579 - val_accuracy: 0.7222\n", |
|
|
2342 |
"Epoch 825/1000\n", |
|
|
2343 |
"42/42 [==============================] - 0s 500us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.9727 - val_accuracy: 0.7222\n", |
|
|
2344 |
"Epoch 826/1000\n", |
|
|
2345 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.0513 - val_accuracy: 0.7222\n", |
|
|
2346 |
"Epoch 827/1000\n" |
|
|
2347 |
] |
|
|
2348 |
}, |
|
|
2349 |
{ |
|
|
2350 |
"name": "stdout", |
|
|
2351 |
"output_type": "stream", |
|
|
2352 |
"text": [ |
|
|
2353 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 2.0729 - val_accuracy: 0.7222\n", |
|
|
2354 |
"Epoch 828/1000\n", |
|
|
2355 |
"42/42 [==============================] - 0s 480us/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 2.0277 - val_accuracy: 0.7222\n", |
|
|
2356 |
"Epoch 829/1000\n", |
|
|
2357 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.9477 - val_accuracy: 0.7222\n", |
|
|
2358 |
"Epoch 830/1000\n", |
|
|
2359 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.8894 - val_accuracy: 0.7222\n", |
|
|
2360 |
"Epoch 831/1000\n", |
|
|
2361 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 1.8470 - val_accuracy: 0.7222\n", |
|
|
2362 |
"Epoch 832/1000\n", |
|
|
2363 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8004 - val_accuracy: 0.7222\n", |
|
|
2364 |
"Epoch 833/1000\n", |
|
|
2365 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.7386 - val_accuracy: 0.7222\n", |
|
|
2366 |
"Epoch 834/1000\n", |
|
|
2367 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.6964 - val_accuracy: 0.7222\n", |
|
|
2368 |
"Epoch 835/1000\n", |
|
|
2369 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 1.6643 - val_accuracy: 0.7222\n", |
|
|
2370 |
"Epoch 836/1000\n", |
|
|
2371 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.6343 - val_accuracy: 0.7222\n", |
|
|
2372 |
"Epoch 837/1000\n", |
|
|
2373 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 1.6063 - val_accuracy: 0.7222\n", |
|
|
2374 |
"Epoch 838/1000\n", |
|
|
2375 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0049 - accuracy: 1.0000 - val_loss: 1.6481 - val_accuracy: 0.7222\n", |
|
|
2376 |
"Epoch 839/1000\n", |
|
|
2377 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.6906 - val_accuracy: 0.7222\n", |
|
|
2378 |
"Epoch 840/1000\n", |
|
|
2379 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.7241 - val_accuracy: 0.7222\n", |
|
|
2380 |
"Epoch 841/1000\n", |
|
|
2381 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0062 - accuracy: 1.0000 - val_loss: 1.7165 - val_accuracy: 0.7222\n", |
|
|
2382 |
"Epoch 842/1000\n", |
|
|
2383 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0056 - accuracy: 1.0000 - val_loss: 1.6952 - val_accuracy: 0.7222\n", |
|
|
2384 |
"Epoch 843/1000\n", |
|
|
2385 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 1.6988 - val_accuracy: 0.7222\n", |
|
|
2386 |
"Epoch 844/1000\n", |
|
|
2387 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0067 - accuracy: 1.0000 - val_loss: 1.7188 - val_accuracy: 0.7222\n", |
|
|
2388 |
"Epoch 845/1000\n", |
|
|
2389 |
"42/42 [==============================] - 0s 664us/sample - loss: 8.4670e-04 - accuracy: 1.0000 - val_loss: 1.7176 - val_accuracy: 0.7222\n", |
|
|
2390 |
"Epoch 846/1000\n", |
|
|
2391 |
"42/42 [==============================] - 0s 594us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.7193 - val_accuracy: 0.7222\n", |
|
|
2392 |
"Epoch 847/1000\n", |
|
|
2393 |
"42/42 [==============================] - 0s 486us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.7551 - val_accuracy: 0.7222\n", |
|
|
2394 |
"Epoch 848/1000\n", |
|
|
2395 |
"42/42 [==============================] - 0s 521us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.7652 - val_accuracy: 0.7222\n", |
|
|
2396 |
"Epoch 849/1000\n", |
|
|
2397 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.7245 - val_accuracy: 0.7222\n", |
|
|
2398 |
"Epoch 850/1000\n", |
|
|
2399 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.6886 - val_accuracy: 0.7222\n", |
|
|
2400 |
"Epoch 851/1000\n", |
|
|
2401 |
"42/42 [==============================] - 0s 520us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 1.6777 - val_accuracy: 0.7222\n", |
|
|
2402 |
"Epoch 852/1000\n", |
|
|
2403 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.6958 - val_accuracy: 0.7222\n", |
|
|
2404 |
"Epoch 853/1000\n", |
|
|
2405 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.6987 - val_accuracy: 0.7222\n", |
|
|
2406 |
"Epoch 854/1000\n", |
|
|
2407 |
"42/42 [==============================] - 0s 512us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 1.6896 - val_accuracy: 0.7222\n", |
|
|
2408 |
"Epoch 855/1000\n", |
|
|
2409 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.6687 - val_accuracy: 0.7222\n", |
|
|
2410 |
"Epoch 856/1000\n", |
|
|
2411 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.6256 - val_accuracy: 0.7222\n", |
|
|
2412 |
"Epoch 857/1000\n", |
