conv_mnist.py

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

from tensorflow.python.client import device_lib

print(device_lib.list_local_devices())

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

train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32')/255
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)
train_labels = to_categorical(train_labels, 10)
test_labels = to_categorical(test_labels, 10)

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
#model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

#print(model.summary())

model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=2, batch_size=64)

score = model.evaluate(test_images, test_labels)
print('Evaluated')
print('Test loss:', score[0])
print('Test accuracy:', score[1])