Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
# load mnist dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# compute the number of labels
num_labels = len(np.unique(y_train))
# convert to one-hot vector
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
# resize and normalize
image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1, image_size, image_size])
x_test = np.reshape(x_test,[-1, image_size, image_size])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# network parameters
input_shape = (image_size, image_size)
batch_size = 128
units = 256
dropout = 0.2
# model is RNN with 256 units, input is 28-dim vector 28 timesteps
model = Sequential()
model.add(SimpleRNN(units=units,
dropout=dropout,
input_shape=input_shape))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='rnn-mnist.png', show_shapes=True)
# loss function for one-hot vector
# use of sgd optimizer
# accuracy is good metric for classification tasks
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
# train the network
model.fit(x_train, y_train, epochs=20, batch_size=batch_size)
loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))
[ 32 ]