|
|
2413 |
"42/42 [==============================] - 0s 508us/sample - loss: 0.0034 - accuracy: 1.0000 - val_loss: 1.5813 - val_accuracy: 0.7222\n", |
|
|
2414 |
"Epoch 858/1000\n", |
|
|
2415 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 1.5619 - val_accuracy: 0.7222\n", |
|
|
2416 |
"Epoch 859/1000\n", |
|
|
2417 |
"42/42 [==============================] - 0s 474us/sample - loss: 8.2880e-04 - accuracy: 1.0000 - val_loss: 1.5561 - val_accuracy: 0.7222\n", |
|
|
2418 |
"Epoch 860/1000\n", |
|
|
2419 |
"42/42 [==============================] - 0s 522us/sample - loss: 9.0853e-04 - accuracy: 1.0000 - val_loss: 1.5484 - val_accuracy: 0.7222\n", |
|
|
2420 |
"Epoch 861/1000\n", |
|
|
2421 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 1.5889 - val_accuracy: 0.7222\n", |
|
|
2422 |
"Epoch 862/1000\n", |
|
|
2423 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0114 - accuracy: 1.0000 - val_loss: 1.7950 - val_accuracy: 0.7222\n", |
|
|
2424 |
"Epoch 863/1000\n", |
|
|
2425 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 1.9200 - val_accuracy: 0.7222\n", |
|
|
2426 |
"Epoch 864/1000\n", |
|
|
2427 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.9942 - val_accuracy: 0.7222\n", |
|
|
2428 |
"Epoch 865/1000\n", |
|
|
2429 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.9944 - val_accuracy: 0.7222\n", |
|
|
2430 |
"Epoch 866/1000\n", |
|
|
2431 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.9586 - val_accuracy: 0.7222\n", |
|
|
2432 |
"Epoch 867/1000\n", |
|
|
2433 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0038 - accuracy: 1.0000 - val_loss: 1.8458 - val_accuracy: 0.7222\n", |
|
|
2434 |
"Epoch 868/1000\n", |
|
|
2435 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.7394 - val_accuracy: 0.7222\n", |
|
|
2436 |
"Epoch 869/1000\n", |
|
|
2437 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0013 - accuracy: 1.0000 - val_loss: 1.6655 - val_accuracy: 0.7222\n", |
|
|
2438 |
"Epoch 870/1000\n", |
|
|
2439 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.6701 - val_accuracy: 0.7222\n", |
|
|
2440 |
"Epoch 871/1000\n", |
|
|
2441 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.7097 - val_accuracy: 0.7222\n", |
|
|
2442 |
"Epoch 872/1000\n", |
|
|
2443 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 1.7807 - val_accuracy: 0.7222\n", |
|
|
2444 |
"Epoch 873/1000\n", |
|
|
2445 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0053 - accuracy: 1.0000 - val_loss: 1.8571 - val_accuracy: 0.7222\n", |
|
|
2446 |
"Epoch 874/1000\n", |
|
|
2447 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.9032 - val_accuracy: 0.7222\n", |
|
|
2448 |
"Epoch 875/1000\n", |
|
|
2449 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.9149 - val_accuracy: 0.7222\n", |
|
|
2450 |
"Epoch 876/1000\n", |
|
|
2451 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.9030 - val_accuracy: 0.7222\n", |
|
|
2452 |
"Epoch 877/1000\n", |
|
|
2453 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0010 - accuracy: 1.0000 - val_loss: 1.8831 - val_accuracy: 0.7222\n", |
|
|
2454 |
"Epoch 878/1000\n", |
|
|
2455 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.8398 - val_accuracy: 0.7222\n", |
|
|
2456 |
"Epoch 879/1000\n", |
|
|
2457 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.7969 - val_accuracy: 0.7222\n", |
|
|
2458 |
"Epoch 880/1000\n", |
|
|
2459 |
"42/42 [==============================] - 0s 510us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.7489 - val_accuracy: 0.7222\n", |
|
|
2460 |
"Epoch 881/1000\n", |
|
|
2461 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.7831 - val_accuracy: 0.7222\n", |
|
|
2462 |
"Epoch 882/1000\n" |
|
|
2463 |
] |
|
|
2464 |
}, |
|
|
2465 |
{ |
|
|
2466 |
"name": "stdout", |
|
|
2467 |
"output_type": "stream", |
|
|
2468 |
"text": [ |
|
|
2469 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.8353 - val_accuracy: 0.7222\n", |
|
|
2470 |
"Epoch 883/1000\n", |
|
|
2471 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0013 - accuracy: 1.0000 - val_loss: 1.8907 - val_accuracy: 0.7222\n", |
|
|
2472 |
"Epoch 884/1000\n", |
|
|
2473 |
"42/42 [==============================] - 0s 498us/sample - loss: 8.7721e-04 - accuracy: 1.0000 - val_loss: 1.9235 - val_accuracy: 0.7222\n", |
|
|
2474 |
"Epoch 885/1000\n", |
|
|
2475 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.9460 - val_accuracy: 0.7222\n", |
|
|
2476 |
"Epoch 886/1000\n", |
|
|
2477 |
"42/42 [==============================] - 0s 498us/sample - loss: 5.1471e-04 - accuracy: 1.0000 - val_loss: 1.9587 - val_accuracy: 0.7222\n", |
|
|
2478 |
"Epoch 887/1000\n", |
|
|
2479 |
"42/42 [==============================] - 0s 498us/sample - loss: 7.0366e-04 - accuracy: 1.0000 - val_loss: 1.9629 - val_accuracy: 0.7222\n", |
|
|
2480 |
"Epoch 888/1000\n", |
|
|
2481 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0056 - accuracy: 1.0000 - val_loss: 1.8376 - val_accuracy: 0.7222\n", |
|
|
