clean code

This commit is contained in:
Tejan Karmali 2018-11-28 11:48:53 -05:00
parent 95e490a2c5
commit 519c3db5c0
2 changed files with 0 additions and 45 deletions

View File

@ -150,46 +150,6 @@ function Base.show(io::IO, l::DepthwiseConv)
print(io, ")")
end
"""
ConvTranspose(size, in=>out)
ConvTranspose(size, in=>out, relu)
Standard convolutional transpose 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 WHCN 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`, `stride` and `dilation`.
"""
struct ConvTranspose{N,F,A,V}
σ::F
weight::A
bias::V
stride::NTuple{N,Int}
pad::NTuple{N,Int}
dilation::NTuple{N,Int}
end
ConvTranspose(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N} =
ConvTranspose(σ, w, b, expand.(sub2(Val(N)), (stride, pad, dilation))...)
ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = initn,
stride = 1, pad = 0, dilation = 1) where N =
ConvTranspose(param(init(k..., reverse(ch)...)), param(zeros(ch[2])), σ,
stride = stride, pad = pad, dilation = dilation)
@treelike ConvTranspose
function (c::ConvTranspose)(x)
# ndims(x) == ndims(c.weight)-1 && return squeezebatch(c(reshape(x, size(x)..., 1)))
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
σ.(∇conv_data(x, c.weight, stride = c.stride, pad = c.pad, dilation = c.dilation) .+ b)
end
function Base.show(io::IO, l::ConvTranspose)
print(io, "ConvTranspose(", size(l.weight)[1:ndims(l.weight)-2])
end
"""
MaxPool(k)

View File

@ -356,12 +356,7 @@ x::TrackedVector * y::TrackedVector = track(*, x, y)
# NNlib
using NNlib
<<<<<<< HEAD:src/tracker/lib/array.jl
import NNlib: softmax, ∇softmax, logsoftmax, ∇logsoftmax, conv, ∇conv_data, depthwiseconv, maxpool, meanpool
=======
import NNlib: softmax, ∇softmax, logsoftmax, ∇logsoftmax,
conv, ∇conv_data, depthwiseconv, maxpool, meanpool
>>>>>>> a657c287d0590fdd9e49bb68c35bf96febe45e6d:src/tracker/array.jl
softmax(xs::TrackedArray) = track(softmax, xs)