pub fn convolve2_nn<T>(
signal: &Array<T>,
filter: &Array<T>,
strides: Dim4,
padding: Dim4,
dilation: Dim4
) -> Array<T> where
T: HasAfEnum + RealFloating,
Convolution Integral for two dimensional data
This version of convolution is consistent with the machine learning formulation that will spatially convolve a filter on 2-dimensions against a signal. Multiple signals and filters can be batched against each other. Furthermore, the signals and filters can be multi-dimensional however their dimensions must match. Usually, this is the forward pass convolution in ML
Example:
Signals with dimensions: d0 x d1 x d2 x Ns
Filters with dimensions: d0 x d1 x d2 x Nf
Resulting Convolution: d0 x d1 x Nf x Ns
signal is the input signalfilter is convolution filterstrides are distance between consecutive elements along each dimension for original convolutionpadding specifies padding width along each dimension for original convolutiondilation specifies filter dilation along each dimension for original convolutionConvolved Array
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