4. train, test and save the model

mnist04.py

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adam(),
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=2,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=1)

print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save('mnist_keras_cnn_model.h5')