# [−][src]Function arrayfire::convolve2_nn

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

# Parameters

`signal`

is the input signal`filter`

is convolution filter`strides`

are distance between consecutive elements along each dimension for original convolution`padding`

specifies padding width along each dimension for original convolution`dilation`

specifies filter dilation along each dimension for original convolution

# Return Values

Convolved Array