""" Conv2D(size, in=>out) Conv2d(size, in=>out, relu) Standard convolutional layer. `size` should be a tuple like `(2, 2)`. `in` and `out` specify the number of input and output channels respectively. Data should be stored in HWCN order. In other words, a 100×100 RGB image would be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array. Takes the keyword arguments `pad` and `stride`. """ struct Conv2D{F,A,V} σ::F weight::A bias::V stride::Int pad::Int end Conv2D(k::NTuple{2,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = initn, stride = 1, pad = 0) = Conv2D(σ, param(init(k..., ch...)), param(zeros(ch[2])), stride, pad) Flux.treelike(Conv2D) function (c::Conv2D)(x) σ, b = c.σ, reshape(c.bias, 1, 1, :) σ.(conv2d(x, c.weight, stride = c.stride, padding = c.pad) .+ b) end function Base.show(io::IO, l::Conv2D) print(io, "Conv2D((", size(l.weight, 1), ", ", size(l.weight, 2), ")") print(io, ", ", size(l.weight, 3), "=>", size(l.weight, 4)) l.σ == identity || print(io, ", ", l.σ) print(io, ")") end