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Flux.jl
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Removed spurious promotions

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Adarsh Kumar 2020-02-06 01:06:41 +05:30 committed by GitHub
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commit 7710bb0b4b
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1 changed files with 8 additions and 6 deletions
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14
src/layers/stateless.jl
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@ -6,7 +6,7 @@ using NNlib: logsoftmax, logσ
mae(ŷ, y)
L1 loss function. Computes the mean of absolute error between prediction and true values
"""
mae(ŷ, y) = sum(abs.(ŷ.- y)) * 1 // length(y)
mae(ŷ, y) = sum(abs.(ŷ .- y)) * 1 // length(y)
"""
@ -42,9 +42,9 @@ Alias:
msle(ŷ,y;ϵ1=eps.(Float64.(ŷ)),ϵ2=eps.(Float64.(y)))
"""
mean_squared_logarithmic_error(ŷ, y;ϵ1=eps.(Float64.(ŷ)),ϵ2=eps.(Float64.(y))) = sum((log.(ŷ+ϵ1).-log.(y+ϵ2)).^2) * 1 // length(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.(Float64.(ŷ)),ϵ2=eps.(Float64.(y))) = sum((log.(ŷ+ϵ1).-log.(y+ϵ2)).^2) * 1 // length(y)
msle(ŷ, y;ϵ1=eps.(ŷ),ϵ2=eps.(eltype(ŷ).(y))) = sum((log.(ŷ+ϵ1).-log.(y+ϵ2)).^2) * 1 // length(y)
@ -74,12 +74,14 @@ Computes the mean of the Huber loss between prediction ŷ and true values y. By
"""
function huber_loss(ŷ, y,delta=1.0)
abs_error = abs.(ŷ.-y)
hub_loss =0
type_ = eltype(ŷ)
delta = type_(delta)
hub_loss =type_(0)
for i in 1:length(y)
if (abs_error[i]<=delta)
hub_loss+=abs_error[i]^2*0.5
hub_loss+=abs_error[i]^2*type_(0.5)
else
hub_loss+=delta*(abs_error[i]-0.5*delta)
hub_loss+=delta*(abs_error[i]-type_(0.5*delta))
end
return hub_loss*1//length(y)