Fix problems due to rebase
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@ -17,7 +17,6 @@ makedocs(modules=[Flux, NNlib],
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"DataLoader" => "data/dataloader.md"],
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"DataLoader" => "data/dataloader.md"],
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"Training Models" =>
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"Training Models" =>
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["Optimisers" => "training/optimisers.md",
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["Optimisers" => "training/optimisers.md",
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"Loss Functions" => "training/loss_functions.md",
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"Training" => "training/training.md"],
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"Training" => "training/training.md"],
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"GPU Support" => "gpu.md",
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"GPU Support" => "gpu.md",
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"Saving & Loading" => "saving.md",
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"Saving & Loading" => "saving.md",
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@ -1,13 +0,0 @@
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# Loss Functions
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The following functions provide basic loss (or cost) functions.
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```@docs
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Flux.mse
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Flux.crossentropy
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Flux.logitcrossentropy
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Flux.binarycrossentropy
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Flux.logitbinarycrossentropy
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Flux.normalise
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```
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@ -98,10 +98,9 @@ julia> d = Dense(5, 2)
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Dense(5, 2)
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Dense(5, 2)
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julia> d(rand(5))
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julia> d(rand(5))
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Array{Float64,1}:
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2-element Array{Float32,1}:
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0.00257447
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-0.16210233
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-0.00449443
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0.12311903```
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```
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"""
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"""
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struct Dense{F,S,T}
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struct Dense{F,S,T}
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W::S
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W::S
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@ -128,7 +128,13 @@ Split `xs` into `n` parts.
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# Examples
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# Examples
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```jldoctest
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```jldoctest
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julia> Flux.chunk(1:10, 3)
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julia> Flux.chunk(1:10, 3)
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3-element Array{Array{Int64,1},1}:
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3-element Array{UnitRange{Int64},1}:
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1:4
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5:8
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9:10
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julia> Flux.chunk(collect(1:10), 3)
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3-element Array{SubArray{Int64,1,Array{Int64,1},Tuple{UnitRange{Int64}},true},1}:
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[1, 2, 3, 4]
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[1, 2, 3, 4]
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[5, 6, 7, 8]
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[5, 6, 7, 8]
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[9, 10]
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[9, 10]
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