In [5]:
sns.countplot(x='benign_0__mal_1',data=data);
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
data = pd.read_csv('DATA/cancer_classification.csv')
data.isnull().sum()
mean radius 0 mean texture 0 mean perimeter 0 mean area 0 mean smoothness 0 mean compactness 0 mean concavity 0 mean concave points 0 mean symmetry 0 mean fractal dimension 0 radius error 0 texture error 0 perimeter error 0 area error 0 smoothness error 0 compactness error 0 concavity error 0 concave points error 0 symmetry error 0 fractal dimension error 0 worst radius 0 worst texture 0 worst perimeter 0 worst area 0 worst smoothness 0 worst compactness 0 worst concavity 0 worst concave points 0 worst symmetry 0 worst fractal dimension 0 benign_0__mal_1 0 dtype: int64
data.describe().transpose()
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| mean radius | 569.0 | 14.127292 | 3.524049 | 6.981000 | 11.700000 | 13.370000 | 15.780000 | 28.11000 |
| mean texture | 569.0 | 19.289649 | 4.301036 | 9.710000 | 16.170000 | 18.840000 | 21.800000 | 39.28000 |
| mean perimeter | 569.0 | 91.969033 | 24.298981 | 43.790000 | 75.170000 | 86.240000 | 104.100000 | 188.50000 |
| mean area | 569.0 | 654.889104 | 351.914129 | 143.500000 | 420.300000 | 551.100000 | 782.700000 | 2501.00000 |
| mean smoothness | 569.0 | 0.096360 | 0.014064 | 0.052630 | 0.086370 | 0.095870 | 0.105300 | 0.16340 |
| mean compactness | 569.0 | 0.104341 | 0.052813 | 0.019380 | 0.064920 | 0.092630 | 0.130400 | 0.34540 |
| mean concavity | 569.0 | 0.088799 | 0.079720 | 0.000000 | 0.029560 | 0.061540 | 0.130700 | 0.42680 |
| mean concave points | 569.0 | 0.048919 | 0.038803 | 0.000000 | 0.020310 | 0.033500 | 0.074000 | 0.20120 |
| mean symmetry | 569.0 | 0.181162 | 0.027414 | 0.106000 | 0.161900 | 0.179200 | 0.195700 | 0.30400 |
| mean fractal dimension | 569.0 | 0.062798 | 0.007060 | 0.049960 | 0.057700 | 0.061540 | 0.066120 | 0.09744 |
| radius error | 569.0 | 0.405172 | 0.277313 | 0.111500 | 0.232400 | 0.324200 | 0.478900 | 2.87300 |
| texture error | 569.0 | 1.216853 | 0.551648 | 0.360200 | 0.833900 | 1.108000 | 1.474000 | 4.88500 |
| perimeter error | 569.0 | 2.866059 | 2.021855 | 0.757000 | 1.606000 | 2.287000 | 3.357000 | 21.98000 |
| area error | 569.0 | 40.337079 | 45.491006 | 6.802000 | 17.850000 | 24.530000 | 45.190000 | 542.20000 |
| smoothness error | 569.0 | 0.007041 | 0.003003 | 0.001713 | 0.005169 | 0.006380 | 0.008146 | 0.03113 |
| compactness error | 569.0 | 0.025478 | 0.017908 | 0.002252 | 0.013080 | 0.020450 | 0.032450 | 0.13540 |
| concavity error | 569.0 | 0.031894 | 0.030186 | 0.000000 | 0.015090 | 0.025890 | 0.042050 | 0.39600 |
| concave points error | 569.0 | 0.011796 | 0.006170 | 0.000000 | 0.007638 | 0.010930 | 0.014710 | 0.05279 |
| symmetry error | 569.0 | 0.020542 | 0.008266 | 0.007882 | 0.015160 | 0.018730 | 0.023480 | 0.07895 |
| fractal dimension error | 569.0 | 0.003795 | 0.002646 | 0.000895 | 0.002248 | 0.003187 | 0.004558 | 0.02984 |
| worst radius | 569.0 | 16.269190 | 4.833242 | 7.930000 | 13.010000 | 14.970000 | 18.790000 | 36.04000 |
| worst texture | 569.0 | 25.677223 | 6.146258 | 12.020000 | 21.080000 | 25.410000 | 29.720000 | 49.54000 |
| worst perimeter | 569.0 | 107.261213 | 33.602542 | 50.410000 | 84.110000 | 97.660000 | 125.400000 | 251.20000 |
| worst area | 569.0 | 880.583128 | 569.356993 | 185.200000 | 515.300000 | 686.500000 | 1084.000000 | 4254.00000 |
| worst smoothness | 569.0 | 0.132369 | 0.022832 | 0.071170 | 0.116600 | 0.131300 | 0.146000 | 0.22260 |
| worst compactness | 569.0 | 0.254265 | 0.157336 | 0.027290 | 0.147200 | 0.211900 | 0.339100 | 1.05800 |
| worst concavity | 569.0 | 0.272188 | 0.208624 | 0.000000 | 0.114500 | 0.226700 | 0.382900 | 1.25200 |
| worst concave points | 569.0 | 0.114606 | 0.065732 | 0.000000 | 0.064930 | 0.099930 | 0.161400 | 0.29100 |
| worst symmetry | 569.0 | 0.290076 | 0.061867 | 0.156500 | 0.250400 | 0.282200 | 0.317900 | 0.66380 |
| worst fractal dimension | 569.0 | 0.083946 | 0.018061 | 0.055040 | 0.071460 | 0.080040 | 0.092080 | 0.20750 |
| benign_0__mal_1 | 569.0 | 0.627417 | 0.483918 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.00000 |
sns.countplot(x='benign_0__mal_1',data=data);
data.corr()['benign_0__mal_1'][:-1].sort_values().plot(kind='bar')
<Axes: >
x = data.drop('benign_0__mal_1', axis=1).values
y = data['benign_0__mal_1'].values
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=42)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
model = Sequential()
model.add(Dense(30, activation='relu'))
model.add(Dense(15, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(x=x_train, y=y_train, validation_data=(x_test, y_test), epochs=600)
Epoch 1/600 15/15 [==============================] - 2s 24ms/step - loss: 0.6649 - val_loss: 0.6319 Epoch 2/600 15/15 [==============================] - 0s 6ms/step - loss: 0.6199 - val_loss: 0.5798 Epoch 3/600 15/15 [==============================] - 0s 6ms/step - loss: 0.5653 - val_loss: 0.5170 Epoch 4/600 15/15 [==============================] - 0s 7ms/step - loss: 0.5097 - val_loss: 0.4547 Epoch 5/600 15/15 [==============================] - 0s 7ms/step - loss: 0.4520 - val_loss: 0.3962 Epoch 6/600 15/15 [==============================] - 0s 7ms/step - loss: 0.3988 - val_loss: 0.3442 Epoch 7/600 15/15 [==============================] - 0s 6ms/step - loss: 0.3534 - val_loss: 0.2997 Epoch 8/600 15/15 [==============================] - 0s 7ms/step - loss: 0.3122 - val_loss: 0.2613 Epoch 9/600 15/15 [==============================] - 0s 7ms/step - loss: 0.2793 - val_loss: 0.2297 Epoch 10/600 15/15 [==============================] - 0s 5ms/step - loss: 0.2550 - val_loss: 0.2057 Epoch 11/600 15/15 [==============================] - 0s 5ms/step - loss: 0.2302 - val_loss: 0.1862 Epoch 12/600 15/15 [==============================] - 0s 5ms/step - loss: 0.2133 - val_loss: 0.1722 Epoch 13/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1989 - val_loss: 0.1612 Epoch 14/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1881 - val_loss: 0.1518 Epoch 15/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1781 - val_loss: 0.1422 Epoch 16/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1694 - val_loss: 0.1348 Epoch 17/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1597 - val_loss: 0.1272 Epoch 18/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1514 - val_loss: 0.1214 Epoch 19/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1453 - val_loss: 0.1163 Epoch 20/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1386 - val_loss: 0.1119 Epoch 21/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1330 - val_loss: 0.1073 Epoch 22/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1266 - val_loss: 0.1036 Epoch 23/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1232 - val_loss: 0.0997 Epoch 24/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1165 - val_loss: 0.0965 Epoch 25/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1116 - val_loss: 0.0931 Epoch 26/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1075 - val_loss: 0.0898 Epoch 27/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1049 - val_loss: 0.0890 Epoch 28/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1014 - val_loss: 0.0856 Epoch 29/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0959 - val_loss: 0.0846 Epoch 30/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0938 - val_loss: 0.0836 Epoch 31/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0931 - val_loss: 0.0808 Epoch 32/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0904 - val_loss: 0.0798 Epoch 33/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0875 - val_loss: 0.0809 Epoch 34/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0832 - val_loss: 0.0766 Epoch 35/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0819 - val_loss: 0.0754 Epoch 36/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0794 - val_loss: 0.0739 Epoch 37/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0803 - val_loss: 0.0766 Epoch 38/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0801 - val_loss: 0.0725 Epoch 39/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0753 - val_loss: 0.0711 Epoch 40/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0722 - val_loss: 0.0711 Epoch 41/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0708 - val_loss: 0.0701 