vgg-48-define-model.py

prior = keras.applications.VGG16(
    include_top=False, 
    weights='imagenet',
    input_shape=(48, 48, 3)
)

model = Sequential()
model.add(prior)
model.add(Flatten())
model.add(Dense(256, activation='relu', name='Dense_Intermediate'))
model.add(Dropout(0.1, name='Dropout_Regularization'))
model.add(Dense(12, activation='sigmoid', name='Output'))
for cnn_block_layer in model.layers[0].layers:
    cnn_block_layer.trainable = False
model.layers[0].trainable = False

model.compile(
    optimizer=RMSprop(),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)