118 lines
3.5 KiB
Julia
118 lines
3.5 KiB
Julia
using Test
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using Flux: onehotbatch, mse, crossentropy, logitcrossentropy,
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σ, binarycrossentropy, logitbinarycrossentropy
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const ϵ = 1e-7
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@testset "losses" begin
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# First, regression-style y's
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y = [1, 1, 0, 0]
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ŷ = [.9, .1, .1, .9]
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@testset "mse" begin
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@test mse(ŷ, y) ≈ (.1^2 + .9^2)/2
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end
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@testset "mae" begin
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@test Flux.mae(ŷ, y) ≈ 1/2
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end
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@testset "huber_loss" begin
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@test Flux.huber_loss(ŷ, y) ≈ 0.0012499999999999994
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end
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y = [123.0,456.0,789.0]
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y1 = [345.0,332.0,789.0]
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@testset "msle" begin
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@test Flux.msle(y1, y) ≈ 0.38813985859136585
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end
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# Now onehot y's
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y = onehotbatch([1, 1, 0, 0], 0:1)
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ŷ = [.1 .9; .9 .1; .9 .1; .1 .9]'
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v = log(.1 / .9)
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logŷ = [v 0.0; 0.0 v; 0.0 v; v 0.0]'
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lossvalue = 1.203972804325936
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@testset "crossentropy" begin
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@test crossentropy(ŷ, y) ≈ lossvalue
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end
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@testset "logitcrossentropy" begin
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@test logitcrossentropy(logŷ, y) ≈ lossvalue
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end
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@testset "weighted_crossentropy" begin
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@test crossentropy(ŷ, y, weight = ones(2)) ≈ lossvalue
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@test crossentropy(ŷ, y, weight = [.5, .5]) ≈ lossvalue/2
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@test crossentropy(ŷ, y, weight = [2, .5]) ≈ 1.5049660054074199
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end
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@testset "weighted_logitcrossentropy" begin
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@test logitcrossentropy(logŷ, y, weight = ones(2)) ≈ lossvalue
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@test logitcrossentropy(logŷ, y, weight = [.5, .5]) ≈ lossvalue/2
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@test logitcrossentropy(logŷ, y, weight = [2, .5]) ≈ 1.5049660054074199
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end
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logŷ, y = randn(3), rand(3)
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@testset "binarycrossentropy" begin
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@test binarycrossentropy.(σ.(logŷ), y; ϵ=0) ≈ -y.*log.(σ.(logŷ)) - (1 .- y).*log.(1 .- σ.(logŷ))
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@test binarycrossentropy.(σ.(logŷ), y) ≈ -y.*log.(σ.(logŷ) .+ eps.(σ.(logŷ))) - (1 .- y).*log.(1 .- σ.(logŷ) .+ eps.(σ.(logŷ)))
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end
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@testset "logitbinarycrossentropy" begin
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@test logitbinarycrossentropy.(logŷ, y) ≈ binarycrossentropy.(σ.(logŷ), y; ϵ=0)
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end
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y = [1 2 3]
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y1 = [4.0 5.0 6.0]
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@testset "kldivergence" begin
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@test Flux.kldivergence(y, y1) ≈ 4.761838062403337
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@test Flux.kldivergence(y, y) ≈ 0
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end
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y = [1 2 3 4]
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y1 = [5.0 6.0 7.0 8.0]
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@testset "hinge" begin
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@test Flux.hinge(y, y1) ≈ 0
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@test Flux.hinge(y, 0.5 .* y) ≈ 0.125
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end
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@testset "squared_hinge" begin
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@test Flux.squared_hinge(y, y1) ≈ 0
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@test Flux.squared_hinge(y, 0.5 .* y) ≈ 0.0625
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end
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y = [0.1 0.2 0.3]
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y1 = [0.4 0.5 0.6]
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@testset "poisson" begin
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@test Flux.poisson(y, y1) ≈ 1.0160455586700767
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@test Flux.poisson(y, y) ≈ 0.5044459776946685
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end
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y = [1.0 0.5 0.3 2.4]
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y1 = [0 1.4 0.5 1.2]
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@testset "dice_coeff_loss" begin
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@test Flux.dice_coeff_loss(y, y1) ≈ 0.2799999999999999
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@test Flux.dice_coeff_loss(y,y) ≈ 0.0
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end
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@testset "tversky_loss" begin
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@test Flux.tversky_loss(y,y1) ≈ 0.028747433264887046
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@test Flux.tversky_loss(y,y1,0.8) ≈ 0.050200803212851364
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@test Flux.tversky_loss(y,y) ≈ -0.5576923076923075
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end
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@testset "no spurious promotions" begin
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for T in (Float32, Float64)
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y = rand(T, 2)
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ŷ = rand(T, 2)
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for f in (mse, crossentropy, logitcrossentropy, Flux.kldivergence, Flux.hinge, Flux.poisson,Flux.mae,Flux.huber_loss,Flux.msle,Flux.squared_hinge,Flux.dice_coeff_loss,Flux.tversky_loss)
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fwd, back = Flux.pullback(f, ŷ, y)
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@test fwd isa T
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@test eltype(back(one(T))[1]) == T
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end
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end
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end
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end
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