2482 |
"Epoch 889/1000\n", |
|
|
2483 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.6898 - val_accuracy: 0.7222\n", |
|
|
2484 |
"Epoch 890/1000\n", |
|
|
2485 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.5948 - val_accuracy: 0.7222\n", |
|
|
2486 |
"Epoch 891/1000\n", |
|
|
2487 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0050 - accuracy: 1.0000 - val_loss: 1.4739 - val_accuracy: 0.7222\n", |
|
|
2488 |
"Epoch 892/1000\n", |
|
|
2489 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.3767 - val_accuracy: 0.7222\n", |
|
|
2490 |
"Epoch 893/1000\n", |
|
|
2491 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.3616 - val_accuracy: 0.7222\n", |
|
|
2492 |
"Epoch 894/1000\n", |
|
|
2493 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.4446 - val_accuracy: 0.7222\n", |
|
|
2494 |
"Epoch 895/1000\n", |
|
|
2495 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.5106 - val_accuracy: 0.7222\n", |
|
|
2496 |
"Epoch 896/1000\n", |
|
|
2497 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0043 - accuracy: 1.0000 - val_loss: 1.6266 - val_accuracy: 0.7222\n", |
|
|
2498 |
"Epoch 897/1000\n", |
|
|
2499 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0171 - accuracy: 1.0000 - val_loss: 1.5026 - val_accuracy: 0.7222\n", |
|
|
2500 |
"Epoch 898/1000\n", |
|
|
2501 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 1.3945 - val_accuracy: 0.7222\n", |
|
|
2502 |
"Epoch 899/1000\n", |
|
|
2503 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.3823 - val_accuracy: 0.7222\n", |
|
|
2504 |
"Epoch 900/1000\n", |
|
|
2505 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.3913 - val_accuracy: 0.7222\n", |
|
|
2506 |
"Epoch 901/1000\n", |
|
|
2507 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.4346 - val_accuracy: 0.7222\n", |
|
|
2508 |
"Epoch 902/1000\n", |
|
|
2509 |
"42/42 [==============================] - 0s 520us/sample - loss: 0.0058 - accuracy: 1.0000 - val_loss: 1.6181 - val_accuracy: 0.7222\n", |
|
|
2510 |
"Epoch 903/1000\n", |
|
|
2511 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0038 - accuracy: 1.0000 - val_loss: 1.8476 - val_accuracy: 0.7222\n", |
|
|
2512 |
"Epoch 904/1000\n", |
|
|
2513 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 2.0002 - val_accuracy: 0.7222\n", |
|
|
2514 |
"Epoch 905/1000\n", |
|
|
2515 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 2.0873 - val_accuracy: 0.7222\n", |
|
|
2516 |
"Epoch 906/1000\n", |
|
|
2517 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0069 - accuracy: 1.0000 - val_loss: 2.0803 - val_accuracy: 0.7222\n", |
|
|
2518 |
"Epoch 907/1000\n", |
|
|
2519 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 2.0289 - val_accuracy: 0.7222\n", |
|
|
2520 |
"Epoch 908/1000\n", |
|
|
2521 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.9915 - val_accuracy: 0.7222\n", |
|
|
2522 |
"Epoch 909/1000\n", |
|
|
2523 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.9600 - val_accuracy: 0.7222\n", |
|
|
2524 |
"Epoch 910/1000\n", |
|
|
2525 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.9298 - val_accuracy: 0.6667\n", |
|
|
2526 |
"Epoch 911/1000\n", |
|
|
2527 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.8873 - val_accuracy: 0.6667\n", |
|
|
2528 |
"Epoch 912/1000\n", |
|
|
2529 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0073 - accuracy: 1.0000 - val_loss: 1.8808 - val_accuracy: 0.6667\n", |
|
|
2530 |
"Epoch 913/1000\n", |
|
|
2531 |
"42/42 [==============================] - 0s 499us/sample - loss: 8.6095e-04 - accuracy: 1.0000 - val_loss: 2.0206 - val_accuracy: 0.7222\n", |
|
|
2532 |
"Epoch 914/1000\n", |
|
|
2533 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.0802 - val_accuracy: 0.7222\n", |
|
|
2534 |
"Epoch 915/1000\n", |
|
|
2535 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0025 - accuracy: 1.0000 - val_loss: 2.1066 - val_accuracy: 0.7222\n", |
|
|
2536 |
"Epoch 916/1000\n", |
|
|
2537 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 2.0763 - val_accuracy: 0.7222\n", |
|
|
2538 |
"Epoch 917/1000\n", |
|
|
2539 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 2.0377 - val_accuracy: 0.7222\n", |
|
|
2540 |
"Epoch 918/1000\n", |
|
|
2541 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0089 - accuracy: 1.0000 - val_loss: 1.8408 - val_accuracy: 0.7222\n", |
|
|
2542 |
"Epoch 919/1000\n", |
|
|
2543 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0093 - accuracy: 1.0000 - val_loss: 1.6287 - val_accuracy: 0.7222\n", |
|
|
2544 |
"Epoch 920/1000\n", |
|
|
2545 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.5185 - val_accuracy: 0.7222\n", |
|
|
2546 |
"Epoch 921/1000\n", |
|
|
2547 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0061 - accuracy: 1.0000 - val_loss: 1.4857 - val_accuracy: 0.7222\n", |
|
|
2548 |
"Epoch 922/1000\n", |
|
|
2549 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0026 - accuracy: 1.0000 - val_loss: 1.4904 - val_accuracy: 0.7222\n", |
