Arty33/Keras

import numpy as np import matplotlib.pyplot as plt from tensorflow.keras.datasets import mnist from tensorflow import keras from tensorflow.keras.layers import Dense, Flatten

(x_train, y_train), (x_test, y_test) = mnist.load_data()

plt.figure(figsize=(10,5)) for i in range(25): plt.subplot(5,5,i+1) plt.xticks([]) plt.yticks([]) plt.imshow(x_train[i], cmap=plt.cm.binary)

plt.show()

model = keras.Sequential([ Flatten(input_shape=(28, 28, 1)), Dense(128, activation='relu'), Dense(10, activation='softmax') ])

print(model.summary())

x_train = x_train / 255 x_test = x_test / 255

y_train_cat = keras.utils.to_categorical(y_train, 10) y_test_cat = keras.utils.to_categorical(y_test, 10)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=32, epochs=10, validation_split=0.2)

model.evaluate(x_test, y_test_cat)

n = 1 x = np.expand_dims(x_test[n], axis=0) res = model.predict(x) print( res )

print( np.argmax(res) )

plt.imshow(x_test[n], cmap=plt.cm.binary) plt.show()

pred = model.predict(x_test) pred = np.argmax(pred, axis=1)

print(pred.shape)

print(pred[:20]) print(y_test[:20])

mask = pred == y_test print(mask[:10])

x_false = x_test[mask] y_false = x_test[mask]

print(x_false.shape)

for i in range(5): print("Значение сети: "+str(y_test[i])) plt.imshow(x_false[i], cmap=plt.cm.binary) plt.show()