142 KiB
142 KiB
Import bibliotek
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras import regularizers
import numpy as np
import pandas as pd
import opendatasets as od
import chardet
import matplotlib.pyplot as pltPobranie zbioru danych z kaggle
od.download('https://www.kaggle.com/datasets/jjayfabor/lettuce-growth-days')
#{"username":"jakubbg","key":"e42b293c818e4ecd7b9365ee037af428"}Skipping, found downloaded files in ".\lettuce-growth-days" (use force=True to force download)
Załadowanie zbioru danych
with open('./lettuce-growth-days/lettuce_dataset_updated.csv', 'rb') as f:
result = chardet.detect(f.read())dataset = pd.read_csv('./lettuce-growth-days/lettuce_dataset_updated.csv', encoding=result['encoding'])print(len(dataset))
print(dataset[:5])3169 Plant_ID Date Temperature (°C) Humidity (%) TDS Value (ppm) \ 0 1 8/3/2023 33.4 53 582 1 1 8/4/2023 33.5 53 451 2 1 8/5/2023 33.4 59 678 3 1 8/6/2023 33.4 68 420 4 1 8/7/2023 33.4 74 637 pH Level Growth Days Temperature (F) Humidity 0 6.4 1 92.12 0.53 1 6.1 2 92.30 0.53 2 6.4 3 92.12 0.59 3 6.4 4 92.12 0.68 4 6.5 5 92.12 0.74
Wyciąganie ze zbioru wybranych kolumn
ph_level = dataset['pH Level'].values.tolist()
temp_F = dataset['Temperature (F)'].values.tolist()
humid = dataset['Humidity'].values.tolist()
days = dataset['Growth Days'].values.tolist()
plant_id = dataset['Plant_ID'].values.tolist()Przetwarzanie danych do postaci zbioru X i Y
Każda próbka składać się będzie ze średniej temperatury (F), średniej wilgotności oraz średniego ph gleby dla danej rośliny oraz z przypisanej jej klasy będącej ilością dni, jakie były wymagane do całkowitego wyrośnięcia rośliny.
X = []
Y = []
id = plant_id[0]
temp_sum = 0
humid_sum = 0
ph_level_sum = 0
day = 1
for i in range(0, len(plant_id)):
if plant_id[i] == id:
temp_sum += temp_F[i]
humid_sum += humid[i]
ph_level_sum += ph_level[i]
day = days[i]
else:
temp = []
temp.append(temp_sum/day)
temp.append(humid_sum/day)
temp.append(ph_level_sum/day)
X.append(temp)
Y.append(day)
temp_sum = 0
humid_sum = 0
ph_level_sum = 0
day = 1
id = plant_id[i]print(X[:10])[[87.13199999999998, 0.6395555555555558, 6.382222222222223], [85.08488888888886, 0.6295555555555553, 6.311111111111113], [85.13148936170211, 0.6759574468085107, 6.27659574468085], [85.24333333333331, 0.6206250000000001, 6.293749999999999], [85.08488888888886, 0.6357777777777776, 6.264444444444444], [85.08488888888886, 0.6295555555555555, 6.297777777777776], [85.10851063829786, 0.625531914893617, 6.285106382978723], [85.08488888888886, 0.6457777777777778, 6.266666666666666], [85.15565217391303, 0.6530434782608696, 6.2195652173913025], [85.08488888888886, 0.6435555555555555, 6.262222222222222]]
Normalizacja danych
scaler = MinMaxScaler()
X = scaler.fit_transform(X)X = np.array(X)
Y = np.array(Y)
encoder = OneHotEncoder(sparse=False)
y_onehot = encoder.fit_transform(Y.reshape(-1,1))
X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.4, random_state=42)
print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)(41, 3) (28, 3) (41, 4) (28, 4)
C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing\_encoders.py:808: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value. warnings.warn(
Budowanie modelu sieci neuronowej
model = Sequential([
Dense(8, activation='relu', input_dim=3, kernel_regularizer=regularizers.l2(0.04)),
Dropout(0.5),
Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.04)),
Dropout(0.5),
Dense(4, activation='softmax', kernel_regularizer=regularizers.l2(0.04)),
])C:\Users\obses\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\src\layers\core\dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
model.compile(optimizer='sgd',
loss='categorical_crossentropy',
metrics=['accuracy'])Trenowanie modelu
history = model.fit(X_train, y_train, epochs=500, validation_data=(X_test, y_test), verbose=2)
test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=2)
print(f"Dokładność testowa: {test_accuracy:.2%}")Epoch 1/500 2/2 - 1s - 265ms/step - accuracy: 0.3902 - loss: 2.1238 - val_accuracy: 0.1429 - val_loss: 2.1243 Epoch 2/500 2/2 - 0s - 20ms/step - accuracy: 0.2439 - loss: 2.1641 - val_accuracy: 0.2500 - val_loss: 2.1104 Epoch 3/500 2/2 - 0s - 20ms/step - accuracy: 0.3902 - loss: 2.1102 - val_accuracy: 0.5714 - val_loss: 2.0970 Epoch 4/500 2/2 - 0s - 23ms/step - accuracy: 0.4146 - loss: 2.0846 - val_accuracy: 0.7857 - val_loss: 2.0847 Epoch 5/500 2/2 - 0s - 21ms/step - accuracy: 0.5854 - loss: 2.1145 - val_accuracy: 0.8571 - val_loss: 2.0710 Epoch 6/500 2/2 - 0s - 20ms/step - accuracy: 0.6585 - loss: 2.0605 - val_accuracy: 0.8571 - val_loss: 2.0591 Epoch 7/500 2/2 - 0s - 21ms/step - accuracy: 0.6829 - loss: 2.0652 - val_accuracy: 0.8571 - val_loss: 2.0459 Epoch 8/500 2/2 - 0s - 23ms/step - accuracy: 0.6585 - loss: 2.0399 - val_accuracy: 0.8571 - val_loss: 2.0329 Epoch 9/500 2/2 - 0s - 22ms/step - accuracy: 0.8049 - loss: 2.0242 - val_accuracy: 0.8571 - val_loss: 2.0210 Epoch 10/500 2/2 - 0s - 23ms/step - accuracy: 0.6829 - loss: 2.0091 - val_accuracy: 0.8571 - val_loss: 2.0091 Epoch 11/500 2/2 - 0s - 21ms/step - accuracy: 0.8049 - loss: 1.9942 - val_accuracy: 0.8571 - val_loss: 1.9976 Epoch 12/500 2/2 - 0s - 20ms/step - accuracy: 0.7561 - loss: 1.9887 - val_accuracy: 0.8571 - val_loss: 1.9867 Epoch 13/500 2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.9710 - val_accuracy: 0.8571 - val_loss: 1.9748 Epoch 14/500 2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.9850 - val_accuracy: 0.8571 - val_loss: 1.9635 Epoch 15/500 2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.9553 - val_accuracy: 0.8571 - val_loss: 1.9527 Epoch 16/500 2/2 - 0s - 21ms/step - accuracy: 0.7805 - loss: 1.9349 - val_accuracy: 0.8571 - val_loss: 1.9419 Epoch 17/500 2/2 - 0s - 20ms/step - accuracy: 0.7561 - loss: 1.9401 - val_accuracy: 0.8571 - val_loss: 1.9307 Epoch 18/500 2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.9213 - val_accuracy: 0.8571 - val_loss: 1.9209 Epoch 19/500 2/2 - 0s - 21ms/step - accuracy: 0.8293 - loss: 1.9173 - val_accuracy: 0.8571 - val_loss: 1.9100 Epoch 20/500 2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.8995 - val_accuracy: 0.8571 - val_loss: 1.8995 Epoch 21/500 2/2 - 0s - 21ms/step - accuracy: 0.8780 - loss: 1.8758 - val_accuracy: 0.8571 - val_loss: 1.8900 Epoch 22/500 2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.8806 - val_accuracy: 0.8571 - val_loss: 1.8796 Epoch 23/500 2/2 - 0s - 24ms/step - accuracy: 0.8293 - loss: 1.8815 - val_accuracy: 0.8571 - val_loss: 1.8693 Epoch 24/500 2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.8560 - val_accuracy: 0.8571 - val_loss: 1.8589 Epoch 25/500 2/2 - 0s - 20ms/step - accuracy: 0.7805 - loss: 1.8495 - val_accuracy: 0.8571 - val_loss: 1.8491 Epoch 26/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.8245 - val_accuracy: 0.8571 - val_loss: 1.8394 Epoch 27/500 2/2 - 0s - 23ms/step - accuracy: 0.8049 - loss: 1.8372 - val_accuracy: 0.8571 - val_loss: 1.8294 Epoch 28/500 2/2 - 0s - 25ms/step - accuracy: 0.7805 - loss: 1.8236 - val_accuracy: 0.8571 - val_loss: 1.8191 Epoch 29/500 2/2 - 0s - 22ms/step - accuracy: 0.8293 - loss: 1.8232 - val_accuracy: 0.8571 - val_loss: 1.8090 Epoch 30/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.7873 - val_accuracy: 0.8571 - val_loss: 1.8008 Epoch 31/500 2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.7862 - val_accuracy: 0.8571 - val_loss: 1.7918 Epoch 32/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.8029 - val_accuracy: 0.8571 - val_loss: 1.7828 Epoch 33/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.7658 - val_accuracy: 0.8571 - val_loss: 1.7734 Epoch 34/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.7646 - val_accuracy: 0.8571 - val_loss: 1.7637 Epoch 35/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.7255 - val_accuracy: 0.8571 - val_loss: 1.7539 Epoch 36/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.7385 - val_accuracy: 0.8571 - val_loss: 1.7438 Epoch 37/500 2/2 - 0s - 20ms/step - accuracy: 0.8293 - loss: 1.7172 - val_accuracy: 0.8571 - val_loss: 1.7340 Epoch 38/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.7158 - val_accuracy: 0.8571 - val_loss: 1.7246 Epoch 39/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.7122 - val_accuracy: 0.8571 - val_loss: 1.7162 Epoch 40/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.7141 - val_accuracy: 0.8571 - val_loss: 1.7086 Epoch 41/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6996 - val_accuracy: 0.8571 - val_loss: 1.6996 Epoch 42/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.6795 - val_accuracy: 0.8571 - val_loss: 1.6900 Epoch 43/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6662 - val_accuracy: 0.8571 - val_loss: 1.6833 Epoch 44/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.6729 - val_accuracy: 0.8571 - val_loss: 1.6753 Epoch 45/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6760 - val_accuracy: 0.8571 - val_loss: 1.6665 Epoch 46/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6566 - val_accuracy: 0.8571 - val_loss: 1.6581 Epoch 47/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6475 - val_accuracy: 0.8571 - val_loss: 1.6501 Epoch 48/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6537 - val_accuracy: 0.8571 - val_loss: 1.6430 Epoch 49/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.6250 - val_accuracy: 0.8571 - val_loss: 1.6356 Epoch 50/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6135 - val_accuracy: 0.8571 - val_loss: 1.6266 Epoch 51/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.6359 - val_accuracy: 0.8571 - val_loss: 1.6192 Epoch 52/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.5970 - val_accuracy: 0.8571 - val_loss: 1.6120 Epoch 53/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.6041 - val_accuracy: 0.8571 - val_loss: 1.6046 Epoch 54/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5999 - val_accuracy: 0.8571 - val_loss: 1.5963 Epoch 55/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 1.5691 - val_accuracy: 0.8571 - val_loss: 1.5882 Epoch 56/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5940 - val_accuracy: 0.8571 - val_loss: 1.5820 Epoch 57/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.5604 - val_accuracy: 0.8571 - val_loss: 1.5755 Epoch 58/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5783 - val_accuracy: 0.8571 - val_loss: 1.5676 Epoch 59/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5775 - val_accuracy: 0.8571 - val_loss: 1.5591 Epoch 60/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5195 - val_accuracy: 0.8571 - val_loss: 1.5509 Epoch 61/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5291 - val_accuracy: 0.8571 - val_loss: 1.5442 Epoch 62/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5216 - val_accuracy: 0.8571 - val_loss: 1.5362 Epoch 63/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5330 - val_accuracy: 0.8571 - val_loss: 1.5293 Epoch 64/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5344 - val_accuracy: 0.8571 - val_loss: 1.5228 Epoch 65/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5161 - val_accuracy: 0.8571 - val_loss: 1.5158 Epoch 66/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4849 - val_accuracy: 0.8571 - val_loss: 1.5081 Epoch 67/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.5029 - val_accuracy: 0.8571 - val_loss: 1.5009 Epoch 68/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4785 - val_accuracy: 0.8571 - val_loss: 1.4936 Epoch 69/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.5226 - val_accuracy: 0.8571 - val_loss: 1.4871 Epoch 70/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4863 - val_accuracy: 0.8571 - val_loss: 1.4801 Epoch 71/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4651 - val_accuracy: 0.8571 - val_loss: 1.4741 Epoch 72/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4743 - val_accuracy: 0.8571 - val_loss: 1.4673 Epoch 73/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4487 - val_accuracy: 0.8571 - val_loss: 1.4599 Epoch 74/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4422 - val_accuracy: 0.8571 - val_loss: 1.4536 Epoch 75/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4413 - val_accuracy: 0.8571 - val_loss: 1.4475 Epoch 76/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4394 - val_accuracy: 0.8571 - val_loss: 1.4413 Epoch 77/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.4215 - val_accuracy: 0.8571 - val_loss: 1.4355 Epoch 78/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4270 - val_accuracy: 0.8571 - val_loss: 1.4285 Epoch 79/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.4395 - val_accuracy: 0.8571 - val_loss: 1.4219 Epoch 80/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.4311 - val_accuracy: 0.8571 - val_loss: 1.4154 Epoch 81/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4002 - val_accuracy: 0.8571 - val_loss: 1.4086 Epoch 82/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.4201 - val_accuracy: 0.8571 - val_loss: 1.4030 Epoch 83/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3839 - val_accuracy: 0.8571 - val_loss: 1.3973 Epoch 84/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.4089 - val_accuracy: 0.8571 - val_loss: 1.3913 Epoch 85/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3913 - val_accuracy: 0.8571 - val_loss: 1.3845 Epoch 86/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3826 - val_accuracy: 0.8571 - val_loss: 1.3788 Epoch 87/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3724 - val_accuracy: 0.8571 - val_loss: 1.3733 Epoch 88/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3838 - val_accuracy: 0.8571 - val_loss: 1.3679 Epoch 89/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3502 - val_accuracy: 0.8571 - val_loss: 1.3624 Epoch 90/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.3619 - val_accuracy: 0.8571 - val_loss: 1.3573 Epoch 91/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3734 - val_accuracy: 0.8571 - val_loss: 1.3538 Epoch 92/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3499 - val_accuracy: 0.8571 - val_loss: 1.3479 Epoch 93/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3392 - val_accuracy: 0.8571 - val_loss: 1.3426 Epoch 94/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3334 - val_accuracy: 0.8571 - val_loss: 1.3370 Epoch 95/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3347 - val_accuracy: 0.8571 - val_loss: 1.3315 Epoch 96/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.3316 - val_accuracy: 0.8571 - val_loss: 1.3265 Epoch 97/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3638 - val_accuracy: 0.8571 - val_loss: 1.3228 Epoch 98/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3231 - val_accuracy: 0.8571 - val_loss: 1.3187 Epoch 99/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3332 - val_accuracy: 0.8571 - val_loss: 1.3138 Epoch 100/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.3142 - val_accuracy: 0.8571 - val_loss: 1.3088 Epoch 101/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3083 - val_accuracy: 0.8571 - val_loss: 1.3047 Epoch 102/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2839 - val_accuracy: 0.8571 - val_loss: 1.2998 Epoch 103/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2872 - val_accuracy: 0.8571 - val_loss: 1.2956 Epoch 104/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.3188 - val_accuracy: 0.8571 - val_loss: 1.2922 Epoch 105/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2763 - val_accuracy: 0.8571 - val_loss: 1.2873 Epoch 106/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2957 - val_accuracy: 0.8571 - val_loss: 1.2831 Epoch 107/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 1.2953 - val_accuracy: 0.8571 - val_loss: 1.2787 Epoch 108/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2710 - val_accuracy: 0.8571 - val_loss: 1.2737 Epoch 109/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2953 - val_accuracy: 0.8571 - val_loss: 1.2693 Epoch 110/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2716 - val_accuracy: 0.8571 - val_loss: 1.2660 Epoch 111/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2748 - val_accuracy: 0.8571 - val_loss: 1.2631 Epoch 112/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2488 - val_accuracy: 0.8571 - val_loss: 1.2585 Epoch 113/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2603 - val_accuracy: 0.8571 - val_loss: 1.2538 Epoch 114/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2413 - val_accuracy: 0.8571 - val_loss: 1.2496 Epoch 115/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2690 - val_accuracy: 0.8571 - val_loss: 1.2452 Epoch 116/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2510 - val_accuracy: 0.8571 - val_loss: 1.2411 Epoch 117/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2378 - val_accuracy: 0.8571 - val_loss: 1.2377 Epoch 118/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2281 - val_accuracy: 0.8571 - val_loss: 1.2339 Epoch 119/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2639 - val_accuracy: 0.8571 - val_loss: 1.2304 Epoch 120/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2622 - val_accuracy: 0.8571 - val_loss: 1.2262 Epoch 121/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.2354 - val_accuracy: 0.8571 - val_loss: 1.2230 Epoch 122/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.2722 - val_accuracy: 0.8571 - val_loss: 1.2196 Epoch 123/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2421 - val_accuracy: 0.8571 - val_loss: 1.2153 Epoch 124/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2292 - val_accuracy: 0.8571 - val_loss: 1.2126 Epoch 125/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.2138 - val_accuracy: 0.8571 - val_loss: 1.2092 Epoch 126/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1895 - val_accuracy: 0.8571 - val_loss: 1.2055 Epoch 127/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2061 - val_accuracy: 0.8571 - val_loss: 1.2014 Epoch 128/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1968 - val_accuracy: 0.8571 - val_loss: 1.1979 Epoch 129/500 2/2 - 0s - 22ms/step - 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loss: 1.1911 - val_accuracy: 0.8571 - val_loss: 1.1646 Epoch 139/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1821 - val_accuracy: 0.8571 - val_loss: 1.1611 Epoch 140/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1455 - val_accuracy: 0.8571 - val_loss: 1.1576 Epoch 141/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1711 - val_accuracy: 0.8571 - val_loss: 1.1544 Epoch 142/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1591 - val_accuracy: 0.8571 - val_loss: 1.1509 Epoch 143/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1675 - val_accuracy: 0.8571 - val_loss: 1.1474 Epoch 144/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1860 - val_accuracy: 0.8571 - val_loss: 1.1444 Epoch 145/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1656 - val_accuracy: 0.8571 - val_loss: 1.1419 Epoch 146/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1621 - val_accuracy: 0.8571 - val_loss: 