ium_464903/dockerfiles/Biblioteka_DL_trenowanie.ipynb

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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 plt

Pobranie 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 - accuracy: 0.8537 - loss: 1.2231 - val_accuracy: 0.8571 - val_loss: 1.1943
Epoch 130/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.2190 - val_accuracy: 0.8571 - val_loss: 1.1910
Epoch 131/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.2191 - val_accuracy: 0.8571 - val_loss: 1.1880
Epoch 132/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.2025 - val_accuracy: 0.8571 - val_loss: 1.1843
Epoch 133/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.1829 - val_accuracy: 0.8571 - val_loss: 1.1806
Epoch 134/500
2/2 - 0s - 19ms/step - accuracy: 0.8537 - loss: 1.1836 - val_accuracy: 0.8571 - val_loss: 1.1770
Epoch 135/500
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.1937 - val_accuracy: 0.8571 - val_loss: 1.1741
Epoch 136/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1785 - val_accuracy: 0.8571 - val_loss: 1.1710
Epoch 137/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1702 - val_accuracy: 0.8571 - val_loss: 1.1680
Epoch 138/500
2/2 - 0s - 25ms/step - accuracy: 0.8537 - 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 - val_accuracy: 0.8571 - val_loss: 1.1365
Epoch 148/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1870 - val_accuracy: 0.8571 - val_loss: 1.1336
Epoch 149/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1472 - val_accuracy: 0.8571 - val_loss: 1.1304
Epoch 150/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.1646 - val_accuracy: 0.8571 - val_loss: 1.1277
Epoch 151/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1335 - val_accuracy: 0.8571 - val_loss: 1.1259
Epoch 152/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1459 - val_accuracy: 0.8571 - val_loss: 1.1237
Epoch 153/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1264 - val_accuracy: 0.8571 - val_loss: 1.1209
Epoch 154/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1382 - val_accuracy: 0.8571 - val_loss: 1.1182
Epoch 155/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.1280 - val_accuracy: 0.8571 - val_loss: 1.1153
Epoch 156/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1313 - val_accuracy: 0.8571 - val_loss: 1.1123
Epoch 157/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1102 - val_accuracy: 0.8571 - val_loss: 1.1094
Epoch 158/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.1071 - val_accuracy: 0.8571 - val_loss: 1.1069
Epoch 159/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0845 - val_accuracy: 0.8571 - val_loss: 1.1038
Epoch 160/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1140 - val_accuracy: 0.8571 - val_loss: 1.1009
Epoch 161/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1390 - val_accuracy: 0.8571 - val_loss: 1.0985
Epoch 162/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0967 - val_accuracy: 0.8571 - val_loss: 1.0959
Epoch 163/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0894 - val_accuracy: 0.8571 - val_loss: 1.0935
Epoch 164/500
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0937 - val_accuracy: 0.8571 - val_loss: 1.0908
Epoch 165/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1170 - val_accuracy: 0.8571 - val_loss: 1.0882
Epoch 166/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.1016 - val_accuracy: 0.8571 - val_loss: 1.0855
Epoch 167/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1365 - val_accuracy: 0.8571 - val_loss: 1.0842
Epoch 168/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.1163 - val_accuracy: 0.8571 - val_loss: 1.0821
Epoch 169/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0915 - val_accuracy: 0.8571 - val_loss: 1.0794
Epoch 170/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0816 - val_accuracy: 0.8571 - val_loss: 1.0777
Epoch 171/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1195 - val_accuracy: 0.8571 - val_loss: 1.0762
Epoch 172/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0741 - val_accuracy: 0.8571 - val_loss: 1.0741
Epoch 173/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.1333 - val_accuracy: 0.8571 - val_loss: 1.0718
Epoch 174/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0593 - val_accuracy: 0.8571 - val_loss: 1.0693
Epoch 175/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0957 - val_accuracy: 0.8571 - val_loss: 1.0670
Epoch 176/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0722 - val_accuracy: 0.8571 - val_loss: 1.0650
Epoch 177/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0786 - val_accuracy: 0.8571 - val_loss: 1.0626
Epoch 178/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0939 - val_accuracy: 0.8571 - val_loss: 1.0609
Epoch 179/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0600 - val_accuracy: 0.8571 - val_loss: 1.0586