|
|
2550 |
"Epoch 923/1000\n", |
|
|
2551 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.5265 - val_accuracy: 0.7222\n", |
|
|
2552 |
"Epoch 924/1000\n", |
|
|
2553 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.5832 - val_accuracy: 0.7222\n", |
|
|
2554 |
"Epoch 925/1000\n", |
|
|
2555 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.6536 - val_accuracy: 0.7222\n", |
|
|
2556 |
"Epoch 926/1000\n", |
|
|
2557 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 1.7298 - val_accuracy: 0.7222\n", |
|
|
2558 |
"Epoch 927/1000\n", |
|
|
2559 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 1.7878 - val_accuracy: 0.7222\n", |
|
|
2560 |
"Epoch 928/1000\n", |
|
|
2561 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0088 - accuracy: 1.0000 - val_loss: 1.7381 - val_accuracy: 0.7222\n", |
|
|
2562 |
"Epoch 929/1000\n", |
|
|
2563 |
"42/42 [==============================] - 0s 509us/sample - loss: 0.0010 - accuracy: 1.0000 - val_loss: 1.6313 - val_accuracy: 0.7222\n", |
|
|
2564 |
"Epoch 930/1000\n", |
|
|
2565 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.6088 - val_accuracy: 0.7222\n", |
|
|
2566 |
"Epoch 931/1000\n", |
|
|
2567 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0096 - accuracy: 1.0000 - val_loss: 1.6037 - val_accuracy: 0.7222\n", |
|
|
2568 |
"Epoch 932/1000\n", |
|
|
2569 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.6331 - val_accuracy: 0.7222\n", |
|
|
2570 |
"Epoch 933/1000\n", |
|
|
2571 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0040 - accuracy: 1.0000 - val_loss: 1.7117 - val_accuracy: 0.7222\n", |
|
|
2572 |
"Epoch 934/1000\n", |
|
|
2573 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.8003 - val_accuracy: 0.7222\n", |
|
|
2574 |
"Epoch 935/1000\n", |
|
|
2575 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.8709 - val_accuracy: 0.7222\n", |
|
|
2576 |
"Epoch 936/1000\n", |
|
|
2577 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 1.9062 - val_accuracy: 0.7222\n", |
|
|
2578 |
"Epoch 937/1000\n" |
|
|
2579 |
] |
|
|
2580 |
}, |
|
|
2581 |
{ |
|
|
2582 |
"name": "stdout", |
|
|
2583 |
"output_type": "stream", |
|
|
2584 |
"text": [ |
|
|
2585 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 1.8747 - val_accuracy: 0.7222\n", |
|
|
2586 |
"Epoch 938/1000\n", |
|
|
2587 |
"42/42 [==============================] - 0s 518us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.8280 - val_accuracy: 0.7222\n", |
|
|
2588 |
"Epoch 939/1000\n", |
|
|
2589 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0037 - accuracy: 1.0000 - val_loss: 1.7857 - val_accuracy: 0.7222\n", |
|
|
2590 |
"Epoch 940/1000\n", |
|
|
2591 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.7648 - val_accuracy: 0.7222\n", |
|
|
2592 |
"Epoch 941/1000\n", |
|
|
2593 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.7342 - val_accuracy: 0.7222\n", |
|
|
2594 |
"Epoch 942/1000\n", |
|
|
2595 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.6969 - val_accuracy: 0.7222\n", |
|
|
2596 |
"Epoch 943/1000\n", |
|
|
2597 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.6983 - val_accuracy: 0.7222\n", |
|
|
2598 |
"Epoch 944/1000\n", |
|
|
2599 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0030 - accuracy: 1.0000 - val_loss: 1.7351 - val_accuracy: 0.7222\n", |
|
|
2600 |
"Epoch 945/1000\n", |
|
|
2601 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0075 - accuracy: 1.0000 - val_loss: 1.9439 - val_accuracy: 0.7222\n", |
|
|
2602 |
"Epoch 946/1000\n", |
|
|
2603 |
"42/42 [==============================] - 0s 499us/sample - loss: 6.5963e-04 - accuracy: 1.0000 - val_loss: 2.1180 - val_accuracy: 0.7222\n", |
|
|
2604 |
"Epoch 947/1000\n", |
|
|
2605 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0019 - accuracy: 1.0000 - val_loss: 2.2464 - val_accuracy: 0.7222\n", |
|
|
2606 |
"Epoch 948/1000\n", |
|
|
2607 |
"42/42 [==============================] - 0s 505us/sample - loss: 0.0028 - accuracy: 1.0000 - val_loss: 2.2884 - val_accuracy: 0.7222\n", |
|
|
2608 |
"Epoch 949/1000\n", |
|
|
2609 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0084 - accuracy: 1.0000 - val_loss: 2.0690 - val_accuracy: 0.7222\n", |
|
|
2610 |
"Epoch 950/1000\n", |
|
|
2611 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.8715 - val_accuracy: 0.7222\n", |
|
|
2612 |
"Epoch 951/1000\n", |
|
|
2613 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0085 - accuracy: 1.0000 - val_loss: 1.7777 - val_accuracy: 0.7222\n", |
|
|
2614 |
"Epoch 952/1000\n", |
|
|
2615 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.7423 - val_accuracy: 0.7222\n", |
|
|
2616 |
"Epoch 953/1000\n", |
|
|
2617 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.6972 - val_accuracy: 0.7222\n", |
|
|
2618 |
"Epoch 954/1000\n", |
|
|
2619 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0024 - accuracy: 1.0000 - val_loss: 1.6800 - val_accuracy: 0.7222\n", |
|
|
2620 |
"Epoch 955/1000\n", |
|
|
