---

import tersorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer'
               'dog', 'frog', 'horse', 'ship', 'truck']
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)

model = keras.sequential([
    keras.layers.conv2d(32, (3, 3), padding='same', activation='relu', input_shape=(32, 32, 3)),
    keras.layers.maxpooling2d((2,2)),
    keras.layers.conv2d(64, (3, 3), padding='same', activation='relu'),
    keras.layers.maxpooling2d((2, 2)),
    keras.layers.conv2d(128, (3, 3), padding='same', activation='relu'),
    keras.layers.maxpooling2d((2, 2)),
    keras.layers.flatten(),
    keras.layers.dense(128, activation='relu')
    keras.layers.dense(10, activation='softmax')
])

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

history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print('test accuracy:', test_acc)

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('epoch')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
plt.show()

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

plt.figure(figsize=(10, 10))
for i in range(25):
    plt.subplot(5, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(x_test[i], cmap=plt.cm.binary)
plt.xlabel(class_names[predicted_classes[i]])
plt.show()