I was working on a project to convert keras json model to caffe prototxt
caffe supports arbitrary padding values
keras (on top of tensorflow) supports 'same' and 'valid' values
For any padding value in caffe, we can manually add ZeroPadding layers in keras and then apply 'valid' scheme to get the same output dimensions
from
https://github.com/MarcBS/keras/blob/master/keras/caffe/README.md
Given the differences between Caffe and Keras when applying the MAX pooling opperation, in some occasions the MAX pooling layers must include a
pad: 1
value even if they did not include them in their original .prototxt
What exactly is the difference the implementation of MAX pooling between these two frameworks?