I read a lot about CNNs but I didn't quite understand some things:
What are the activation function in CLayers for? If I understood it right, the only weights in these layers are the ones in Filters, and for the activation function a weighted sum is needed?
The computational effort should increase, shouldn't it? When there are many Filters many Feature Maps (Map with the dot-products) are produced. All of them are given to the next Layer, so if they are as big as the input Image and there are 10 Filters in the first CLayer the second CLayer would have to apply 10x the computational effort(per Filter in the second Layer) that the first layer had or, since they all take all the last layers outputs (the feature maps).
If there are more Layers than one, how does the backpropagation know what they have to change on each one of them, especially since the backpropagation occurs after all layers are applied.
(CLayer = Convolutional Layer)