2621 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.6566 - val_accuracy: 0.7222\n", |
|
|
2622 |
"Epoch 956/1000\n", |
|
|
2623 |
"42/42 [==============================] - 0s 475us/sample - loss: 9.3389e-04 - accuracy: 1.0000 - val_loss: 1.6376 - val_accuracy: 0.7222\n", |
|
|
2624 |
"Epoch 957/1000\n", |
|
|
2625 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.6641 - val_accuracy: 0.7222\n", |
|
|
2626 |
"Epoch 958/1000\n", |
|
|
2627 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.6433 - val_accuracy: 0.7222\n", |
|
|
2628 |
"Epoch 959/1000\n", |
|
|
2629 |
"42/42 [==============================] - 0s 499us/sample - loss: 9.8323e-04 - accuracy: 1.0000 - val_loss: 1.6290 - val_accuracy: 0.7222\n", |
|
|
2630 |
"Epoch 960/1000\n", |
|
|
2631 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0036 - accuracy: 1.0000 - val_loss: 1.6498 - val_accuracy: 0.6667\n", |
|
|
2632 |
"Epoch 961/1000\n", |
|
|
2633 |
"42/42 [==============================] - 0s 494us/sample - loss: 0.0016 - accuracy: 1.0000 - val_loss: 1.7098 - val_accuracy: 0.7222\n", |
|
|
2634 |
"Epoch 962/1000\n", |
|
|
2635 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0060 - accuracy: 1.0000 - val_loss: 1.7833 - val_accuracy: 0.7222\n", |
|
|
2636 |
"Epoch 963/1000\n", |
|
|
2637 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.8445 - val_accuracy: 0.7222\n", |
|
|
2638 |
"Epoch 964/1000\n", |
|
|
2639 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.8803 - val_accuracy: 0.7222\n", |
|
|
2640 |
"Epoch 965/1000\n", |
|
|
2641 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.8904 - val_accuracy: 0.7222\n", |
|
|
2642 |
"Epoch 966/1000\n", |
|
|
2643 |
"42/42 [==============================] - 0s 546us/sample - loss: 0.0044 - accuracy: 1.0000 - val_loss: 1.9277 - val_accuracy: 0.7222\n", |
|
|
2644 |
"Epoch 967/1000\n", |
|
|
2645 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0066 - accuracy: 1.0000 - val_loss: 1.8407 - val_accuracy: 0.7222\n", |
|
|
2646 |
"Epoch 968/1000\n", |
|
|
2647 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.6127 - val_accuracy: 0.7222\n", |
|
|
2648 |
"Epoch 969/1000\n", |
|
|
2649 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0033 - accuracy: 1.0000 - val_loss: 1.4941 - val_accuracy: 0.7222\n", |
|
|
2650 |
"Epoch 970/1000\n", |
|
|
2651 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0032 - accuracy: 1.0000 - val_loss: 1.4258 - val_accuracy: 0.7222\n", |
|
|
2652 |
"Epoch 971/1000\n", |
|
|
2653 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.4174 - val_accuracy: 0.7222\n", |
|
|
2654 |
"Epoch 972/1000\n", |
|
|
2655 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0031 - accuracy: 1.0000 - val_loss: 1.4516 - val_accuracy: 0.7222\n", |
|
|
2656 |
"Epoch 973/1000\n", |
|
|
2657 |
"42/42 [==============================] - 0s 527us/sample - loss: 0.0010 - accuracy: 1.0000 - val_loss: 1.4905 - val_accuracy: 0.7222\n", |
|
|
2658 |
"Epoch 974/1000\n", |
|
|
2659 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0029 - accuracy: 1.0000 - val_loss: 1.5433 - val_accuracy: 0.7222\n", |
|
|
2660 |
"Epoch 975/1000\n", |
|
|
2661 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.5868 - val_accuracy: 0.7222\n", |
|
|
2662 |
"Epoch 976/1000\n", |
|
|
2663 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.6529 - val_accuracy: 0.7222\n", |
|
|
2664 |
"Epoch 977/1000\n", |
|
|
2665 |
"42/42 [==============================] - 0s 475us/sample - loss: 0.0052 - accuracy: 1.0000 - val_loss: 1.8051 - val_accuracy: 0.7222\n", |
|
|
2666 |
"Epoch 978/1000\n", |
|
|
2667 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0022 - accuracy: 1.0000 - val_loss: 1.9529 - val_accuracy: 0.7222\n", |
|
|
2668 |
"Epoch 979/1000\n", |
|
|
2669 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 2.0538 - val_accuracy: 0.7222\n", |
|
|
2670 |
"Epoch 980/1000\n", |
|
|
2671 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0018 - accuracy: 1.0000 - val_loss: 2.1170 - val_accuracy: 0.7222\n", |
|
|
2672 |
"Epoch 981/1000\n", |
|
|
2673 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0072 - accuracy: 1.0000 - val_loss: 2.0318 - val_accuracy: 0.7222\n", |
|
|
2674 |
"Epoch 982/1000\n", |
|
|
2675 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.9021 - val_accuracy: 0.7222\n", |
|
|
2676 |
"Epoch 983/1000\n", |
|
|
2677 |
"42/42 [==============================] - 0s 492us/sample - loss: 0.0014 - accuracy: 1.0000 - val_loss: 1.7965 - val_accuracy: 0.7222\n", |
|
|
2678 |
"Epoch 984/1000\n", |
|
|
2679 |
"42/42 [==============================] - 0s 522us/sample - loss: 8.2441e-04 - accuracy: 1.0000 - val_loss: 1.7237 - val_accuracy: 0.7222\n", |
|
|
2680 |
"Epoch 985/1000\n", |
|
|
2681 |
"42/42 [==============================] - 0s 523us/sample - loss: 0.0011 - accuracy: 1.0000 - val_loss: 1.6741 - val_accuracy: 0.7222\n", |
|
|
2682 |
"Epoch 986/1000\n", |
|
|
2683 |
