CrossCor(filter, in => out, σ=identity; stride=1, pad=0, dilation=1, [bias, init])

Standard cross correlation layer. filter is a tuple of integers specifying the size of the convolutional kernel; in and out specify the number of input and output channels.

Parameters are controlled by additional keywords, with defaults init=glorot_uniform and bias=true.

See also Conv for more detailed description of keywords.

Examples

			julia> xs = rand(Float32, 100, 100, 3, 50);  # a batch of 50 RGB images

julia> layer = CrossCor((5,5), 3 => 6, relu; bias=false)
CrossCor((5, 5), 3 => 6, relu, bias=false)  # 450 parameters

julia> layer(xs) |> size
(96, 96, 6, 50)

julia> CrossCor((5,5), 3 => 7, stride=3, pad=(2,0))(xs) |> size
(34, 32, 7, 50)

			CrossCor(weight::AbstractArray, [bias, activation; stride, pad, dilation])

Constructs a CrossCor layer with the given weight and bias. Accepts the same keywords and has the same defaults as [ CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ; ...)]( CrossCor).

Examples

			julia> weight = rand(3, 4, 5);

julia> bias = zeros(5);

julia> layer = CrossCor(weight, bias, relu)
CrossCor((3,), 4 => 5, relu)  # 65 parameters

julia> layer(randn(100, 4, 64)) |> size
(98, 5, 64)