29 lines
713 B
Julia
29 lines
713 B
Julia
using NNlib: log_fast
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# Cost functions
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mse(ŷ, y) = sum((ŷ .- y).^2)/length(y)
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function crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat; weight = 1)
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return -sum(y .* log_fast.(ŷ) .* weight) / size(y, 2)
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end
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@deprecate logloss(x, y) crossentropy(x, y)
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function logitcrossentropy(logŷ::AbstractVecOrMat, y::AbstractVecOrMat)
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logŷ = logŷ .- maximum(logŷ, 1)
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ypred = logŷ .- log_fast.(sum(exp.(logŷ), 1))
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-sum(y .* ypred) / size(y, 2)
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end
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"""
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normalise(x::AbstractVecOrMat)
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Normalise each column of `x` to mean 0 and standard deviation 1.
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"""
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function normalise(x::AbstractVecOrMat)
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μ′ = mean(x, 1)
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σ′ = std(x, 1, mean = μ′)
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return (x .- μ′) ./ σ′
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end
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