1.1386 Epoch 147/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1650 - 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loss: 1.0515 - val_accuracy: 0.8571 - val_loss: 1.0314 Epoch 194/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0273 - val_accuracy: 0.8571 - val_loss: 1.0289 Epoch 195/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0470 - val_accuracy: 0.8571 - val_loss: 1.0268 Epoch 196/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0527 - val_accuracy: 0.8571 - val_loss: 1.0246 Epoch 197/500 2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 1.0460 - val_accuracy: 0.8571 - val_loss: 1.0228 Epoch 198/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0396 - val_accuracy: 0.8571 - val_loss: 1.0212 Epoch 199/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0383 - val_accuracy: 0.8571 - val_loss: 1.0199 Epoch 200/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0257 - val_accuracy: 0.8571 - val_loss: 1.0177 Epoch 201/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0097 - val_accuracy: 0.8571 - val_loss: 1.0159 Epoch 202/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9978 - 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val_loss: 0.9984 Epoch 212/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9847 - val_accuracy: 0.8571 - val_loss: 0.9963 Epoch 213/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9929 - val_accuracy: 0.8571 - val_loss: 0.9942 Epoch 214/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9696 - val_accuracy: 0.8571 - val_loss: 0.9922 Epoch 215/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0115 - val_accuracy: 0.8571 - val_loss: 0.9909 Epoch 216/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0426 - val_accuracy: 0.8571 - val_loss: 0.9896 Epoch 217/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0355 - val_accuracy: 0.8571 - val_loss: 0.9878 Epoch 218/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0111 - val_accuracy: 0.8571 - val_loss: 0.9862 Epoch 219/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0047 - val_accuracy: 0.8571 - val_loss: 0.9850 Epoch 220/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9901 - val_accuracy: 0.8571 - val_loss: 0.9831 Epoch 221/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9875 - val_accuracy: 0.8571 - val_loss: 0.9815 Epoch 222/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9783 - val_accuracy: 0.8571 - val_loss: 0.9796 Epoch 223/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0097 - val_accuracy: 0.8571 - val_loss: 0.9780 Epoch 224/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9895 - val_accuracy: 0.8571 - val_loss: 0.9764 Epoch 225/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0003 - val_accuracy: 0.8571 - val_loss: 0.9752 Epoch 226/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0074 - val_accuracy: 0.8571 - val_loss: 0.9736 Epoch 227/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9722 - val_accuracy: 0.8571 - val_loss: 0.9722 Epoch 228/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9806 - val_accuracy: 0.8571 - val_loss: 0.9703 Epoch 229/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9936 - val_accuracy: 0.8571 - val_loss: 0.9686 Epoch 230/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9889 - val_accuracy: 0.8571 - val_loss: 0.9668 Epoch 231/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9880 - val_accuracy: 0.8571 - val_loss: 0.9654 Epoch 232/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9783 - val_accuracy: 0.8571 - val_loss: 0.9638 Epoch 233/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9825 - val_accuracy: 0.8571 - val_loss: 0.9623 Epoch 234/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9876 - val_accuracy: 0.8571 - val_loss: 0.9615 Epoch 235/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0037 - val_accuracy: 0.8571 - val_loss: 0.9605 Epoch 236/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9587 - val_accuracy: 0.8571 - val_loss: 0.9587 Epoch 237/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9992 - val_accuracy: 0.8571 - val_loss: 0.9576 Epoch 238/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9783 - val_accuracy: 0.8571 - val_loss: 0.9562 Epoch 239/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0140 - val_accuracy: 0.8571 - val_loss: 0.9549 Epoch 240/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9395 - val_accuracy: 0.8571 - val_loss: 0.9534 Epoch 241/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9933 - val_accuracy: 0.8571 - val_loss: 0.9526 Epoch 242/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9540 - val_accuracy: 0.8571 - val_loss: 0.9511 Epoch 243/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9409 - val_accuracy: 0.8571 - val_loss: 0.9498 Epoch 244/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9850 - val_accuracy: 0.8571 - val_loss: 0.9487 Epoch 245/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9520 - val_accuracy: 0.8571 - val_loss: 0.9474 Epoch 246/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9655 - val_accuracy: 0.8571 - val_loss: 0.9462 Epoch 247/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9951 - val_accuracy: 0.8571 - val_loss: 0.9448 Epoch 248/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9752 - val_accuracy: 0.8571 - val_loss: 0.9434 Epoch 249/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9690 - val_accuracy: 0.8571 - val_loss: 0.9419 Epoch 250/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9625 - val_accuracy: 0.8571 - val_loss: 0.9407 Epoch 251/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9777 - val_accuracy: 0.8571 - val_loss: 0.9396 Epoch 252/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9895 - val_accuracy: 0.8571 - val_loss: 0.9383 Epoch 253/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9370 - val_accuracy: 0.8571 - val_loss: 0.9368 Epoch 254/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9518 - val_accuracy: 0.8571 - val_loss: 0.9352 Epoch 255/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.9588 - val_accuracy: 0.8571 - val_loss: 0.9337 Epoch 256/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9775 - val_accuracy: 0.8571 - val_loss: 0.9326 Epoch 257/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9639 - 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val_loss: 0.9195 Epoch 