I have a data set which I have various aspects of the data, so I think a CNN will work best. I have grouped each aspect of the data into sets of 5 data points, a couple of which I have padded with zeroes. My first layer has a kernel size and strides of 5, with 200 output filters.
My question is around pooling, should I set the strides to equal 5 too, so that I'm not overlapping my features? And if I set the pooling size to 5, I assume I will have 1 pool per feature per filter? And if I set pool size to 2, then I will have 3 pools and here I should enable padding to equal the same?
Then on my next layer I'll use a kernel and strides of the same size as the number of pools to the previous.
Have I understood this correctly?