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Updated Msle loss

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Adarsh Kumar 2020-02-27 16:26:17 +05:30 committed by GitHub
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src/layers/stateless.jl
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@ -17,33 +17,28 @@ mse(ŷ, y) = sum((ŷ .- y).^2) * 1 // length(y)
"""
mean_squared_logarithmic_error(ŷ, y;ϵ1=eps.(Float64.(ŷ)),ϵ2=eps.(Float64.(y)))
msle(ŷ, y;ϵ1=eps.(Float64.(ŷ)),ϵ2=eps.(Float64.(y)))
L2 loss function. Returns the mean of the squared logarithmic errors of prediction ŷ, and true values y. The ϵ1 and ϵ2 terms provide numerical stability.
Mean Squared Logarithmic Error,an L2 loss function. Returns the mean of the squared logarithmic errors of prediction ŷ, and true values y. The ϵ1 and ϵ2 terms provide numerical stability.
(Computes mean of squared(log(predicted values)-log(true value)). This error penalizes an under-predicted estimate greater than an over-predicted estimate.
```julia
julia> y_=[14726,327378,74734]
julia> y=[14726,327378,74734]
3-element Array{Int64,1}:
14726
327378
74734
julia> y = [12466.1,16353.95,16367.98]
julia> ŷ = [12466.1,16353.95,16367.98]
3-element Array{Float64,1}:
12466.1
16353.95
16367.98
julia> mean_squared_logarithmic_error(y,y_)
julia> msle(ŷ,y)
3.771271382334686
```
Alias:
msle(ŷ,y;ϵ1=eps.(Float64.(ŷ)),ϵ2=eps.(Float64.(y)))
"""
mean_squared_logarithmic_error(ŷ, y;ϵ1=eps.(ŷ),ϵ2=eps.(eltype(ŷ).(y))) = sum((log.(ŷ+ϵ1).-log.(y+ϵ2)).^2) * 1 // length(y)
#Alias
msle(ŷ, y;ϵ1=eps.(ŷ),ϵ2=eps.(eltype(ŷ).(y))) = sum((log.(ŷ+ϵ1).-log.(y+ϵ2)).^2) * 1 // length(y)