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

# load images
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

#import matplotlib.pyplot as plt
#plt.subplot(111)
#plt.imshow(train_images[100], cmap=plt.get_cmap('gray'))
#plt.show()

# create deep network.
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'))

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

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

# convert labels to categories.
train_labels = to_categorical(train_labels)

# start training.
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

model.save('MNIST.h5')
# HDF5 format (Hierarchical Data Format)

# from keras.models import load_model
# model = load_model('MNIST.model')