At first, this question is less about programming itself but about some logic behind the CNN architecture. I do understand how every layer works but my only question is: Does is make sense to separate the ReLU and Convolution-Layer? I mean, can a ConvLayer exist and work and update its weights by using backpropagation without having a ReLU behind it?
I thought so. This is why I created the following independent layers:
- Fully Connected
- Transformation (transform the 3D output into one dimension) for ConvLayer -> Fully Connected.
I am thinking about merging Layer 1 and 2 into one. What should I go for?