Epoch 42/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0694 - val_loss: 0.0699 Epoch 43/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0680 - val_loss: 0.0693 Epoch 44/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0695 - val_loss: 0.0687 Epoch 45/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0668 - val_loss: 0.0680 Epoch 46/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0653 - val_loss: 0.0676 Epoch 47/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0643 - val_loss: 0.0670 Epoch 48/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0615 - val_loss: 0.0660 Epoch 49/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0610 - val_loss: 0.0674 Epoch 50/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0608 - val_loss: 0.0660 Epoch 51/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0593 - val_loss: 0.0664 Epoch 52/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0587 - val_loss: 0.0661 Epoch 53/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0609 - val_loss: 0.0707 Epoch 54/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0606 - val_loss: 0.0655 Epoch 55/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0576 - val_loss: 0.0642 Epoch 56/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0558 - val_loss: 0.0648 Epoch 57/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0545 - val_loss: 0.0645 Epoch 58/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0567 - val_loss: 0.0684 Epoch 59/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0589 - val_loss: 0.0653 Epoch 60/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0532 - val_loss: 0.0641 Epoch 61/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0562 - val_loss: 0.0645 Epoch 62/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0523 - val_loss: 0.0634 Epoch 63/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0513 - val_loss: 0.0630 Epoch 64/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0513 - val_loss: 0.0629 Epoch 65/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0524 - val_loss: 0.0644 Epoch 66/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0532 - val_loss: 0.0641 Epoch 67/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0509 - val_loss: 0.0653 Epoch 68/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0499 - val_loss: 0.0649 Epoch 69/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0485 - val_loss: 0.0646 Epoch 70/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0483 - val_loss: 0.0637 Epoch 71/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0488 - val_loss: 0.0639 Epoch 72/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0478 - val_loss: 0.0642 Epoch 73/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0508 - val_loss: 0.0662 Epoch 74/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0471 - val_loss: 0.0643 Epoch 75/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0480 - val_loss: 0.0636 Epoch 76/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0457 - val_loss: 0.0646 Epoch 77/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0460 - val_loss: 0.0641 Epoch 78/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0463 - val_loss: 0.0655 Epoch 79/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0479 - val_loss: 0.0641 Epoch 80/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0445 - val_loss: 0.0665 Epoch 81/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0457 - val_loss: 0.0641 Epoch 82/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0447 - val_loss: 0.0651 Epoch 83/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0453 - val_loss: 0.0646 Epoch 84/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0445 - val_loss: 0.0641 Epoch 85/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0435 - val_loss: 0.0638 Epoch 86/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0436 - val_loss: 0.0655 Epoch 87/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0441 - val_loss: 0.0652 Epoch 88/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0474 - val_loss: 0.0636 Epoch 89/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0421 - val_loss: 0.0641 Epoch 90/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0431 - val_loss: 0.0638 Epoch 91/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0472 - val_loss: 0.0702 Epoch 92/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0455 - val_loss: 0.0648 Epoch 93/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0463 - val_loss: 0.0658 Epoch 94/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0410 - val_loss: 0.0658 Epoch 95/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0476 - val_loss: 0.0654 Epoch 96/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0475 - val_loss: 0.0668 Epoch 97/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0395 - val_loss: 0.0660 Epoch 98/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0425 - val_loss: 0.0655 Epoch 99/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0390 - val_loss: 0.0652 Epoch 100/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0392 - val_loss: 0.0667 Epoch 101/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0393 - val_loss: 0.0673 Epoch 102/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0389 - val_loss: 0.0670 Epoch 103/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0398 - val_loss: 0.0695 Epoch 104/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0427 - val_loss: 0.0664 Epoch 105/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0452 - val_loss: 0.0698 Epoch 106/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0407 - val_loss: 0.0711 Epoch 107/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0390 - val_loss: 0.0731 Epoch 108/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0397 - val_loss: 0.0672 Epoch 109/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0367 - val_loss: 0.0698 Epoch 110/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0391 - val_loss: 0.0674 Epoch 111/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0435 - val_loss: 0.0705 Epoch 112/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0393 - val_loss: 0.0679 Epoch 113/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0367 - val_loss: 0.0692 Epoch 114/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0360 - val_loss: 0.0687 Epoch 115/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0379 - val_loss: 0.0698 Epoch 116/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0498 - val_loss: 0.0728 Epoch 117/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0411 - val_loss: 0.0726 Epoch 118/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0368 - val_loss: 0.0742 Epoch 119/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0355 - val_loss: 0.0691 Epoch 120/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0347 - val_loss: 0.0692 Epoch 121/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0344 - val_loss: 0.0687 Epoch 122/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0386 - val_loss: 0.0683 Epoch 123/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0360 - val_loss: 0.0745 Epoch 124/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0359 - val_loss: 0.0783 Epoch 125/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0357 - val_loss: 0.0712 Epoch 126/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0347 - val_loss: 0.0708 Epoch 127/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0344 - val_loss: 0.0696 Epoch 128/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0326 - val_loss: 0.0733 Epoch 129/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0359 - val_loss: 0.0698 Epoch 130/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0374 - val_loss: 0.0732 Epoch 131/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0372 - val_loss: 0.0708 Epoch 132/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0339 - val_loss: 0.0707 Epoch 133/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0329 - val_loss: 0.0741 Epoch 134/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0327 - val_loss: 0.0715 Epoch 135/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0314 - val_loss: 0.0735 Epoch 136/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0315 - val_loss: 0.0710 Epoch 137/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0325 - val_loss: 0.0769 Epoch 138/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0308 - val_loss: 0.0715 Epoch 139/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0312 - val_loss: 0.0741 Epoch 140/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0306 - val_loss: 0.0744 Epoch 141/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0326 - val_loss: 0.0725 Epoch 142/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0313 - val_loss: 0.0703 Epoch 143/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0332 - val_loss: 0.0822 Epoch 144/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0409 - val_loss: 0.0736 Epoch 145/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0320 - val_loss: 0.0757 Epoch 146/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0301 - val_loss: 0.0782 Epoch 147/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0305 - val_loss: 0.0742 Epoch 148/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0287 - val_loss: 0.0814 Epoch 149/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0287 - val_loss: 0.0741 Epoch 150/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0289 - val_loss: 0.0837 Epoch 151/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0377 - val_loss: 0.0733 Epoch 152/600 15/15 [==============================] - 0s 14ms/step - loss: 0.0301 - val_loss: 0.0779 Epoch 153/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0280 - val_loss: 0.0827 Epoch 154/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0277 - val_loss: 0.0773 Epoch 155/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0278 - val_loss: 0.0794 Epoch 156/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0290 - val_loss: 0.0759 Epoch 157/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0309 - val_loss: 0.0815 Epoch 158/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0301 - val_loss: 0.0768 Epoch 159/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0270 - val_loss: 0.0837 Epoch 160/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0270 - val_loss: 0.0800 Epoch 161/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0262 - val_loss: 0.0775 Epoch 162/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0261 - val_loss: 0.0881 Epoch 163/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0268 - val_loss: 0.0749 Epoch 164/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0271 - val_loss: 0.0822 Epoch 165/600 15/15 [==============================] - 0s 22ms/step - loss: 0.0251 - val_loss: 0.0767 Epoch 166/600 15/15 [==============================] - 0s 19ms/step - loss: 0.0268 - val_loss: 0.0882 Epoch 167/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0250 - val_loss: 0.0787 Epoch 168/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0245 - val_loss: 0.0820 Epoch 169/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0245 - val_loss: 0.0820 Epoch 170/600 15/15 [==============================] - 0s 23ms/step - loss: 0.0245 - val_loss: 0.0853 Epoch 171/600 15/15 [==============================] - 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0s 9ms/step - loss: 0.0040 - val_loss: 0.1940 Epoch 372/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0042 - val_loss: 0.2129 Epoch 373/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0044 - val_loss: 0.1815 Epoch 374/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0042 - val_loss: 0.2130 Epoch 375/600 15/15 [==============================] - 0s 16ms/step - loss: 0.0043 - val_loss: 0.2110 Epoch 376/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0036 - val_loss: 0.1914 Epoch 377/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0037 - val_loss: 0.2196 Epoch 378/600 15/15 [==============================] - 0s 16ms/step - loss: 0.0037 - val_loss: 0.1884 Epoch 379/600 15/15 [==============================] - 0s 15ms/step - loss: 0.0038 - val_loss: 0.2061 Epoch 380/600 15/15 [==============================] - 0s 16ms/step - loss: 0.0034 - val_loss: 0.1919 Epoch 381/600 15/15 [==============================] - 0s 21ms/step - loss: 0.0038 - val_loss: 0.2012 Epoch 382/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0035 - val_loss: 0.2030 Epoch 383/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0034 - val_loss: 0.1968 Epoch 384/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0048 - val_loss: 0.2147 Epoch 385/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0046 - val_loss: 0.2041 Epoch 386/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0034 - val_loss: 0.1951 Epoch 387/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0038 - val_loss: 0.2199 Epoch 388/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0033 - val_loss: 0.2058 Epoch 389/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0034 - val_loss: 0.2104 Epoch 390/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0031 - val_loss: 0.2130 Epoch 391/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0031 - val_loss: 0.2182 Epoch 392/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0033 - val_loss: 0.2092 Epoch 393/600 15/15 [==============================] - 0s 28ms/step - loss: 0.0039 - val_loss: 0.2183 Epoch 394/600 15/15 [==============================] - 0s 21ms/step - loss: 0.0045 - val_loss: 0.2081 Epoch 395/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0031 - val_loss: 0.2137 Epoch 396/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0031 - val_loss: 0.2127 Epoch 397/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0030 - val_loss: 0.2113 Epoch 398/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0039 - val_loss: 0.2352 Epoch 399/600 15/15 [==============================] - 0s 20ms/step - loss: 0.0031 - val_loss: 0.2085 Epoch 400/600 15/15 [==============================] - 1s 46ms/step - loss: 0.0028 - val_loss: 0.2151 Epoch 401/600 15/15 [==============================] - 0s 21ms/step - loss: 0.0029 - val_loss: 0.2284 Epoch 402/600 15/15 [==============================] - 0s 27ms/step - loss: 0.0045 - val_loss: 0.2092 Epoch 403/600 15/15 [==============================] - 0s 25ms/step - loss: 0.0031 - val_loss: 0.2178 Epoch 404/600 15/15 [==============================] - 0s 20ms/step - loss: 0.0031 - val_loss: 0.2254 Epoch 405/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0034 - val_loss: 0.2025 Epoch 406/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0039 - val_loss: 0.2328 Epoch 407/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0030 - val_loss: 0.2219 Epoch 408/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0026 - val_loss: 0.2190 Epoch 409/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0029 - val_loss: 0.2189 Epoch 410/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0027 - val_loss: 0.2275 Epoch 411/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0025 - val_loss: 0.2262 Epoch 412/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0037 - val_loss: 0.2167 Epoch 413/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0030 - val_loss: 0.2378 Epoch 414/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0034 - val_loss: 0.2156 Epoch 415/600 15/15 [==============================] - 0s 14ms/step - loss: 0.0024 - val_loss: 0.2241 Epoch 416/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0024 - val_loss: 0.2239 Epoch 417/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0023 - val_loss: 0.2230 Epoch 418/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0029 - val_loss: 0.2311 Epoch 419/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0023 - val_loss: 0.2114 Epoch 420/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0027 - val_loss: 0.2412 Epoch 421/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0024 - val_loss: 0.2167 Epoch 422/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0025 - val_loss: 0.2353 Epoch 423/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0024 - val_loss: 0.2205 Epoch 424/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0024 - val_loss: 0.2359 Epoch 425/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0023 - val_loss: 0.2350 Epoch 426/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0024 - val_loss: 0.2200 Epoch 427/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0023 - val_loss: 0.2458 Epoch 428/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0023 - val_loss: 0.2320 Epoch 429/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0026 - val_loss: 0.2593 Epoch 430/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0031 - val_loss: 0.2212 Epoch 431/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0022 - val_loss: 0.2280 Epoch 432/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0035 - val_loss: 0.2527 Epoch 433/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0023 - val_loss: 0.2301 Epoch 434/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0026 - val_loss: 0.2249 Epoch 435/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0023 - val_loss: 0.2371 Epoch 436/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0020 - val_loss: 0.2348 Epoch 437/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0021 - val_loss: 0.2433 Epoch 438/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0034 - val_loss: 0.2082 Epoch 439/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0074 - val_loss: 0.4150 Epoch 440/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0266 - val_loss: 0.3076 Epoch 441/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0088 - val_loss: 0.2046 Epoch 442/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0061 - val_loss: 0.2495 Epoch 443/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0033 - val_loss: 0.2406 Epoch 444/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0025 - val_loss: 0.2456 Epoch 445/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0022 - val_loss: 0.2462 Epoch 446/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0020 - val_loss: 0.2446 Epoch 447/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0022 - val_loss: 0.2462 Epoch 448/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0021 - val_loss: 0.2471 Epoch 449/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0018 - val_loss: 0.2560 Epoch 450/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0020 - val_loss: 0.2528 Epoch 451/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0019 - val_loss: 0.2494 Epoch 452/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0018 - val_loss: 0.2490 Epoch 453/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0017 - val_loss: 0.2486 Epoch 454/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0018 - val_loss: 0.2479 Epoch 455/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0017 - val_loss: 0.2508 Epoch 456/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0016 - val_loss: 0.2576 Epoch 457/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0018 - val_loss: 0.2618 Epoch 458/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0022 - val_loss: 0.2455 Epoch 459/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0018 - val_loss: 0.2498 Epoch 460/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0016 - val_loss: 0.2575 Epoch 461/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0016 - val_loss: 0.2557 Epoch 462/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0018 - val_loss: 0.2498 Epoch 463/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0018 - val_loss: 0.2615 Epoch 464/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0016 - val_loss: 0.2592 Epoch 465/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0016 - val_loss: 0.2595 Epoch 466/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0015 - val_loss: 0.2553 Epoch 467/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0016 - val_loss: 0.2572 Epoch 468/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0018 - val_loss: 0.2720 Epoch 469/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0017 - val_loss: 0.2526 Epoch 470/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0016 - val_loss: 0.2562 Epoch 471/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0016 - val_loss: 0.2704 Epoch 472/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0017 - val_loss: 0.2561 Epoch 473/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0016 - val_loss: 0.2548 Epoch 474/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0016 - val_loss: 0.2602 Epoch 475/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0014 - val_loss: 0.2651 Epoch 476/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0016 - val_loss: 0.2531 Epoch 477/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0015 - val_loss: 0.2576 Epoch 478/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0015 - val_loss: 0.2601 Epoch 479/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0015 - val_loss: 0.2608 Epoch 480/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0019 - val_loss: 0.2638 Epoch 481/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0018 - val_loss: 0.2519 Epoch 482/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0018 - val_loss: 0.2814 Epoch 483/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0015 - val_loss: 0.2544 Epoch 484/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0015 - val_loss: 0.2655 Epoch 485/600 15/15 [==============================] - 0s 14ms/step - loss: 0.0014 - val_loss: 0.2643 Epoch 486/600 15/15 [==============================] - 0s 23ms/step - loss: 0.0013 - val_loss: 0.2680 Epoch 487/600 15/15 [==============================] - 0s 15ms/step - loss: 0.0017 - val_loss: 0.2757 Epoch 488/600 15/15 [==============================] - 0s 18ms/step - loss: 0.0019 - val_loss: 0.2642 Epoch 489/600 15/15 [==============================] - 0s 15ms/step - loss: 0.0016 - val_loss: 0.2602 Epoch 490/600 15/15 [==============================] - 1s 46ms/step - loss: 0.0025 - val_loss: 0.2774 Epoch 491/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0013 - val_loss: 0.2630 Epoch 492/600 15/15 [==============================] - 0s 19ms/step - loss: 0.0013 - val_loss: 0.2682 Epoch 493/600 15/15 [==============================] - 0s 15ms/step - loss: 0.0014 - val_loss: 0.2733 Epoch 494/600 15/15 [==============================] - 0s 16ms/step - loss: 0.0015 - val_loss: 0.2669 Epoch 495/600 15/15 [==============================] - 0s 17ms/step - loss: 0.0013 - val_loss: 0.2747 Epoch 496/600 15/15 [==============================] - 0s 16ms/step - loss: 0.0012 - val_loss: 0.2709 Epoch 497/600 15/15 [==============================] - 0s 29ms/step - loss: 0.0012 - val_loss: 0.2711 Epoch 498/600 15/15 [==============================] - 0s 14ms/step - loss: 0.0012 - val_loss: 0.2721 Epoch 499/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0012 - val_loss: 0.2673 Epoch 500/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0011 - val_loss: 0.2769 Epoch 501/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0012 - val_loss: 0.2712 Epoch 502/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0014 - val_loss: 0.2746 Epoch 503/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0011 - val_loss: 0.2721 Epoch 504/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0012 - val_loss: 0.2678 Epoch 505/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0012 - val_loss: 0.2733 Epoch 506/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0011 - val_loss: 0.2755 Epoch 507/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0012 - val_loss: 0.2651 Epoch 508/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0013 - val_loss: 0.2878 Epoch 509/600 15/15 [==============================] - 1s 37ms/step - loss: 0.0016 - val_loss: 0.2644 Epoch 510/600 15/15 [==============================] - 0s 21ms/step - loss: 0.0015 - val_loss: 0.2807 Epoch 511/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0010 - val_loss: 0.2713 Epoch 512/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0011 - val_loss: 0.2746 Epoch 513/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0010 - val_loss: 0.2808 Epoch 514/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0010 - val_loss: 0.2747 Epoch 515/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0013 - val_loss: 0.2935 Epoch 516/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0013 - val_loss: 0.2716 Epoch 517/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0015 - val_loss: 0.2704 Epoch 518/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0012 - val_loss: 0.2859 Epoch 519/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0011 - val_loss: 0.2741 Epoch 520/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0010 - val_loss: 0.2858 Epoch 521/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0011 - val_loss: 0.2728 Epoch 522/600 15/15 [==============================] - 0s 10ms/step - loss: 9.9242e-04 - val_loss: 0.2857 Epoch 523/600 15/15 [==============================] - 0s 11ms/step - loss: 9.9168e-04 - val_loss: 0.2784 Epoch 524/600 15/15 [==============================] - 0s 12ms/step - loss: 9.3395e-04 - val_loss: 0.2773 Epoch 525/600 15/15 [==============================] - 0s 8ms/step - loss: 9.4250e-04 - val_loss: 0.2813 Epoch 526/600 15/15 [==============================] - 0s 8ms/step - loss: 9.9577e-04 - val_loss: 0.2788 Epoch 527/600 15/15 [==============================] - 0s 7ms/step - loss: 9.5923e-04 - val_loss: 0.2802 Epoch 528/600 15/15 [==============================] - 0s 8ms/step - loss: 9.7047e-04 - val_loss: 0.2806 Epoch 529/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0010 - val_loss: 0.2885 Epoch 530/600 15/15 [==============================] - 0s 11ms/step - loss: 9.5432e-04 - val_loss: 0.2755 Epoch 531/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0010 - val_loss: 0.2890 Epoch 532/600 15/15 [==============================] - 0s 11ms/step - loss: 8.5708e-04 - val_loss: 0.2762 Epoch 533/600 15/15 [==============================] - 0s 9ms/step - loss: 9.7404e-04 - val_loss: 0.2932 Epoch 534/600 15/15 [==============================] - 0s 7ms/step - loss: 9.4466e-04 - val_loss: 0.2859 Epoch 535/600 15/15 [==============================] - 0s 7ms/step - loss: 9.5139e-04 - val_loss: 0.2867 Epoch 536/600 15/15 [==============================] - 0s 8ms/step - loss: 9.5536e-04 - val_loss: 0.2779 Epoch 537/600 15/15 [==============================] - 0s 16ms/step - loss: 9.4992e-04 - val_loss: 0.2962 Epoch 538/600 15/15 [==============================] - 0s 14ms/step - loss: 9.1500e-04 - val_loss: 0.2857 Epoch 539/600 15/15 [==============================] - 0s 14ms/step - loss: 9.8641e-04 - val_loss: 0.2901 Epoch 540/600 15/15 [==============================] - 0s 15ms/step - loss: 0.0013 - val_loss: 0.3153 Epoch 541/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0018 - val_loss: 0.2597 Epoch 542/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0018 - val_loss: 0.2852 Epoch 543/600 15/15 [==============================] - 0s 9ms/step - loss: 8.4766e-04 - val_loss: 0.2901 Epoch 544/600 15/15 [==============================] - 0s 10ms/step - loss: 8.6434e-04 - val_loss: 0.2942 Epoch 545/600 15/15 [==============================] - 0s 13ms/step - loss: 8.4590e-04 - val_loss: 0.2908 Epoch 546/600 15/15 [==============================] - 0s 8ms/step - loss: 8.1135e-04 - val_loss: 0.2907 Epoch 547/600 15/15 [==============================] - 0s 8ms/step - loss: 8.2484e-04 - val_loss: 0.2916 Epoch 548/600 15/15 [==============================] - 0s 9ms/step - loss: 8.6365e-04 - val_loss: 0.2927 Epoch 549/600 15/15 [==============================] - 0s 9ms/step - loss: 8.1643e-04 - val_loss: 0.2943 Epoch 550/600 15/15 [==============================] - 0s 9ms/step - loss: 9.0202e-04 - val_loss: 0.2950 Epoch 551/600 15/15 [==============================] - 0s 11ms/step - loss: 9.9143e-04 - val_loss: 0.2821 Epoch 552/600 15/15 [==============================] - 0s 12ms/step - loss: 9.2664e-04 - val_loss: 0.2901 Epoch 553/600 15/15 [==============================] - 0s 10ms/step - loss: 7.5374e-04 - val_loss: 0.2890 Epoch 554/600 15/15 [==============================] - 0s 11ms/step - loss: 7.9077e-04 - val_loss: 0.2959 Epoch 555/600 15/15 [==============================] - 0s 10ms/step - loss: 7.7441e-04 - val_loss: 0.2959 Epoch 556/600 15/15 [==============================] - 0s 9ms/step - loss: 7.2620e-04 - val_loss: 0.2893 Epoch 557/600 15/15 [==============================] - 0s 8ms/step - loss: 7.6446e-04 - val_loss: 0.3015 Epoch 558/600 15/15 [==============================] - 0s 6ms/step - loss: 7.1794e-04 - val_loss: 0.2964 Epoch 559/600 15/15 [==============================] - 0s 5ms/step - loss: 7.8442e-04 - val_loss: 0.2946 Epoch 560/600 15/15 [==============================] - 0s 7ms/step - loss: 7.2334e-04 - val_loss: 0.3067 Epoch 561/600 15/15 [==============================] - 0s 5ms/step - loss: 7.6359e-04 - val_loss: 0.2900 Epoch 562/600 15/15 [==============================] - 0s 7ms/step - loss: 6.8092e-04 - val_loss: 0.3018 Epoch 563/600 15/15 [==============================] - 0s 6ms/step - loss: 7.0018e-04 - val_loss: 0.3009 Epoch 564/600 15/15 [==============================] - 0s 7ms/step - loss: 7.3212e-04 - val_loss: 0.2977 Epoch 565/600 15/15 [==============================] - 0s 8ms/step - loss: 6.8915e-04 - val_loss: 0.2994 Epoch 566/600 15/15 [==============================] - 0s 6ms/step - loss: 6.8366e-04 - val_loss: 0.2990 Epoch 567/600 15/15 [==============================] - 0s 7ms/step - loss: 6.9534e-04 - val_loss: 0.2984 Epoch 568/600 15/15 [==============================] - 0s 6ms/step - loss: 7.7296e-04 - val_loss: 0.2961 Epoch 569/600 15/15 [==============================] - 0s 6ms/step - loss: 6.9852e-04 - val_loss: 0.3068 Epoch 570/600 15/15 [==============================] - 0s 6ms/step - loss: 7.1999e-04 - val_loss: 0.3006 Epoch 571/600 15/15 [==============================] - 0s 8ms/step - loss: 6.5149e-04 - val_loss: 0.3057 Epoch 572/600 15/15 [==============================] - 0s 6ms/step - loss: 6.8190e-04 - val_loss: 0.3058 Epoch 573/600 15/15 [==============================] - 0s 6ms/step - loss: 6.9828e-04 - val_loss: 0.2978 Epoch 574/600 15/15 [==============================] - 0s 6ms/step - loss: 6.9211e-04 - val_loss: 0.3106 Epoch 575/600 15/15 [==============================] - 0s 6ms/step - loss: 8.4102e-04 - val_loss: 0.3021 Epoch 576/600 15/15 [==============================] - 0s 6ms/step - loss: 7.3322e-04 - val_loss: 0.2916 Epoch 577/600 15/15 [==============================] - 0s 5ms/step - loss: 7.4751e-04 - val_loss: 0.3068 Epoch 578/600 15/15 [==============================] - 0s 5ms/step - loss: 6.3847e-04 - val_loss: 0.2974 Epoch 579/600 15/15 [==============================] - 0s 6ms/step - loss: 7.1483e-04 - val_loss: 0.3030 Epoch 580/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0012 - val_loss: 0.3258 Epoch 581/600 15/15 [==============================] - 0s 6ms/step - loss: 7.6783e-04 - val_loss: 0.3111 Epoch 582/600 15/15 [==============================] - 0s 9ms/step - loss: 5.8668e-04 - val_loss: 0.3150 Epoch 583/600 15/15 [==============================] - 0s 9ms/step - loss: 5.9852e-04 - val_loss: 0.3136 Epoch 584/600 15/15 [==============================] - 0s 10ms/step - loss: 6.0574e-04 - val_loss: 0.3113 Epoch 585/600 15/15 [==============================] - 0s 10ms/step - loss: 6.8632e-04 - val_loss: 0.3140 Epoch 586/600 15/15 [==============================] - 0s 10ms/step - loss: 6.2093e-04 - val_loss: 0.3033 Epoch 587/600 15/15 [==============================] - 0s 12ms/step - loss: 5.8883e-04 - val_loss: 0.3185 Epoch 588/600 15/15 [==============================] - 0s 6ms/step - loss: 7.7442e-04 - val_loss: 0.3025 Epoch 589/600 15/15 [==============================] - 0s 8ms/step - loss: 6.4632e-04 - val_loss: 0.3116 Epoch 590/600 15/15 [==============================] - 0s 8ms/step - loss: 5.8365e-04 - val_loss: 0.3122 Epoch 591/600 15/15 [==============================] - 0s 6ms/step - loss: 6.0909e-04 - val_loss: 0.3126 Epoch 592/600 15/15 [==============================] - 0s 7ms/step - loss: 5.5198e-04 - val_loss: 0.3134 Epoch 593/600 15/15 [==============================] - 0s 7ms/step - loss: 5.6166e-04 - val_loss: 0.3096 Epoch 594/600 15/15 [==============================] - 0s 6ms/step - loss: 5.6706e-04 - val_loss: 0.3189 Epoch 595/600 15/15 [==============================] - 0s 7ms/step - loss: 6.2608e-04 - val_loss: 0.3259 Epoch 596/600 15/15 [==============================] - 0s 8ms/step - loss: 6.2328e-04 - val_loss: 0.3083 Epoch 597/600 15/15 [==============================] - 0s 9ms/step - loss: 5.7743e-04 - val_loss: 0.3202 Epoch 598/600 15/15 [==============================] - 0s 10ms/step - loss: 5.3605e-04 - val_loss: 0.3139 Epoch 599/600 15/15 [==============================] - 0s 8ms/step - loss: 5.2770e-04 - val_loss: 0.3153 Epoch 600/600 15/15 [==============================] - 0s 8ms/step - loss: 5.4030e-04 - val_loss: 0.3126
<keras.callbacks.History at 0x190194fb650>
loss = pd.DataFrame(model.history.history)
loss.plot()
<Axes: >
prediction = (model.predict(x_test) > 0.5).astype('int32')
4/4 [==============================] - 0s 4ms/step
print(classification_report(y_pred=prediction, y_true=y_test))
precision recall f1-score support
0 0.89 0.95 0.92 43
