Advanced Deep Learning with Keras

Deep Neural Networks
# reshape and normalize input images
image_size = x_train.shape[1]
x_train = np.reshape(x_train,[-1, image_size, image_size, 1])
x_test = np.reshape(x_test,[-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
# network parameters
# image is processed as is (square grayscale)
input_shape = (image_size, image_size, 1)
batch_size = 128
kernel_size = 3
filters = 64
dropout = 0.3
# use functional API to build cnn layers
inputs = Input(shape=input_shape)
y = Conv2D(filters=filters,
kernel_size=kernel_size,
activation='relu')(inputs)
y = MaxPooling2D()(y)
y = Conv2D(filters=filters,
kernel_size=kernel_size,
activation='relu')(y)
y = MaxPooling2D()(y)
y = Conv2D(filters=filters,
kernel_size=kernel_size,
activation='relu')(y)
# image to vector before connecting to dense layer
y = Flatten()(y)
# dropout regularization
y = Dropout(dropout)(y)
outputs = Dense(num_labels, activation='softmax')(y)
# build the model by supplying inputs/outputs
model = Model(inputs=inputs, outputs=outputs)
# network model in text
model.summary()
# classifier loss, Adam optimizer, classifier accuracy
model.compile(loss='categorical_crossentropy',
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