Keras VGG16 fine tuning

Question

There is an example of VGG16 fine-tuning on keras blog, but I can\'t reproduce it.

More precisely, here is code used to init VGG16 without top layer and to freeze a

Answer 1

I think you can concatenate both by doing something like this:

#load vgg model
vgg_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
print('Model loaded.')

#initialise top model
top_model = Sequential()
top_model.add(Flatten(input_shape=vgg_model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(1, activation='sigmoid'))


top_model.load_weights(top_model_weights_path)

# add the model on top of the convolutional base

model = Model(input= vgg_model.input, output= top_model(vgg_model.output))

This solution refers to the example Fine-tuning the top layers of a a pre-trained network. Full code can be found here.