1 0.97 0.93 0.95 71
accuracy 0.94 114
macro avg 0.93 0.94 0.94 114
weighted avg 0.94 0.94 0.94 114
ConfusionMatrixDisplay.from_predictions(y_true=y_test, y_pred=prediction)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x19021060650>
model = Sequential()
model.add(Dense(30, activation='relu'))
model.add(Dense(15, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
from tensorflow.keras.callbacks import EarlyStopping
# help(EarlyStopping)
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=25)
model.fit(x=x_train, y=y_train, validation_data=(x_test, y_test), epochs=600, verbose=1,callbacks=[early_stop])
Epoch 1/600 15/15 [==============================] - 3s 37ms/step - loss: 0.6920 - val_loss: 0.6697 Epoch 2/600 15/15 [==============================] - 0s 8ms/step - loss: 0.6574 - val_loss: 0.6322 Epoch 3/600 15/15 [==============================] - 0s 6ms/step - loss: 0.6229 - val_loss: 0.5920 Epoch 4/600 15/15 [==============================] - 0s 5ms/step - loss: 0.5862 - val_loss: 0.5507 Epoch 5/600 15/15 [==============================] - 0s 7ms/step - loss: 0.5467 - val_loss: 0.5031 Epoch 6/600 15/15 [==============================] - 0s 6ms/step - loss: 0.4961 - val_loss: 0.4488 Epoch 7/600 15/15 [==============================] - 0s 5ms/step - loss: 0.4424 - val_loss: 0.3919 Epoch 8/600 15/15 [==============================] - 0s 6ms/step - loss: 0.3905 - val_loss: 0.3398 Epoch 9/600 15/15 [==============================] - 0s 6ms/step - loss: 0.3436 - val_loss: 0.2958 Epoch 10/600 15/15 [==============================] - 0s 5ms/step - loss: 0.3056 - val_loss: 0.2611 Epoch 11/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2726 - val_loss: 0.2308 Epoch 12/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2484 - val_loss: 0.2068 Epoch 13/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2293 - val_loss: 0.1884 Epoch 14/600 15/15 [==============================] - 0s 5ms/step - loss: 0.2113 - val_loss: 0.1740 Epoch 15/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1982 - val_loss: 0.1607 Epoch 16/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1854 - val_loss: 0.1506 Epoch 17/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1802 - val_loss: 0.1424 Epoch 18/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1688 - val_loss: 0.1344 Epoch 19/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1589 - val_loss: 0.1274 Epoch 20/600 15/15 [==============================] - 0s 8ms/step - loss: 0.1515 - val_loss: 0.1216 Epoch 21/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1464 - val_loss: 0.1179 Epoch 22/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1441 - val_loss: 0.1142 Epoch 23/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1339 - val_loss: 0.1074 Epoch 24/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1281 - val_loss: 0.1038 Epoch 25/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1261 - val_loss: 0.1004 Epoch 26/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1203 - val_loss: 0.0967 Epoch 27/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1148 - val_loss: 0.0936 Epoch 28/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1141 - val_loss: 0.0913 Epoch 29/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1072 - val_loss: 0.0902 Epoch 30/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1043 - val_loss: 0.0868 Epoch 31/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1010 - val_loss: 0.0849 Epoch 32/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0977 - val_loss: 0.0835 Epoch 33/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1002 - val_loss: 0.0814 Epoch 34/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0993 - val_loss: 0.0824 Epoch 35/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0928 - val_loss: 0.0785 Epoch 36/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0891 - val_loss: 0.0779 Epoch 37/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0883 - val_loss: 0.0767 Epoch 38/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0893 - val_loss: 0.0788 Epoch 39/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0864 - val_loss: 0.0740 Epoch 40/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0823 - val_loss: 0.0730 Epoch 41/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0796 - val_loss: 0.0723 Epoch 42/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0780 - val_loss: 0.0719 Epoch 43/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0761 - val_loss: 0.0707 Epoch 44/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0751 - val_loss: 0.0701 Epoch 45/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0746 - val_loss: 0.0720 Epoch 46/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0738 - val_loss: 0.0693 Epoch 47/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0718 - val_loss: 0.0682 Epoch 48/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0702 - val_loss: 0.0676 Epoch 49/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0711 - val_loss: 0.0680 Epoch 50/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0683 - val_loss: 0.0677 Epoch 51/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0670 - val_loss: 0.0662 Epoch 52/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0670 - val_loss: 0.0655 Epoch 53/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0668 - val_loss: 0.0668 Epoch 54/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0659 - val_loss: 0.0648 Epoch 55/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0643 - val_loss: 0.0639 Epoch 56/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0628 - val_loss: 0.0633 Epoch 57/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0636 - val_loss: 0.0628 Epoch 58/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0631 - val_loss: 0.0652 Epoch 59/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0614 - val_loss: 0.0639 Epoch 60/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0600 - val_loss: 0.0627 Epoch 61/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0596 - val_loss: 0.0635 Epoch 62/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0619 - val_loss: 0.0619 Epoch 63/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0584 - val_loss: 0.0645 Epoch 64/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0581 - val_loss: 0.0623 Epoch 65/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0588 - val_loss: 0.0631 Epoch 66/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0576 - val_loss: 0.0614 Epoch 67/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0579 - val_loss: 0.0655 Epoch 68/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0603 - val_loss: 0.0608 Epoch 69/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0594 - val_loss: 0.0668 Epoch 70/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0570 - val_loss: 0.0609 Epoch 71/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0560 - val_loss: 0.0625 Epoch 72/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0544 - val_loss: 0.0610 Epoch 73/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0548 - val_loss: 0.0608 Epoch 74/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0546 - val_loss: 0.0616 Epoch 75/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0533 - val_loss: 0.0607 Epoch 76/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0566 - val_loss: 0.0653 Epoch 77/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0519 - val_loss: 0.0592 Epoch 78/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0556 - val_loss: 0.0593 Epoch 79/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0521 - val_loss: 0.0602 Epoch 80/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0529 - val_loss: 0.0584 Epoch 81/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0555 - val_loss: 0.0603 Epoch 82/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0506 - val_loss: 0.0589 Epoch 83/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0505 - val_loss: 0.0598 Epoch 84/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0495 - val_loss: 0.0595 Epoch 85/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0501 - val_loss: 0.0590 Epoch 86/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0494 - val_loss: 0.0589 Epoch 87/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0490 - val_loss: 0.0589 Epoch 88/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0490 - val_loss: 0.0612 Epoch 