267/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9419 - val_accuracy: 0.8571 - val_loss: 0.9182 Epoch 268/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9278 - val_accuracy: 0.8571 - val_loss: 0.9171 Epoch 269/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9086 - val_accuracy: 0.8571 - val_loss: 0.9159 Epoch 270/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9250 - val_accuracy: 0.8571 - val_loss: 0.9145 Epoch 271/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9115 - val_accuracy: 0.8571 - val_loss: 0.9132 Epoch 272/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9435 - val_accuracy: 0.8571 - val_loss: 0.9118 Epoch 273/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9561 - val_accuracy: 0.8571 - val_loss: 0.9108 Epoch 274/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9403 - val_accuracy: 0.8571 - val_loss: 0.9099 Epoch 275/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9595 - val_accuracy: 0.8571 - val_loss: 0.9086 Epoch 276/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9120 - val_accuracy: 0.8571 - val_loss: 0.9076 Epoch 277/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9327 - val_accuracy: 0.8571 - val_loss: 0.9065 Epoch 278/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9364 - val_accuracy: 0.8571 - val_loss: 0.9056 Epoch 279/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9078 - val_accuracy: 0.8571 - val_loss: 0.9044 Epoch 280/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8879 - val_accuracy: 0.8571 - val_loss: 0.9031 Epoch 281/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9402 - val_accuracy: 0.8571 - val_loss: 0.9023 Epoch 282/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9134 - val_accuracy: 0.8571 - val_loss: 0.9009 Epoch 283/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9364 - val_accuracy: 0.8571 - val_loss: 0.8997 Epoch 284/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8979 - val_accuracy: 0.8571 - val_loss: 0.8985 Epoch 285/500 2/2 - 0s - 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accuracy: 0.8537 - loss: 0.9204 - val_accuracy: 0.8571 - val_loss: 0.8879 Epoch 295/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8734 - val_accuracy: 0.8571 - val_loss: 0.8866 Epoch 296/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9355 - val_accuracy: 0.8571 - val_loss: 0.8856 Epoch 297/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8829 - val_accuracy: 0.8571 - val_loss: 0.8843 Epoch 298/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8882 - val_accuracy: 0.8571 - val_loss: 0.8831 Epoch 299/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9170 - val_accuracy: 0.8571 - val_loss: 0.8821 Epoch 300/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8941 - val_accuracy: 0.8571 - val_loss: 0.8812 Epoch 301/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8746 - val_accuracy: 0.8571 - val_loss: 0.8801 Epoch 302/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8817 - val_accuracy: 0.8571 - val_loss: 0.8789 Epoch 303/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9101 - val_accuracy: 0.8571 - val_loss: 0.8777 Epoch 304/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8715 - val_accuracy: 0.8571 - val_loss: 0.8766 Epoch 305/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.8919 - val_accuracy: 0.8571 - val_loss: 0.8756 Epoch 306/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9159 - val_accuracy: 0.8571 - val_loss: 0.8745 Epoch 307/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9122 - val_accuracy: 0.8571 - val_loss: 0.8736 Epoch 308/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8950 - val_accuracy: 0.8571 - val_loss: 0.8726 Epoch 309/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8599 - val_accuracy: 0.8571 - val_loss: 0.8716 Epoch 310/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9009 - val_accuracy: 0.8571 - val_loss: 0.8705 Epoch 311/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8738 - val_accuracy: 0.8571 - val_loss: 0.8694 Epoch 312/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8587 - val_accuracy: 0.8571 - val_loss: 0.8682 Epoch 313/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8846 - val_accuracy: 0.8571 - val_loss: 0.8674 Epoch 314/500 2/2 - 0s - 28ms/step - accuracy: 0.8537 - loss: 0.8935 - val_accuracy: 0.8571 - val_loss: 0.8664 Epoch 315/500 2/2 - 0s - 30ms/step - accuracy: 0.8537 - loss: 0.8979 - val_accuracy: 0.8571 - val_loss: 0.8655 Epoch 316/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8693 - val_accuracy: 0.8571 - val_loss: 0.8644 Epoch 317/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8792 - val_accuracy: 0.8571 - val_loss: 0.8633 Epoch 318/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8820 - val_accuracy: 0.8571 - val_loss: 0.8626 Epoch 319/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8652 - val_accuracy: 0.8571 - val_loss: 0.8614 Epoch 320/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8814 - val_accuracy: 0.8571 - val_loss: 0.8605 Epoch 321/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8621 - val_accuracy: 0.8571 - val_loss: 0.8594 Epoch 322/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8744 - val_accuracy: 0.8571 - val_loss: 0.8585 Epoch 323/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8805 - val_accuracy: 0.8571 - val_loss: 0.8575 Epoch 324/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8818 - val_accuracy: 0.8571 - val_loss: 0.8568 Epoch 325/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8989 - val_accuracy: 0.8571 - val_loss: 0.8560 Epoch 326/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8706 - val_accuracy: 0.8571 - val_loss: 0.8548 Epoch 327/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8623 - val_accuracy: 0.8571 - val_loss: 0.8539 Epoch 328/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8570 - val_accuracy: 0.8571 - val_loss: 0.8529 Epoch 329/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8858 - val_accuracy: 0.8571 - val_loss: 0.8519 Epoch 330/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8571 - val_accuracy: 0.8571 - val_loss: 0.8512 Epoch 331/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8839 - val_accuracy: 0.8571 - val_loss: 0.8503 Epoch 332/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8556 - val_accuracy: 0.8571 - val_loss: 0.8492 Epoch 333/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8764 - val_accuracy: 0.8571 - val_loss: 0.8482 Epoch 334/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8353 - val_accuracy: 0.8571 - val_loss: 0.8470 Epoch 335/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8659 - val_accuracy: 0.8571 - val_loss: 0.8461 Epoch 336/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8745 - val_accuracy: 0.8571 - val_loss: 0.8452 Epoch 337/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8465 - val_accuracy: 0.8571 - val_loss: 0.8444 Epoch 338/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8740 - val_accuracy: 0.8571 - val_loss: 0.8434 Epoch 339/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8760 - val_accuracy: 0.8571 - val_loss: 0.8423 Epoch 340/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8553 - val_accuracy: 0.8571 - val_loss: 0.8415 Epoch 341/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8460 - val_accuracy: 0.8571 - val_loss: 0.8406 Epoch 342/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8487 - val_accuracy: 0.8571 - val_loss: 0.8397 Epoch 343/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8667 - val_accuracy: 0.8571 - val_loss: 0.8387 Epoch 344/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8513 - val_accuracy: 0.8571 - val_loss: 0.8378 Epoch 345/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8692 - val_accuracy: 0.8571 - val_loss: 0.8371 Epoch 346/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8271 - val_accuracy: 0.8571 - val_loss: 0.8360 Epoch 347/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8738 - val_accuracy: 0.8571 - val_loss: 0.8353 Epoch 348/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8613 - val_accuracy: 0.8571 - val_loss: 0.8344 Epoch 349/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8322 - val_accuracy: 0.8571 - val_loss: 0.8334 Epoch 350/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8745 - val_accuracy: 0.8571 - val_loss: 0.8325 Epoch 351/500 2/2 - 0s - 26ms/step - accuracy: 0.8537 - loss: 0.8528 - val_accuracy: 0.8571 - val_loss: 0.8316 Epoch 352/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8418 - val_accuracy: 0.8571 - val_loss: 0.8306 Epoch 353/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8466 - val_accuracy: 0.8571 - val_loss: 0.8296 Epoch 354/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8299 - val_accuracy: 0.8571 - val_loss: 0.8287 Epoch 355/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8493 - val_accuracy: 0.8571 - val_loss: 0.8277 Epoch 356/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8468 - val_accuracy: 0.8571 - val_loss: 0.8267 Epoch 357/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8561 - val_accuracy: 0.8571 - val_loss: 0.8261 Epoch 358/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8319 - val_accuracy: 0.8571 - val_loss: 0.8252 Epoch 359/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8462 - val_accuracy: 0.8571 - val_loss: 0.8244 Epoch 360/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8342 - val_accuracy: 0.8571 - val_loss: 0.8235 Epoch 361/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8424 - val_accuracy: 0.8571 - val_loss: 0.8225 Epoch 362/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8315 - val_accuracy: 0.8571 - val_loss: 0.8216 Epoch 363/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8650 - val_accuracy: 0.8571 - val_loss: 0.8210 Epoch 364/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8644 - val_accuracy: 0.8571 - val_loss: 0.8201 Epoch 365/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8452 - val_accuracy: 0.8571 - val_loss: 0.8193 Epoch 366/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8463 - val_accuracy: 0.8571 - val_loss: 0.8186 Epoch 367/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8374 - val_accuracy: 0.8571 - val_loss: 0.8176 Epoch 368/500 2/2 - 0s - 17ms/step - accuracy: 0.8537 - loss: 0.8477 - val_accuracy: 0.8571 - val_loss: 0.8168 Epoch 369/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8179 - val_accuracy: 0.8571 - val_loss: 0.8159 Epoch 370/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7949 - val_accuracy: 0.8571 - val_loss: 0.8150 Epoch 371/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8133 - val_accuracy: 0.8571 - val_loss: 0.8141 Epoch 372/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8204 - val_accuracy: 0.8571 - val_loss: 0.8132 Epoch 373/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8575 - val_accuracy: 0.8571 - val_loss: 0.8124 Epoch 374/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8066 - val_accuracy: 0.8571 - val_loss: 0.8115 Epoch 375/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8284 - val_accuracy: 0.8571 - val_loss: 0.8106 Epoch 376/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8040 - val_accuracy: 0.8571 - val_loss: 0.8097 Epoch 377/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8243 - val_accuracy: 0.8571 - val_loss: 0.8091 Epoch 378/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8145 - val_accuracy: 0.8571 - val_loss: 0.8084 Epoch 379/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8263 - val_accuracy: 0.8571 - val_loss: 0.8075 Epoch 380/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8289 - val_accuracy: 0.8571 - val_loss: 0.8068 Epoch 381/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8228 - val_accuracy: 0.8571 - val_loss: 0.8059 Epoch 382/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8157 - val_accuracy: 0.8571 - val_loss: 0.8050 Epoch 383/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8155 - val_accuracy: 0.8571 - val_loss: 0.8045 Epoch 384/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8240 - val_accuracy: 0.8571 - val_loss: 0.8036 Epoch 385/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8237 - val_accuracy: 0.8571 - val_loss: 0.8027 Epoch 386/500 2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 0.8163 - val_accuracy: 0.8571 - val_loss: 0.8020 Epoch 387/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8028 - val_accuracy: 0.8571 - val_loss: 0.8012 Epoch 388/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7948 - val_accuracy: 0.8571 - val_loss: 0.8004 Epoch 389/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7891 - val_accuracy: 0.8571 - val_loss: 0.7996 Epoch 390/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8254 - val_accuracy: 0.8571 - val_loss: 0.7987 Epoch 