TastyPancakes/Flux.jl
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This commit is contained in:
Avik Pal 2018-07-27 20:12:49 +05:30
parent 7dd5ec16c9
commit 2cc0f112f1
1 changed files with 22 additions and 2 deletions
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24
src/cuda/cudnn.jl
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@ -58,7 +58,7 @@ function cudnnBNForward!(y::CuArray{T}, g::CuArray{T}, b::CuArray{T}, x::CuArray
eps = T(1e-5), training = true) where T<:Union{Float32, Float64} eps = T(1e-5), training = true) where T<:Union{Float32, Float64}
dims = _wsize(x) dims = _wsize(x)
if eps < BATCHNORM_MIN_EPS if eps < BATCHNORM_MIN_EPS
warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS) # warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
eps = BATCHNORM_MIN_EPS eps = BATCHNORM_MIN_EPS
end end
xd = TensorDesc(x) xd = TensorDesc(x)
@ -145,7 +145,7 @@ function cudnnBNBackward!(dg::CuArray{T}, g::CuArray{T}, db::CuArray{T},
end end
if eps < BATCHNORM_MIN_EPS if eps < BATCHNORM_MIN_EPS
warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS) # warn("eps ",eps," is too small for CuDNN so eps has been assigned the value ", BATCHNORM_MIN_EPS)
eps = BATCHNORM_MIN_EPS eps = BATCHNORM_MIN_EPS
end end
@ -187,5 +187,25 @@ batchnorm(g::TrackedArray, b::TrackedArray, x::CuArray{T}, running_mean::CuArray
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::TrackedArray, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::CuArray{T}, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::CuArray{T}, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::TrackedArray, b::CuArray{T}, x::CuArray{T}, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
batchnorm(g::CuArray{T}, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T},
running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} =
track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...)
@grad batchnorm(g, b, x, running_mean, running_var, momentum; kw...) = @grad batchnorm(g, b, x, running_mean, running_var, momentum; kw...) =
batchnorm(data.((g, b, x))..., running_mean, running_var, momentum; kw...), Δ -> (nobacksies(:batchnorm, ∇batchnorm(data.((g, b, x, Δ))..., running_mean, running_var, momentum; kw...))..., nothing, nothing, nothing) batchnorm(data.((g, b, x))..., running_mean, running_var, momentum; kw...), Δ -> (nobacksies(:batchnorm, ∇batchnorm(data.((g, b, x, Δ))..., running_mean, running_var, momentum; kw...))..., nothing, nothing, nothing)