89/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0533 - val_loss: 0.0585 Epoch 90/600 15/15 [==============================] - 0s 12ms/step - loss: 0.0621 - val_loss: 0.0641 Epoch 91/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0647 - val_loss: 0.0592 Epoch 92/600 15/15 [==============================] - 0s 13ms/step - loss: 0.0559 - val_loss: 0.0613 Epoch 93/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0495 - val_loss: 0.0600 Epoch 94/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0502 - val_loss: 0.0633 Epoch 95/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0506 - val_loss: 0.0594 Epoch 96/600 15/15 [==============================] - 0s 11ms/step - loss: 0.0479 - val_loss: 0.0597 Epoch 97/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0474 - val_loss: 0.0616 Epoch 98/600 15/15 [==============================] - 0s 10ms/step - loss: 0.0463 - val_loss: 0.0596 Epoch 99/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0513 - val_loss: 0.0645 Epoch 100/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0518 - val_loss: 0.0605 Epoch 101/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0455 - val_loss: 0.0609 Epoch 102/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0455 - val_loss: 0.0608 Epoch 103/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0458 - val_loss: 0.0619 Epoch 104/600 15/15 [==============================] - 0s 8ms/step - loss: 0.0445 - val_loss: 0.0612 Epoch 105/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0447 - val_loss: 0.0608 Epoch 105: early stopping
<keras.callbacks.History at 0x1902340f250>
loss = pd.DataFrame(model.history.history)
loss.plot()
<Axes: >
prediction = (model.predict(x_test) > 0.5).astype('int32')
4/4 [==============================] - 0s 2ms/step
print(classification_report(y_pred=prediction, y_true=y_test))
precision recall f1-score support
0 0.95 0.98 0.97 43
1 0.99 0.97 0.98 71
accuracy 0.97 114
macro avg 0.97 0.97 0.97 114
weighted avg 0.97 0.97 0.97 114
ConfusionMatrixDisplay.from_predictions(y_true=y_test, y_pred=prediction)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1902467fed0>
model = Sequential()
model.add(Dense(30,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(15,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')
model.fit(x=x_train,y=y_train, epochs=600, validation_data=(x_test, y_test),
verbose=1, callbacks=[early_stop])
Epoch 1/600 15/15 [==============================] - 2s 21ms/step - loss: 0.6787 - val_loss: 0.6557 Epoch 2/600 15/15 [==============================] - 0s 7ms/step - loss: 0.6716 - val_loss: 0.6246 Epoch 3/600 15/15 [==============================] - 0s 7ms/step - loss: 0.6495 - val_loss: 0.5981 Epoch 4/600 15/15 [==============================] - 0s 6ms/step - loss: 0.6143 - val_loss: 0.5724 Epoch 5/600 15/15 [==============================] - 0s 7ms/step - loss: 0.5965 - val_loss: 0.5461 Epoch 6/600 15/15 [==============================] - 0s 7ms/step - loss: 0.5975 - val_loss: 0.5119 Epoch 7/600 15/15 [==============================] - 0s 6ms/step - loss: 0.5817 - val_loss: 0.4870 Epoch 8/600 15/15 [==============================] - 0s 6ms/step - loss: 0.5221 - val_loss: 0.4593 Epoch 9/600 15/15 [==============================] - 0s 6ms/step - loss: 0.5351 - val_loss: 0.4332 Epoch 10/600 15/15 [==============================] - 0s 6ms/step - loss: 0.5028 - val_loss: 0.4101 Epoch 11/600 15/15 [==============================] - 0s 6ms/step - loss: 0.4805 - val_loss: 0.3822 Epoch 12/600 15/15 [==============================] - 0s 6ms/step - loss: 0.4759 - val_loss: 0.3564 Epoch 13/600 15/15 [==============================] - 0s 7ms/step - loss: 0.4511 - val_loss: 0.3351 Epoch 14/600 15/15 [==============================] - 0s 8ms/step - loss: 0.4345 - val_loss: 0.3126 Epoch 15/600 15/15 [==============================] - 0s 6ms/step - loss: 0.3957 - val_loss: 0.2873 Epoch 16/600 15/15 [==============================] - 0s 6ms/step - loss: 0.3769 - val_loss: 0.2644 Epoch 17/600 15/15 [==============================] - 0s 5ms/step - loss: 0.3907 - val_loss: 0.2513 Epoch 18/600 15/15 [==============================] - 0s 5ms/step - loss: 0.3640 - val_loss: 0.2387 Epoch 19/600 15/15 [==============================] - 0s 8ms/step - loss: 0.3367 - val_loss: 0.2276 Epoch 20/600 15/15 [==============================] - 0s 5ms/step - loss: 0.3300 - val_loss: 0.2139 Epoch 21/600 15/15 [==============================] - 0s 6ms/step - loss: 0.3307 - val_loss: 0.2037 Epoch 22/600 15/15 [==============================] - 0s 5ms/step - loss: 0.3152 - val_loss: 0.1924 Epoch 23/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2892 - val_loss: 0.1849 Epoch 24/600 15/15 [==============================] - 0s 7ms/step - loss: 0.2881 - val_loss: 0.1785 Epoch 25/600 15/15 [==============================] - 0s 8ms/step - loss: 0.3079 - val_loss: 0.1710 Epoch 26/600 15/15 [==============================] - 0s 7ms/step - loss: 0.2856 - val_loss: 0.1648 Epoch 27/600 15/15 [==============================] - 0s 10ms/step - loss: 0.2720 - val_loss: 0.1592 Epoch 28/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2642 - val_loss: 0.1545 Epoch 29/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2598 - val_loss: 0.1515 Epoch 30/600 15/15 [==============================] - 0s 5ms/step - loss: 0.2625 - val_loss: 0.1383 Epoch 31/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2674 - val_loss: 0.1327 Epoch 32/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2608 - val_loss: 0.1347 Epoch 33/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2338 - val_loss: 0.1284 Epoch 34/600 15/15 [==============================] - 0s 7ms/step - loss: 0.2491 - val_loss: 0.1210 Epoch 35/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2267 - val_loss: 0.1163 Epoch 36/600 15/15 [==============================] - 0s 5ms/step - loss: 0.2444 - val_loss: 0.1172 Epoch 37/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2293 - val_loss: 0.1217 Epoch 38/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2149 - val_loss: 0.1165 Epoch 39/600 15/15 [==============================] - 0s 7ms/step - loss: 0.2077 - val_loss: 0.1081 Epoch 40/600 15/15 [==============================] - 0s 7ms/step - loss: 0.2237 - val_loss: 0.1066 Epoch 41/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2275 - val_loss: 0.1076 Epoch 42/600 15/15 [==============================] - 0s 6ms/step - loss: 0.2019 - val_loss: 0.1055 Epoch 43/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1975 - val_loss: 0.1030 Epoch 44/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1887 - val_loss: 0.0982 Epoch 45/600 15/15 [==============================] - 0s 9ms/step - loss: 0.1968 - val_loss: 0.0977 Epoch 46/600 15/15 [==============================] - 0s 8ms/step - loss: 0.1765 - val_loss: 0.0925 Epoch 47/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1925 - val_loss: 0.0932 Epoch 48/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1946 - val_loss: 0.0957 Epoch 49/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1919 - val_loss: 0.0945 Epoch 50/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1582 - val_loss: 0.0888 Epoch 51/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1488 - val_loss: 0.0843 Epoch 52/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1671 - val_loss: 0.0819 Epoch 53/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1699 - val_loss: 0.0844 Epoch 54/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1736 - val_loss: 0.0828 Epoch 55/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1580 - val_loss: 0.0803 Epoch 56/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1764 - val_loss: 0.0805 Epoch 57/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1614 - val_loss: 0.0792 Epoch 58/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1446 - val_loss: 0.0774 Epoch 59/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1639 - val_loss: 0.0774 Epoch 60/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1477 - val_loss: 0.0777 Epoch 61/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1810 - val_loss: 0.0776 Epoch 62/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1456 - val_loss: 0.0743 Epoch 63/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1418 - val_loss: 0.0731 Epoch 64/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1553 - val_loss: 0.0716 Epoch 65/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1360 - val_loss: 0.0815 Epoch 66/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1666 - val_loss: 0.0733 Epoch 67/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1412 - val_loss: 0.0706 Epoch 68/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1346 - val_loss: 0.0698 Epoch 69/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1601 - val_loss: 0.0703 Epoch 70/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1409 - val_loss: 0.0694 Epoch 71/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1433 - val_loss: 0.0685 Epoch 72/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1303 - val_loss: 0.0682 Epoch 73/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1276 - val_loss: 0.0681 Epoch 74/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1240 - val_loss: 0.0705 Epoch 75/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1160 - val_loss: 0.0664 Epoch 76/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1220 - val_loss: 0.0655 Epoch 77/600 15/15 [==============================] - 0s 8ms/step - loss: 0.1223 - val_loss: 0.0648 Epoch 78/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1191 - val_loss: 0.0633 Epoch 79/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1443 - val_loss: 0.0641 Epoch 80/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1383 - val_loss: 0.0653 Epoch 81/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1203 - val_loss: 0.0664 Epoch 82/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1260 - val_loss: 0.0664 Epoch 83/600 15/15 [==============================] - 0s 10ms/step - loss: 0.1182 - val_loss: 0.0660 Epoch 84/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1348 - val_loss: 0.0713 Epoch 85/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1176 - val_loss: 0.0657 Epoch 86/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1087 - val_loss: 0.0626 Epoch 87/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1277 - val_loss: 0.0630 Epoch 88/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1319 - val_loss: 0.0633 Epoch 89/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1057 - val_loss: 0.0646 Epoch 90/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1028 - val_loss: 0.0627 Epoch 91/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1076 - val_loss: 0.0631 Epoch 92/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1374 - val_loss: 0.0636 Epoch 93/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0936 - val_loss: 0.0648 Epoch 94/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1355 - val_loss: 0.0644 Epoch 95/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1179 - val_loss: 0.0631 Epoch 96/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1194 - val_loss: 0.0634 Epoch 97/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1114 - val_loss: 0.0635 Epoch 98/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0931 - val_loss: 0.0605 Epoch 99/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1103 - val_loss: 0.0587 Epoch 100/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0866 - val_loss: 0.0569 Epoch 101/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0905 - val_loss: 0.0592 Epoch 102/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1171 - val_loss: 0.0590 Epoch 103/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1004 - val_loss: 0.0579 Epoch 104/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1135 - val_loss: 0.0575 Epoch 105/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1004 - val_loss: 0.0611 Epoch 106/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1032 - val_loss: 0.0579 Epoch 107/600 15/15 [==============================] - 0s 5ms/step - loss: 0.1068 - val_loss: 0.0580 Epoch 108/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1104 - val_loss: 0.0591 Epoch 109/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1064 - val_loss: 0.0642 Epoch 110/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1174 - val_loss: 0.0585 Epoch 111/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0872 - val_loss: 0.0585 Epoch 112/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0984 - val_loss: 0.0574 Epoch 113/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0990 - val_loss: 0.0567 Epoch 114/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0932 - val_loss: 0.0574 Epoch 115/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0916 - val_loss: 0.0611 Epoch 116/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1223 - val_loss: 0.0626 Epoch 117/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0763 - val_loss: 0.0580 Epoch 118/600 15/15 [==============================] - 0s 9ms/step - loss: 0.0950 - val_loss: 0.0590 Epoch 119/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1011 - val_loss: 0.0584 Epoch 120/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0825 - val_loss: 0.0574 Epoch 121/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1054 - val_loss: 0.0569 Epoch 122/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0946 - val_loss: 0.0647 Epoch 123/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1097 - val_loss: 0.0591 Epoch 124/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0815 - val_loss: 0.0585 Epoch 125/600 15/15 [==============================] - 0s 7ms/step - loss: 0.1078 - val_loss: 0.0555 Epoch 126/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0787 - val_loss: 0.0525 Epoch 127/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0972 - val_loss: 0.0573 Epoch 128/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0954 - val_loss: 0.0526 Epoch 129/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0855 - val_loss: 0.0556 Epoch 130/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0934 - val_loss: 0.0561 Epoch 131/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0919 - val_loss: 0.0555 Epoch 132/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0912 - val_loss: 0.0560 Epoch 133/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0969 - val_loss: 0.0558 Epoch 134/600 15/15 [==============================] - 0s 6ms/step - loss: 0.1081 - val_loss: 0.0547 Epoch 135/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0838 - val_loss: 0.0582 Epoch 136/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0960 - val_loss: 0.0589 Epoch 137/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0873 - val_loss: 0.0554 Epoch 138/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0683 - val_loss: 0.0551 Epoch 139/600 15/15 [==============================] - 0s 5ms/step - loss: 0.0814 - val_loss: 0.0548 Epoch 140/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0794 - val_loss: 0.0553 Epoch 141/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0882 - val_loss: 0.0570 Epoch 142/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0893 - val_loss: 0.0558 Epoch 143/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0835 - val_loss: 0.0573 Epoch 144/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0774 - val_loss: 0.0634 Epoch 145/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0745 - val_loss: 0.0585 Epoch 146/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0745 - val_loss: 0.0586 Epoch 147/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0729 - val_loss: 0.0617 Epoch 148/600 15/15 [==============================] - 0s 7ms/step - loss: 0.0853 - val_loss: 0.0551 Epoch 149/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0900 - val_loss: 0.0576 Epoch 150/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0831 - val_loss: 0.0615 Epoch 151/600 15/15 [==============================] - 0s 6ms/step - loss: 0.0883 - val_loss: 0.0608 Epoch 151: early stopping
<keras.callbacks.History at 0x190246acdd0>
loss = pd.DataFrame(model.history.history)
loss.plot()
<Axes: >
prediction = (model.predict(x_test) > 0.5).astype('int32')
4/4 [==============================] - 0s 3ms/step
from sklearn.metrics import classification_report, ConfusionMatrixDisplay
print(classification_report(y_true=y_test, y_pred=prediction))
precision recall f1-score support
0 0.98 0.95 0.96 43
1 0.97 0.99 0.98 71
accuracy 0.97 114
macro avg 0.97 0.97 0.97 114
weighted avg 0.97 0.97 0.97 114
ConfusionMatrixDisplay.from_predictions(y_true=y_test, y_pred=prediction)
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x190248bf710>