"42/42 [==============================] - 0s 498us/sample - loss: 0.0027 - accuracy: 1.0000 - val_loss: 1.6539 - val_accuracy: 0.7222\n", |
|
|
2684 |
"Epoch 987/1000\n", |
|
|
2685 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.6824 - val_accuracy: 0.7222\n", |
|
|
2686 |
"Epoch 988/1000\n", |
|
|
2687 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.7144 - val_accuracy: 0.7222\n", |
|
|
2688 |
"Epoch 989/1000\n", |
|
|
2689 |
"42/42 [==============================] - 0s 495us/sample - loss: 0.0017 - accuracy: 1.0000 - val_loss: 1.7497 - val_accuracy: 0.7222\n", |
|
|
2690 |
"Epoch 990/1000\n", |
|
|
2691 |
"42/42 [==============================] - 0s 499us/sample - loss: 7.9925e-04 - accuracy: 1.0000 - val_loss: 1.7817 - val_accuracy: 0.7222\n", |
|
|
2692 |
"Epoch 991/1000\n", |
|
|
2693 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0013 - accuracy: 1.0000 - val_loss: 1.7986 - val_accuracy: 0.7222\n", |
|
|
2694 |
"Epoch 992/1000\n" |
|
|
2695 |
] |
|
|
2696 |
}, |
|
|
2697 |
{ |
|
|
2698 |
"name": "stdout", |
|
|
2699 |
"output_type": "stream", |
|
|
2700 |
"text": [ |
|
|
2701 |
"42/42 [==============================] - 0s 496us/sample - loss: 0.0054 - accuracy: 1.0000 - val_loss: 1.8045 - val_accuracy: 0.7222\n", |
|
|
2702 |
"Epoch 993/1000\n", |
|
|
2703 |
"42/42 [==============================] - 0s 522us/sample - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.8098 - val_accuracy: 0.7222\n", |
|
|
2704 |
"Epoch 994/1000\n", |
|
|
2705 |
"42/42 [==============================] - 0s 491us/sample - loss: 0.0039 - accuracy: 1.0000 - val_loss: 1.8059 - val_accuracy: 0.7222\n", |
|
|
2706 |
"Epoch 995/1000\n", |
|
|
2707 |
"42/42 [==============================] - 0s 499us/sample - loss: 4.3457e-04 - accuracy: 1.0000 - val_loss: 1.8010 - val_accuracy: 0.7222\n", |
|
|
2708 |
"Epoch 996/1000\n", |
|
|
2709 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0015 - accuracy: 1.0000 - val_loss: 1.7816 - val_accuracy: 0.7222\n", |
|
|
2710 |
"Epoch 997/1000\n", |
|
|
2711 |
"42/42 [==============================] - 0s 500us/sample - loss: 8.7628e-04 - accuracy: 1.0000 - val_loss: 1.7382 - val_accuracy: 0.7222\n", |
|
|
2712 |
"Epoch 998/1000\n", |
|
|
2713 |
"42/42 [==============================] - 0s 474us/sample - loss: 0.0051 - accuracy: 1.0000 - val_loss: 1.6954 - val_accuracy: 0.7222\n", |
|
|
2714 |
"Epoch 999/1000\n", |
|
|
2715 |
"42/42 [==============================] - 0s 499us/sample - loss: 0.0081 - accuracy: 1.0000 - val_loss: 1.6853 - val_accuracy: 0.7222\n", |
|
|
2716 |
"Epoch 1000/1000\n", |
|
|
2717 |
"42/42 [==============================] - 0s 522us/sample - loss: 9.9690e-04 - accuracy: 1.0000 - val_loss: 1.6930 - val_accuracy: 0.7222\n" |
|
|
2718 |
] |
|
|
2719 |
} |
|
|
2720 |
], |
|
|
2721 |
"source": [ |
|
|
2722 |
"cnnhistory=model.fit(x_traincnn, y_train, batch_size=16, epochs=1000, validation_data=(x_testcnn, y_test))" |
|
|
2723 |
] |
|
|
2724 |
}, |
|
|
2725 |
{ |
|
|
2726 |
"cell_type": "code", |
|
|
2727 |
"execution_count": 63, |
|
|
2728 |
"metadata": {}, |
|
|
2729 |
"outputs": [ |
|
|
2730 |
{ |
|
|
2731 |
"data": { |
|
|
2732 |
"image/png": 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\n", |
|
|
2733 |
"text/plain": [ |
|
|
2734 |
"<Figure size 432x288 with 1 Axes>" |
|
|
2735 |
] |
|
|
2736 |
}, |
|
|
2737 |
"metadata": { |
|
|
2738 |
"needs_background": "light" |
|
|
2739 |
}, |
|
|
2740 |
"output_type": "display_data" |
|
|
2741 |
} |
|
|
2742 |
], |
|
|
2743 |
"source": [ |
|
|
2744 |
"# Loss \n", |
|
|
2745 |
"plt.plot(cnnhistory.history['loss'])\n", |
|
|
2746 |
"plt.plot(cnnhistory.history['val_loss'])\n", |
|
|
2747 |
"plt.title('model loss')\n", |
|
|
2748 |
"plt.ylabel('loss')\n", |
|
|
2749 |
"plt.xlabel('epoch')\n", |
|
|
2750 |
"plt.legend(['train', 'test'], loc='upper left')\n", |
|
|
2751 |
"plt.show()" |
|
|
2752 |
] |
|
|
2753 |
}, |
|
|
2754 |
{ |
|
|
2755 |
"cell_type": "code", |
|
|
2756 |
"execution_count": 64, |
|
|
2757 |
"metadata": {}, |
|
|
2758 |
"outputs": [ |
|
|
2759 |
{ |
|
|
2760 |
"data": { |
|
|
2761 |
"image/png": 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\n", |
|
|
2762 |
"text/plain": [ |
|
|
2763 |
"<Figure size 432x288 with 1 Axes>" |
|
|
2764 |
] |
|
|
2765 |
}, |
|
|
2766 |
"metadata": { |
|
|
2767 |
"needs_background": "light" |
|
|
2768 |
}, |
|
|
2769 |
"output_type": "display_data" |
|
|
2770 |
} |
|
|
2771 |
], |
|
|
2772 |
"source": [ |
|
|
2773 |
"## This is for Accuracy\n", |
|
|
2774 |
"plt.plot(cnnhistory.history['accuracy'])\n", |
|
|
2775 |
"plt.plot(cnnhistory.history['val_accuracy'])\n", |
|
|
2776 |
"plt.title('model accuracy')\n", |
|
|
2777 |
"plt.ylabel('accuracy')\n", |
|
|
2778 |
"plt.xlabel('epoch')\n", |
|
|
2779 |
"plt.legend(['train', 'test'], loc='upper left')\n", |
|
|
2780 |
"plt.show()" |
|
|
2781 |
] |
|
|
2782 |
}, |
|
|
2783 |
{ |
|
|
2784 |
"cell_type": "code", |
|
|
2785 |
"execution_count": 65, |
|
|
2786 |
"metadata": {}, |
|