391/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8277 - val_accuracy: 0.8571 - val_loss: 0.7981 Epoch 392/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8004 - val_accuracy: 0.8571 - val_loss: 0.7973 Epoch 393/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8248 - val_accuracy: 0.8571 - val_loss: 0.7966 Epoch 394/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8204 - val_accuracy: 0.8571 - val_loss: 0.7958 Epoch 395/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8073 - val_accuracy: 0.8571 - val_loss: 0.7950 Epoch 396/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7913 - val_accuracy: 0.8571 - val_loss: 0.7942 Epoch 397/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8046 - val_accuracy: 0.8571 - val_loss: 0.7934 Epoch 398/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8163 - val_accuracy: 0.8571 - val_loss: 0.7927 Epoch 399/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.7983 - val_accuracy: 0.8571 - val_loss: 0.7918 Epoch 400/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8062 - val_accuracy: 0.8571 - val_loss: 0.7912 Epoch 401/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.8047 - val_accuracy: 0.8571 - val_loss: 0.7904 Epoch 402/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.8145 - val_accuracy: 0.8571 - val_loss: 0.7898 Epoch 403/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8128 - val_accuracy: 0.8571 - val_loss: 0.7892 Epoch 404/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8163 - val_accuracy: 0.8571 - val_loss: 0.7885 Epoch 405/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8054 - val_accuracy: 0.8571 - val_loss: 0.7877 Epoch 406/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8282 - val_accuracy: 0.8571 - val_loss: 0.7870 Epoch 407/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7878 - val_accuracy: 0.8571 - val_loss: 0.7862 Epoch 408/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7720 - val_accuracy: 0.8571 - val_loss: 0.7854 Epoch 409/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.8026 - val_accuracy: 0.8571 - val_loss: 0.7848 Epoch 410/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7872 - val_accuracy: 0.8571 - val_loss: 0.7840 Epoch 411/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7907 - val_accuracy: 0.8571 - val_loss: 0.7833 Epoch 412/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8070 - val_accuracy: 0.8571 - val_loss: 0.7828 Epoch 413/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7948 - val_accuracy: 0.8571 - val_loss: 0.7821 Epoch 414/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8079 - val_accuracy: 0.8571 - val_loss: 0.7815 Epoch 415/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8118 - val_accuracy: 0.8571 - val_loss: 0.7811 Epoch 416/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7934 - val_accuracy: 0.8571 - val_loss: 0.7804 Epoch 417/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7853 - val_accuracy: 0.8571 - val_loss: 0.7797 Epoch 418/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7973 - val_accuracy: 0.8571 - val_loss: 0.7792 Epoch 419/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7896 - val_accuracy: 0.8571 - val_loss: 0.7785 Epoch 420/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8097 - val_accuracy: 0.8571 - val_loss: 0.7779 Epoch 421/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.8039 - val_accuracy: 0.8571 - val_loss: 0.7773 Epoch 422/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8143 - val_accuracy: 0.8571 - val_loss: 0.7769 Epoch 423/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7784 - val_accuracy: 0.8571 - val_loss: 0.7763 Epoch 424/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7868 - val_accuracy: 0.8571 - val_loss: 0.7756 Epoch 425/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7817 - val_accuracy: 0.8571 - val_loss: 0.7748 Epoch 426/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7935 - val_accuracy: 0.8571 - val_loss: 0.7741 Epoch 427/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7944 - val_accuracy: 0.8571 - val_loss: 0.7734 Epoch 428/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7773 - val_accuracy: 0.8571 - val_loss: 0.7727 Epoch 429/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8049 - val_accuracy: 0.8571 - val_loss: 0.7720 Epoch 430/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.8097 - val_accuracy: 0.8571 - val_loss: 0.7714 Epoch 431/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7760 - val_accuracy: 0.8571 - val_loss: 0.7707 Epoch 432/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7626 - val_accuracy: 0.8571 - val_loss: 0.7701 Epoch 433/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7663 - val_accuracy: 0.8571 - val_loss: 0.7694 Epoch 434/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8070 - val_accuracy: 0.8571 - val_loss: 0.7687 Epoch 435/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7924 - val_accuracy: 0.8571 - val_loss: 0.7680 Epoch 436/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7837 - val_accuracy: 0.8571 - val_loss: 0.7674 Epoch 437/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7776 - val_accuracy: 0.8571 - val_loss: 0.7668 Epoch 438/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7733 - val_accuracy: 0.8571 - val_loss: 0.7662 Epoch 439/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7763 - val_accuracy: 0.8571 - val_loss: 0.7655 Epoch 440/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7831 - val_accuracy: 0.8571 - val_loss: 0.7649 Epoch 441/500 2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 0.7754 - val_accuracy: 0.8571 - val_loss: 0.7642 Epoch 442/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7793 - val_accuracy: 0.8571 - val_loss: 0.7636 Epoch 443/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7956 - val_accuracy: 0.8571 - val_loss: 0.7630 Epoch 444/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7908 - val_accuracy: 0.8571 - val_loss: 0.7623 Epoch 445/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7548 - val_accuracy: 0.8571 - val_loss: 0.7617 Epoch 446/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7658 - val_accuracy: 0.8571 - val_loss: 0.7610 Epoch 447/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7624 - val_accuracy: 0.8571 - val_loss: 0.7606 Epoch 448/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7858 - val_accuracy: 