Inspired by ideas of Lesson 7, I’m trying to build my own Fully Convolutional Network based on current versions of libraries available (keras 2.0.9 on theano 1.0.1 in particular)
I’m trying to get the ideas of provided notebooks but not the code itself as much as possible.
I figured out that Keras 2 includes “built-in” implementation of VGG16 model, and I try to utilise it. I figured out that in contrast to VGG16 model version we considered during our lessons (as far as I understand it was developed by @jeremy especially for the classes) the “built-in VGG16” is not Sequential model, so it has no model.pop() method.
My first idea was to use model.layers.pop() (as it was already suggested by @mmusket above ) but it is not exact equivalent of model.pop() for Sequential models. After model.layers.pop() the model had produced the same output as before model.layers.pop().
More precise equivalent is something like the code below. In particular it allowed me to exclude last MaxPooling2D layer of “keras built-in VGG16” and precompute the features before this last layer (exactly as @jeremy suggested) . I’m not sure if there is more elegant, compact or idiomatic way to do it with keras. And will be glad if somebody point me on that way if any.
model.layers.pop()
model.layers[-1].outbound_nodes = []
model.outputs = [model.layers[-1].output]