05_convolutional_net.py

from keras.models import Sequential
from keras.layers import Dense, Flatten, Convolution2D, MaxPooling2D, Dropout
from keras.optimizers import RMSprop
from keras.datasets import mnist
from keras.utils import np_utils
from keras import initializations
from keras import backend as K

batch_size = 128
nb_classes = 10
nb_epoch = 100

img_rows, img_cols = 28, 28         # input image dimensions
pool_size = (2, 2)                  # size of pooling area for max pooling
prob_drop_conv = 0.2                # drop probability for dropout @ conv layer
prob_drop_hidden = 0.5              # drop probability for dropout @ fc layer

def init_weights(shape, name=None):
    return initializations.normal(shape, scale=0.01, name=name)

# Load MNIST dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print('X_train original shape:', X_train.shape)

if K.image_dim_ordering() == 'th':
    # For Theano backend
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    # For TensorFlow backend
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)


X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')


# Convolutional model
model = Sequential()

# conv1 layer
model.add(Convolution2D(32, 3, 3, border_mode='same', activation='relu', input_shape=input_shape, init=init_weights))
model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), border_mode='same'))
model.add(Dropout(prob_drop_conv))

# conv2 layer
model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu', init=init_weights))
model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), border_mode='same'))
model.add(Dropout(prob_drop_conv))

# conv3 layer
model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu', init=init_weights))
model.add(MaxPooling2D(pool_size=pool_size, strides=(2,2), border_mode='same'))
model.add(Flatten())
model.add(Dropout(prob_drop_conv))

# fc1 layer
model.add(Dense(625, activation='relu', init=init_weights))
model.add(Dropout(prob_drop_hidden))

# fc2 layer
model.add(Dense(10, activation='softmax', init=init_weights))

opt = RMSprop(lr=0.001, rho=0.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

# Train
history = model.fit(X_train, Y_train, nb_epoch=nb_epoch, batch_size=batch_size, shuffle=True, verbose=1)

# Evaluate
evaluation = model.evaluate(X_test, Y_test, batch_size=256, verbose=1)
print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))