Removing layers from a pretrained keras model gives the same output as original model

Question

During some feature extraction experiments, I noticed that the \'model.pop()\' functionality is not working as expected. For a pretrained model like vgg16, after using \'model.p

Answer 1

Working off @Koul answer.

I believe you don't need to use the pop method. Instead just pass the layer before the output layer as the argument for the Model method's output parameter:

from keras.models import Model

model2 = Model(model.input, model.layers[-2].output)
model2.summary()

Answer 2

Found the answer here : https://github.com/keras-team/keras/issues/2371#issuecomment-308604552

from keras.models import Model

model.layers.pop()
model2 = Model(model.input, model.layers[-1].output)
model2.summary()

model2 behaves correctly.