cleanup

src/layers/stateless.jl

@@ -120,44 +120,8 @@ function logitbce_loss(ŷ, y; agg=mean)
     agg(@.((1-y)*ŷ - logσ(ŷ)))
 end
 # Re-definition to fix interaction with CuArrays.
-<<<<<<< HEAD
-CuArrays.@cufunc logitbinarycrossentropy(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
-
-"""
-    normalise(x; dims=1, ϵ=1.0e-5))
-
-Normalise `x` to mean 0 and standard deviation 1 across the dimensions given by `dims`.
-Defaults to normalising over columns. Regularizes the standard deviation by `ϵ`.
-
-```jldoctest
-julia> a = reshape(collect(1:9), 3, 3)
-3×3 Array{Int64,2}:
- 1  4  7
- 2  5  8
- 3  6  9
-
-julia> Flux.normalise(a)
-3×3 Array{Float64,2}:
- -1.22473  -1.22473  -1.22473
-  0.0       0.0       0.0
-  1.22473   1.22473   1.22473
-
-julia> Flux.normalise(a, dims=2)
-3×3 Array{Float64,2}:
- -1.22474  0.0  1.22474
- -1.22474  0.0  1.22474
- -1.22474  0.0  1.22474
-```
-"""
-function normalise(x::AbstractArray; dims=1, ϵ=1.0f-5)
-  μ′ = mean(x, dims = dims)
-  σ′ = std(x, dims = dims, mean = μ′, corrected=false)
-  return (x .- μ′) ./ (σ′ .+ ϵ)
-end
-=======
 # CuArrays.@cufunc logitbce_loss(ŷ, y) = (1 - y)*ŷ - logσ(ŷ)
 
->>>>>>> 210028b3... new loss interface
 
 """
     kldivergence(ŷ, y; agg=mean)