54 lines
1.3 KiB
Julia
54 lines
1.3 KiB
Julia
using NNlib: log_fast, logsoftmax, logσ
|
||
|
||
# Cost functions
|
||
|
||
mse(ŷ, y) = sum((ŷ .- y).^2)/length(y)
|
||
|
||
function crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
|
||
return -sum(y .* log_fast.(ŷ) .* weight) / size(y, 2)
|
||
end
|
||
|
||
@deprecate logloss(x, y) crossentropy(x, y)
|
||
|
||
function logitcrossentropy(logŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
|
||
return -sum(y .* logsoftmax(logŷ) .* weight) / size(y, 2)
|
||
end
|
||
|
||
"""
|
||
binarycrossentropy(ŷ, y)
|
||
|
||
Return `-y*log(ŷ) - (1-y)*log(1-ŷ)`.
|
||
|
||
julia> binarycrossentropy.(σ.([-1.1491, 0.8619, 0.3127]), [1, 1, 0.])
|
||
3-element Array{Float64,1}:
|
||
1.4244
|
||
0.352317
|
||
0.86167
|
||
"""
|
||
binarycrossentropy(ŷ, y) = -y*log_fast(ŷ) - (1 - y)*log_fast(1 - ŷ)
|
||
|
||
"""
|
||
logitbinarycrossentropy(logŷ, y)
|
||
|
||
`logitbinarycrossentropy(logŷ, y)` is mathematically equivalent to `binarycrossentropy(σ(logŷ), y)`
|
||
but it is more numerically stable.
|
||
|
||
julia> logitbinarycrossentropy.([-1.1491, 0.8619, 0.3127], [1, 1, 0.])
|
||
3-element Array{Float64,1}:
|
||
1.4244
|
||
0.352317
|
||
0.86167
|
||
"""
|
||
logitbinarycrossentropy(logŷ, y) = (1 - y)*logŷ - logσ(logŷ)
|
||
|
||
"""
|
||
normalise(x::AbstractVecOrMat)
|
||
|
||
Normalise each column of `x` to mean 0 and standard deviation 1.
|
||
"""
|
||
function normalise(x::AbstractVecOrMat)
|
||
μ′ = mean(x, 1)
|
||
σ′ = std(x, 1, mean = μ′)
|
||
return (x .- μ′) ./ σ′
|
||
end
|