TastyPancakes/Flux.jl
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conv padding

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This commit is contained in:
Mike J Innes 2017-12-18 18:05:38 +00:00
parent 51f93d9f0e
commit 269d8f36b9
2 changed files with 15 additions and 13 deletions
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7
src/layers/conv.jl
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@ -2,15 +2,16 @@ struct Conv2D{F,A}
σ::F
weight::A
stride::Int
pad::Int
end
Conv2D(k::NTuple{2,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = initn, stride = 1) =
Conv2D(σ, param(initn(k..., ch...)), stride)
init = initn, stride = 1, pad = 0) =
Conv2D(σ, param(initn(k..., ch...)), stride, pad)
Flux.treelike(Conv2D)
(c::Conv2D)(x) = c.σ.(conv2d(x, c.weight, stride = c.stride))
(c::Conv2D)(x) = c.σ.(conv2d(x, c.weight, stride = c.stride, padding = c.pad))
function Base.show(io::IO, l::Conv2D)
print(io, "Conv2D((", size(l.weight, 1), ", ", size(l.weight, 2), ")")

21
src/tracker/lib.jl
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@ -136,18 +136,19 @@ softmax(xs::TrackedArray) = TrackedArray(Call(softmax, xs))
back(::typeof(softmax), Δ, xs) = @back(xs, ∇softmax(Δ, data(xs)))
_conv2d(x, w, stride) = conv2d(x, w, stride = stride)
# TODO: can store kwargs efficiently in namedtuples
_conv2d(x, w, stride, pad) = conv2d(x, w, stride = stride, padding = pad)
conv2d(x::TrackedArray{<:Any,4}, w::TrackedArray{<:Any,4}; stride = 1) =
TrackedArray(Call(_conv2d, x, w, stride))
conv2d(x::AbstractArray{<:Any,4}, w::TrackedArray{<:Any,4}; stride = 1) =
TrackedArray(Call(_conv2d, x, w, stride))
conv2d(x::TrackedArray{<:Any,4}, w::AbstractArray{<:Any,4}; stride = 1) =
TrackedArray(Call(_conv2d, x, w, stride))
conv2d(x::TrackedArray{<:Any,4}, w::TrackedArray{<:Any,4}; stride = 1, padding = 0) =
TrackedArray(Call(_conv2d, x, w, stride, padding))
conv2d(x::AbstractArray{<:Any,4}, w::TrackedArray{<:Any,4}; stride = 1, padding = 0) =
TrackedArray(Call(_conv2d, x, w, stride, padding))
conv2d(x::TrackedArray{<:Any,4}, w::AbstractArray{<:Any,4}; stride = 1, padding = 0) =
TrackedArray(Call(_conv2d, x, w, stride, padding))
function back(::typeof(_conv2d), Δ, x, w, stride)
@back(x, NNlib.conv2d_grad_x(data(x), data(w), Δ; stride = stride))
@back(w, NNlib.conv2d_grad_w(data(x), data(w), Δ; stride = stride))
function back(::typeof(_conv2d), Δ, x, w, stride, pad)
@back(x, NNlib.conv2d_grad_x(data(x), data(w), Δ; stride = stride, padding = pad))
@back(w, NNlib.conv2d_grad_w(data(x), data(w), Δ; stride = stride, padding = pad))
end
_pool(x, k, mode) = pool(x, window = k, mode = mode)