msle(ŷ, y; agg = mean, ϵ = eps(ŷ))

The loss corresponding to mean squared logarithmic errors, calculated as

			agg((log.(ŷ .+ ϵ) .- log.(y .+ ϵ)) .^ 2)

The ϵ term provides numerical stability. Penalizes an under-estimation more than an over-estimatation.

Example

			julia> Flux.msle(Float32[1.1, 2.2, 3.3], 1:3)
0.009084041f0

julia> Flux.msle(Float32[0.9, 1.8, 2.7], 1:3)
0.011100831f0