Is it possible to copy models or layers in Keras. For example in lesson 3 instead of defining a completely new model with the batchnorm layers included it would be nice if we could just do:
bn =  for layer in model.layers: if type(layer) is Dropout: bn.append(Dropout(.5)) bn.append(BatchNormalization()) else: bn.append(layer) model = Sequential(bn)
If I do this then the model.summary() has extra connected_to layers. For example:
convolution2d_209 (Convolution2D (None, 64, 224, 224) 1792 zeropadding2d_209 zeropadding2d_209
I can fit the model like this but what are practical implications? Is it fitting what I want or adding in extra nodes?