Feature extraction in Keras
There is no general rules on how to choose the layer for feature extraction but you might use a easy rule of thumb. The deeper you go to the network - the less ImageNet specific semantic features you would have. But in the same time - you are getting less semantic features also.
What I would do is to use the pool layers in both topologies - and if this didn't work well - then I would go deeper by setting the depth as metaparameter.
I am using pretrained resnet50 and inception v3 networks to extract features from my images, which I then use with my ML algo. 
Which layers are recommended for feature extraction? 
I am currently using: "mixed10" in Inception v3 and "avg_pool" in resent50. The features are modelling well in XGBoost though.
Thank you.