Convolution something

Neural2d now does convolution networking, which is great, but it already did convolution filtering.

That’s confusing terminology. They sound almost alike.

In neural2d terminology, convolution networking is when you have a set of convolution kernels that you want to train to extract features from an input signal. Convolution filtering is when you have a single predetermined, constant kernel that you want to specify.

In the neural2d topology configuration syntax, a convolution network layer is defined something like:

layerConvolve size 20*64x64 from input convolve 7x7

A convolution filter layer is defined with a syntax like this:

layerConvolve size 64x64 from input convolve {{0,-1,0},{-1,5,-1},{0,-1,0}}

Personally, I’m happy with the configuration syntax, but is there less confusing terminology that we should use instead?

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