shouldn't model.trainable=False freeze weights under the model?

Question

I am trying to freeze the free trained VGG16\'s layers (\'conv_base\' below) and add new layers on top of them for feature extracting. I expect to get same prediction result

Answer 1

You must freeze layers individually (before compilation):

for l in conv_base.layers: 
    l.trainable=False

And if this doesn't work, you should probably use the new sequential model to freeze the layers.

If you have models in models you should do this recursively:

def freezeLayer(layer):
    layer.trainable = False
    if hasattr(layer, 'layers'):
        for l in layer.layers:
            freezeLayer(l)

freezeLayer(model)