mnist.py

from keras.datasets import mnist
from keras import models
from keras import layers
from keras.utils import to_categorical

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

network = models.Sequential()

network.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
network.add(layers.MaxPool2D((2, 2)))
network.add(layers.Conv2D(64, (3, 3), activation='relu'))
network.add(layers.MaxPool2D((2, 2)))
network.add(layers.Conv2D(64, (3, 3), activation='relu'))
network.add(layers.Flatten())
network.add(layers.Dense(64, activation='relu'))
network.add(layers.Dense(10, activation='softmax'))

network.summary()

train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
#
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
#
#
network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
network.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = network.evaluate(test_images, test_labels)
print('test_acc:', test_acc)