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…MaxPoolingLayer` * Pooling is now a subclass of Convolutional Layer. As a result common functions and fields are not replicated. * Constructor arguments that can be internally computed are eliminated.
…frame". This is important to have the same naming convention everywhere.
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API Redesign
Goal
The goal is this PR is to improve the API of the CNN layers (MaxPooling and Conv currently), by eliminating redundant constructor arguments and fields. By redundant in this context, I refer to arguments that can be directly computed from others, and fields that unnecesseraly exist in multiple classes.
Key points
Below some discussion points on design decisions I made, but still consider debatable.
Since my experience in production level C++ is very limited I highly value opinions from experienced colleagues and previous authors of the module.
Making
MaxPoolingLayera sub-class ofConvLayerEvery layer type in a convolutional network follows the logic existing in our
ConvLayer:A filter is sliding over the input and at each step applies an operation to the input elements within its receptive field to produce a single output element.
As we can see they all share the same logic and therefore fields.
Results
Common fields between the 2 layer types in the CNN module are now not duplicated (strides sizes, padding sizes, filter sizes). The same for the 4
protectedmethods inConvLayer.We now have a cleaner API, as the constructor arguments where reduced from 26 to 16 without any change in the functionality).