|
2787 |
"outputs": [], |
|
|
2788 |
"source": [ |
|
|
2789 |
"predictions = model.predict_classes(x_testcnn)" |
|
|
2790 |
] |
|
|
2791 |
}, |
|
|
2792 |
{ |
|
|
2793 |
"cell_type": "code", |
|
|
2794 |
"execution_count": 66, |
|
|
2795 |
"metadata": {}, |
|
|
2796 |
"outputs": [ |
|
|
2797 |
{ |
|
|
2798 |
"data": { |
|
|
2799 |
"text/plain": [ |
|
|
2800 |
"array([5, 3, 5, 5, 2, 5, 1, 3, 0, 7, 3, 1, 7, 3, 4, 1, 2, 3], dtype=int64)" |
|
|
2801 |
] |
|
|
2802 |
}, |
|
|
2803 |
"execution_count": 66, |
|
|
2804 |
"metadata": {}, |
|
|
2805 |
"output_type": "execute_result" |
|
|
2806 |
} |
|
|
2807 |
], |
|
|
2808 |
"source": [ |
|
|
2809 |
"predictions" |
|
|
2810 |
] |
|
|
2811 |
}, |
|
|
2812 |
{ |
|
|
2813 |
"cell_type": "code", |
|
|
2814 |
"execution_count": 67, |
|
|
2815 |
"metadata": {}, |
|
|
2816 |
"outputs": [ |
|
|
2817 |
{ |
|
|
2818 |
"data": { |
|
|
2819 |
"text/plain": [ |
|
|
2820 |
"array([5, 3, 5, 5, 2, 5, 1, 6, 0, 6, 7, 1, 7, 3, 7, 1, 7, 3])" |
|
|
2821 |
] |
|
|
2822 |
}, |
|
|
2823 |
"execution_count": 67, |
|
|
2824 |
"metadata": {}, |
|
|
2825 |
"output_type": "execute_result" |
|
|
2826 |
} |
|
|
2827 |
], |
|
|
2828 |
"source": [ |
|
|
2829 |
"y_test" |
|
|
2830 |
] |
|
|
2831 |
}, |
|
|
2832 |
{ |
|
|
2833 |
"cell_type": "code", |
|
|
2834 |
"execution_count": 68, |
|
|
2835 |
"metadata": {}, |
|
|
2836 |
"outputs": [], |
|
|
2837 |
"source": [ |
|
|
2838 |
"new_Ytest = y_test.astype(int)" |
|
|
2839 |
] |
|
|
2840 |
}, |
|
|
2841 |
{ |
|
|
2842 |
"cell_type": "code", |
|
|
2843 |
"execution_count": 69, |
|
|
2844 |
"metadata": {}, |
|
|
2845 |
"outputs": [ |
|
|
2846 |
{ |
|
|
2847 |
"data": { |
|
|
2848 |
"text/plain": [ |
|
|
2849 |
"array([5, 3, 5, 5, 2, 5, 1, 6, 0, 6, 7, 1, 7, 3, 7, 1, 7, 3])" |
|
|
2850 |
] |
|
|
2851 |
}, |
|
|
2852 |
"execution_count": 69, |
|
|
2853 |
"metadata": {}, |
|
|
2854 |
"output_type": "execute_result" |
|
|
2855 |
} |
|
|
2856 |
], |
|
|
2857 |
"source": [ |
|
|
2858 |
"new_Ytest" |
|
|
2859 |
] |
|
|
2860 |
}, |
|
|
2861 |
{ |
|
|
2862 |
"cell_type": "code", |
|
|
2863 |
"execution_count": 70, |
|
|
2864 |
"metadata": {}, |
|
|
2865 |
"outputs": [ |
|
|
2866 |
{ |
|
|
2867 |
"name": "stdout", |
|
|
2868 |
"output_type": "stream", |
|
|
2869 |
"text": [ |
|
|
2870 |
" precision recall f1-score support\n", |
|
|
2871 |
"\n", |
|
|
2872 |
" 0 1.00 1.00 1.00 1\n", |
|
|
2873 |
" 1 1.00 1.00 1.00 3\n", |
|
|
2874 |
" 2 0.50 1.00 0.67 1\n", |
|
|
2875 |
" 3 0.60 1.00 0.75 3\n", |
|
|
2876 |
" 4 0.00 0.00 0.00 0\n", |
|
|
2877 |
" 5 1.00 1.00 1.00 4\n", |
|
|
2878 |
" 6 0.00 0.00 0.00 2\n", |
|
|
2879 |
" 7 0.50 0.25 0.33 4\n", |
|
|
2880 |
"\n", |
|
|
2881 |
" accuracy 0.72 18\n", |
|
|
2882 |
" macro avg 0.57 0.66 0.59 18\n", |
|
|
2883 |
"weighted avg 0.68 0.72 0.68 18\n", |
|
|
2884 |
"\n" |
|
|
2885 |
] |
|
|
2886 |
}, |
|
|
2887 |
{ |
|
|
2888 |
"name": "stderr", |
|
|
2889 |
"output_type": "stream", |
|
|
2890 |
"text": [ |
|
|
2891 |
"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n", |
|
|
2892 |
" 'precision', 'predicted', average, warn_for)\n", |
|
|
2893 |
"C:\\Users\\Siddhant Mulajkar\\Anaconda3\\envs\\sid\\lib\\site-packages\\sklearn\\metrics\\classification.py:1439: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n", |
|
|
2894 |
" 'recall', 'true', average, warn_for)\n" |
|
|
2895 |
] |
|
|
2896 |
} |
|
|
2897 |
], |
|
|
2898 |
"source": [ |
|
|
2899 |
"from sklearn.metrics import classification_report\n", |
|
|
2900 |
"report = classification_report(new_Ytest, predictions)\n", |
|
|
2901 |
"print(report)" |
|
|
2902 |
] |
|
|
2903 |
}, |
|
|
2904 |
{ |
|
|
2905 |
"cell_type": "code", |
|
|
2906 |
"execution_count": 71, |
|
|
2907 |
"metadata": {}, |
|
|
2908 |
"outputs": [ |
|
|
2909 |
{ |
|
|
2910 |
"name": "stdout", |
|
|
2911 |
"output_type": "stream", |
|
|
2912 |
"text": [ |
|
|
2913 |
"[[1 0 0 0 0 0 0 0]\n", |
|
|
2914 |
" [0 3 0 0 0 0 0 0]\n", |
|
|
2915 |
" [0 0 1 0 0 0 0 0]\n", |
|
|
2916 |
" [0 0 0 3 0 0 0 0]\n", |
|
|
2917 |
" [0 0 0 0 0 0 0 0]\n", |
|
|
2918 |
" [0 0 0 0 0 4 0 0]\n", |
|
|
2919 |
" [0 0 0 1 0 0 0 1]\n", |
|
|
2920 |
" [0 0 1 1 1 0 0 1]]\n" |
|
|
2921 |
] |
|
|
2922 |
} |
|
|
2923 |
], |
|
|
2924 |
"source": [ |
|
|
2925 |
"from sklearn.metrics import confusion_matrix\n", |
|
|
2926 |
"matrix = confusion_matrix(new_Ytest, predictions)\n", |
|
|
2927 |
"print (matrix)\n", |
|
|
2928 |
"\n", |
|
|
2929 |
"# 0 = neutral, 1 = calm, 2 = happy, 3 = sad, 4 = angry, 5 = fearful, 6 = disgust, 7 = surprised" |
|
|
2930 |
] |
|
|
2931 |
}, |
|
|
2932 |
{ |
|
|
2933 |
"cell_type": "code", |
|
|
2934 |
"execution_count": 72, |
|
|
2935 |
"metadata": {}, |
|
|
2936 |
"outputs": [ |
|
|
2937 |
{ |
|
|
2938 |
"name": "stdout", |
|
|
2939 |
"output_type": "stream", |
|
|
2940 |
"text": [ |
|
|
2941 |
"Saved trained model at E:\\8th sem\\Final_Year_Project_Files\\Saved_Models\\Emotion_Voice_Detection_Model.h5 \n" |
|
|
2942 |
] |
|
|
2943 |
} |
|
|
2944 |