Epoch 180/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0768 - val_accuracy: 0.8571 - val_loss: 1.0561
Epoch 181/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0888 - val_accuracy: 0.8571 - val_loss: 1.0545
Epoch 182/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0809 - val_accuracy: 0.8571 - val_loss: 1.0526
Epoch 183/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0803 - val_accuracy: 0.8571 - val_loss: 1.0508
Epoch 184/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0355 - val_accuracy: 0.8571 - val_loss: 1.0486
Epoch 185/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0581 - val_accuracy: 0.8571 - val_loss: 1.0463
Epoch 186/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0827 - val_accuracy: 0.8571 - val_loss: 1.0447
Epoch 187/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0457 - val_accuracy: 0.8571 - val_loss: 1.0424
Epoch 188/500
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 1.0647 - val_accuracy: 0.8571 - val_loss: 1.0413
Epoch 189/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0579 - val_accuracy: 0.8571 - val_loss: 1.0397
Epoch 190/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0724 - val_accuracy: 0.8571 - val_loss: 1.0374
Epoch 191/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 1.0497 - val_accuracy: 0.8571 - val_loss: 1.0353
Epoch 192/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 1.0767 - val_accuracy: 0.8571 - val_loss: 1.0335
Epoch 193/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - 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 - val_accuracy: 0.8571 - val_loss: 1.0139
Epoch 203/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0471 - val_accuracy: 0.8571 - val_loss: 1.0119
Epoch 204/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0409 - val_accuracy: 0.8571 - val_loss: 1.0102
Epoch 205/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0357 - val_accuracy: 0.8571 - val_loss: 1.0086
Epoch 206/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 1.0442 - val_accuracy: 0.8571 - val_loss: 1.0075
Epoch 207/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0219 - val_accuracy: 0.8571 - val_loss: 1.0058
Epoch 208/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 1.0257 - val_accuracy: 0.8571 - val_loss: 1.0042
Epoch 209/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9953 - val_accuracy: 0.8571 - val_loss: 1.0021
Epoch 210/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 1.0650 - val_accuracy: 0.8571 - val_loss: 1.0003
Epoch 211/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9909 - val_accuracy: 0.8571 - 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 - val_accuracy: 0.8571 - val_loss: 0.9314
Epoch 258/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9272 - val_accuracy: 0.8571 - val_loss: 0.9299
Epoch 259/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9601 - val_accuracy: 0.8571 - val_loss: 0.9284
Epoch 260/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9253 - val_accuracy: 0.8571 - val_loss: 0.9271
Epoch 261/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9673 - val_accuracy: 0.8571 - val_loss: 0.9258
Epoch 262/500
2/2 - 0s - 22ms/step - accuracy: 0.8537 - loss: 0.9492 - val_accuracy: 0.8571 - val_loss: 0.9246
Epoch 263/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9503 - val_accuracy: 0.8571 - val_loss: 0.9232
Epoch 264/500
2/2 - 0s - 24ms/step - accuracy: 0.8537 - loss: 0.9502 - val_accuracy: 0.8571 - val_loss: 0.9223
Epoch 265/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8984 - val_accuracy: 0.8571 - val_loss: 0.9208
Epoch 266/500
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9395 - val_accuracy: 0.8571 - 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 - 22ms/step - accuracy: 0.8537 - loss: 0.9045 - val_accuracy: 0.8571 - val_loss: 0.8975
Epoch 286/500
2/2 - 0s - 23ms/step - accuracy: 0.8537 - loss: 0.9414 - val_accuracy: 0.8571 - val_loss: 0.8964
Epoch 287/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8925 - val_accuracy: 0.8571 - val_loss: 0.8951
Epoch 288/500
2/2 - 0s - 25ms/step - accuracy: 0.8537 - loss: 0.9091 - val_accuracy: 0.8571 - val_loss: 0.8944
Epoch 289/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9149 - val_accuracy: 0.8571 - val_loss: 0.8936
Epoch 290/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.8909 - val_accuracy: 0.8571 - val_loss: 0.8925
Epoch 291/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.8932 - val_accuracy: 0.8571 - val_loss: 0.8912
Epoch 292/500
2/2 - 0s - 20ms/step - accuracy: 0.8537 - loss: 0.9086 - val_accuracy: 0.8571 - val_loss: 0.8901
Epoch 293/500
2/2 - 0s - 21ms/step - accuracy: 0.8537 - loss: 0.9072 - val_accuracy: 0.8571 - val_loss: 0.8890
Epoch 294/500
2/2 - 0s - 21ms/step - 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]
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 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')