0.8571 - val_loss: 0.7600 Epoch 449/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7509 - val_accuracy: 0.8571 - val_loss: 0.7593 Epoch 450/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7449 - val_accuracy: 0.8571 - val_loss: 0.7585 Epoch 451/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7845 - val_accuracy: 0.8571 - val_loss: 0.7581 Epoch 452/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7731 - val_accuracy: 0.8571 - val_loss: 0.7575 Epoch 453/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7696 - val_accuracy: 0.8571 - val_loss: 0.7568 Epoch 454/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7762 - val_accuracy: 0.8571 - val_loss: 0.7562 Epoch 455/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7623 - val_accuracy: 0.8571 - val_loss: 0.7558 Epoch 456/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7681 - val_accuracy: 0.8571 - val_loss: 0.7552 Epoch 457/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.7775 - val_accuracy: 0.8571 - val_loss: 0.7546 Epoch 458/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7442 - val_accuracy: 0.8571 - val_loss: 0.7539 Epoch 459/500 2/2 - 0s - 17ms/step - accuracy: 0.8537 - loss: 0.7491 - val_accuracy: 0.8571 - val_loss: 0.7533 Epoch 460/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7569 - val_accuracy: 0.8571 - val_loss: 0.7526 Epoch 461/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7578 - val_accuracy: 0.8571 - val_loss: 0.7520 Epoch 462/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7868 - val_accuracy: 0.8571 - val_loss: 0.7514 Epoch 463/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7384 - val_accuracy: 0.8571 - val_loss: 0.7507 Epoch 464/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.7416 - val_accuracy: 0.8571 - val_loss: 0.7501 Epoch 465/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7469 - val_accuracy: 0.8571 - val_loss: 0.7495 Epoch 466/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7544 - val_accuracy: 0.8571 - val_loss: 0.7489 Epoch 467/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7512 - val_accuracy: 0.8571 - val_loss: 0.7483 Epoch 468/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7596 - val_accuracy: 0.8571 - val_loss: 0.7478 Epoch 469/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7349 - val_accuracy: 0.8571 - val_loss: 0.7473 Epoch 470/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7470 - val_accuracy: 0.8571 - val_loss: 0.7467 Epoch 471/500 2/2 - 0s - 16ms/step - accuracy: 0.8537 - loss: 0.7698 - val_accuracy: 0.8571 - val_loss: 0.7461 Epoch 472/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7768 - val_accuracy: 0.8571 - val_loss: 0.7455 Epoch 473/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7659 - val_accuracy: 0.8571 - val_loss: 0.7449 Epoch 474/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7718 - val_accuracy: 0.8571 - val_loss: 0.7443 Epoch 475/500 2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 0.7533 - val_accuracy: 0.8571 - val_loss: 0.7437 Epoch 476/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7394 - val_accuracy: 0.8571 - val_loss: 0.7432 Epoch 477/500 2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.7536 - val_accuracy: 0.8571 - val_loss: 0.7426 Epoch 478/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7421 - val_accuracy: 0.8571 - val_loss: 0.7421 Epoch 479/500 2/2 - 0s - 18ms/step - accuracy: 0.8537 - loss: 0.7407 - val_accuracy: 0.8571 - val_loss: 0.7414 Epoch 480/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7582 - val_accuracy: 0.8571 - val_loss: 0.7409 Epoch 481/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7463 - val_accuracy: 0.8571 - val_loss: 0.7403 Epoch 482/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7320 - val_accuracy: 0.8571 - val_loss: 0.7397 Epoch 483/500 2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.7533 - val_accuracy: 0.8571 - val_loss: 0.7391 Epoch 484/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7584 - val_accuracy: 0.8571 - val_loss: 0.7385 Epoch 485/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7506 - val_accuracy: 0.8571 - val_loss: 0.7379 Epoch 486/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7710 - val_accuracy: 0.8571 - val_loss: 0.7375 Epoch 487/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7334 - val_accuracy: 0.8571 - val_loss: 0.7369 Epoch 488/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7446 - val_accuracy: 0.8571 - val_loss: 0.7365 Epoch 489/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7415 - val_accuracy: 0.8571 - val_loss: 0.7360 Epoch 490/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7383 - val_accuracy: 0.8571 - val_loss: 0.7354 Epoch 491/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7500 - val_accuracy: 0.8571 - val_loss: 0.7348 Epoch 492/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7453 - val_accuracy: 0.8571 - val_loss: 0.7343 Epoch 493/500 2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.7379 - val_accuracy: 0.8571 - val_loss: 0.7337 Epoch 494/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7510 - val_accuracy: 0.8571 - val_loss: 0.7333 Epoch 495/500 2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.7632 - val_accuracy: 0.8571 - val_loss: 0.7327 Epoch 496/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7321 - val_accuracy: 0.8571 - val_loss: 0.7323 Epoch 497/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7605 - val_accuracy: 0.8571 - val_loss: 0.7317 Epoch 498/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7427 - val_accuracy: 0.8571 - val_loss: 0.7311 Epoch 499/500 2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.7268 - val_accuracy: 0.8571 - val_loss: 0.7305 Epoch 500/500 2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.7267 - val_accuracy: 0.8571 - val_loss: 0.7300 1/1 - 0s - 16ms/step - accuracy: 0.8571 - loss: 0.7300 Dokładność testowa: 85.71%
Efekty uczenia
Wytrenowany model osiąga skuteczność predykcji na poziomie 85,7%
model.evaluate(X_test, y_test)[1][1m1/1[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m0s[0m 25ms/step - accuracy: 0.8571 - loss: 0.7300
0.8571428656578064
Wykresy
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper right')
plt.show()plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='lower right')
plt.show()Zapisanie modelu do pliku
model.save('./model.keras')