], |
|
|
2945 |
"source": [ |
|
|
2946 |
"model_name = 'Emotion_Voice_Detection_Model.h5'\n", |
|
|
2947 |
"save_dir = 'E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Saved_Models\\\\'\n", |
|
|
2948 |
"# Save model and weights\n", |
|
|
2949 |
"if not os.path.isdir(save_dir):\n", |
|
|
2950 |
" os.makedirs(save_dir)\n", |
|
|
2951 |
"model_path = os.path.join(save_dir, model_name)\n", |
|
|
2952 |
"model.save(model_path)\n", |
|
|
2953 |
"print('Saved trained model at %s ' % model_path)" |
|
|
2954 |
] |
|
|
2955 |
}, |
|
|
2956 |
{ |
|
|
2957 |
"cell_type": "code", |
|
|
2958 |
"execution_count": 73, |
|
|
2959 |
"metadata": {}, |
|
|
2960 |
"outputs": [ |
|
|
2961 |
{ |
|
|
2962 |
"name": "stdout", |
|
|
2963 |
"output_type": "stream", |
|
|
2964 |
"text": [ |
|
|
2965 |
"Model: \"sequential_1\"\n", |
|
|
2966 |
"_________________________________________________________________\n", |
|
|
2967 |
"Layer (type) Output Shape Param # \n", |
|
|
2968 |
"=================================================================\n", |
|
|
2969 |
"conv1d_2 (Conv1D) (None, 40, 128) 768 \n", |
|
|
2970 |
"_________________________________________________________________\n", |
|
|
2971 |
"activation_3 (Activation) (None, 40, 128) 0 \n", |
|
|
2972 |
"_________________________________________________________________\n", |
|
|
2973 |
"dropout_2 (Dropout) (None, 40, 128) 0 \n", |
|
|
2974 |
"_________________________________________________________________\n", |
|
|
2975 |
"max_pooling1d_1 (MaxPooling1 (None, 5, 128) 0 \n", |
|
|
2976 |
"_________________________________________________________________\n", |
|
|
2977 |
"conv1d_3 (Conv1D) (None, 5, 128) 82048 \n", |
|
|
2978 |
"_________________________________________________________________\n", |
|
|
2979 |
"activation_4 (Activation) (None, 5, 128) 0 \n", |
|
|
2980 |
"_________________________________________________________________\n", |
|
|
2981 |
"dropout_3 (Dropout) (None, 5, 128) 0 \n", |
|
|
2982 |
"_________________________________________________________________\n", |
|
|
2983 |
"flatten_1 (Flatten) (None, 640) 0 \n", |
|
|
2984 |
"_________________________________________________________________\n", |
|
|
2985 |
"dense_1 (Dense) (None, 8) 5128 \n", |
|
|
2986 |
"_________________________________________________________________\n", |
|
|
2987 |
"activation_5 (Activation) (None, 8) 0 \n", |
|
|
2988 |
"=================================================================\n", |
|
|
2989 |
"Total params: 87,944\n", |
|
|
2990 |
"Trainable params: 87,944\n", |
|
|
2991 |
"Non-trainable params: 0\n", |
|
|
2992 |
"_________________________________________________________________\n" |
|
|
2993 |
] |
|
|
2994 |
} |
|
|
2995 |
], |
|
|
2996 |
"source": [ |
|
|
2997 |
"from tensorflow import keras\n", |
|
|
2998 |
"\n", |
|
|
2999 |
"from tensorflow.keras.models import load_model\n", |
|
|
3000 |
"\n", |
|
|
3001 |
"from tensorflow.keras.utils import CustomObjectScope\n", |
|
|
3002 |
"\n", |
|
|
3003 |
"from tensorflow.keras.initializers import glorot_uniform\n", |
|
|
3004 |
"\n", |
|
|
3005 |
"with CustomObjectScope({'GlorotUniform': glorot_uniform()}):\n", |
|
|
3006 |
"\n", |
|
|
3007 |
" loaded_model = load_model('E:\\\\8th sem\\\\Final_Year_Project_Files\\\\Saved_Models\\\\Emotion_Voice_Detection_Model.h5')\n", |
|
|
3008 |
" loaded_model.summary()" |
|
|
3009 |
] |
|
|
3010 |
}, |
|
|
3011 |
{ |
|
|
3012 |
"cell_type": "code", |
|
|
3013 |
"execution_count": 74, |
|
|
3014 |
"metadata": {}, |
|
|
3015 |
"outputs": [ |
|
|
3016 |
{ |
|
|
3017 |
"name": "stdout", |
|
|
3018 |
"output_type": "stream", |
|
|
3019 |
"text": [ |
|
|
3020 |
"18/1 [============================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 5ms/sample - loss: 1.6930 - accuracy: 0.7222\n", |
|
|
3021 |
"Restored model, accuracy: 72.22%\n" |
|
|
3022 |
] |
|
|
3023 |
} |
|
|
3024 |
], |
|
|
3025 |
"source": [ |
|
|
3026 |
"loss, acc = loaded_model.evaluate(x_testcnn, y_test)\n", |
|
|
3027 |
"print(\"Restored model, accuracy: {:5.2f}%\".format(100*acc))" |
|
|
3028 |
] |
|
|
3029 |
}, |
|
|
3030 |
{ |
|
|
3031 |
"cell_type": "code", |
|
|
3032 |
"execution_count": null, |
|
|
3033 |
"metadata": {}, |
|
|
3034 |
"outputs": [], |
|
|
3035 |
"source": [] |
|
|
3036 |
} |
|
|
3037 |
], |
|
|
3038 |
"metadata": { |
|
|
3039 |
"kernelspec": { |
|
|
3040 |
"display_name": "Python 3", |
|
|
3041 |
"language": "python", |
|
|
3042 |
"name": "python3" |
|
|
3043 |
}, |
|
|
3044 |
"language_info": { |
|
|
3045 |
"codemirror_mode": { |
|
|
3046 |
"name": "ipython", |
|
|
3047 |
"version": 3 |
|
|
3048 |
}, |
|
|
3049 |
"file_extension": ".py", |
|
|
3050 |
"mimetype": "text/x-python", |
|
|
3051 |
"name": "python", |
|
|
3052 |
"nbconvert_exporter": "python", |
|
|
3053 |
"pygments_lexer": "ipython3", |
|
|
3054 |
"version": "3.7.5" |
|
|
3055 |
} |
|
|
3056 |
}, |
|
|
3057 |
"nbformat": 4, |
|
|
3058 |
"nbformat_minor": 2 |
|
|
3059 |
} |