from keras import layers
from keras import models
from keras.datasets import mnist
#get the training and testing examples
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
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.MaxPool2D((2,2)))

model.add(layers.Conv2D(64, (3,3), activation='relu'))

#adding a classifier on top of the convnet

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

train_images = train_images.reshape((60000, 28, 28,1))
train_images = train_images.astype('float32') / 255

#print(test_images.shape)
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255

#categorically encode the labels

from keras.utils import to_categorical

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)

print('test_acc: ', test_acc)