VGG_ex.py

model = Sequential()
model.add(Convolution2D(64, 3, 3, border_mode='valid', input_shape=(1, 128, 128)))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Activation('relu'))

model.add(Convolution2D(128, 3, 3))
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Activation('relu'))

model.add(Convolution2D(256, 3, 3))
model.add(Activation('relu'))
model.add(Convolution2D(256, 3, 3))
model.add(Activation('relu'))
model.add(Convolution2D(256, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Convolution2D(512, 3, 3))
model.add(Activation('relu'))
model.add(Convolution2D(512, 3, 3))
model.add(Activation('relu'))
model.add(Convolution2D(512, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1000))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(250, activation='softmax'))

sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

model.fit(X_train, Y_train, batch_size=model_control.batch_size, nb_epoch=model_control.nb_epoch, verbose=1)