Add weighted_crossentropy for imbalanced classification problems

src/layers/stateless.jl

@@ -4,8 +4,15 @@ using NNlib: log_fast
 
 mse(ŷ, y) = sum((ŷ .- y).^2)/length(y)
 
-crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat) =
+function crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat)
-  -sum(y .* log_fast.(ŷ)) / size(y, 2)
+  return -sum(y .* log_fast.(ŷ)) / size(y, 2)
+end
+
+function weighted_crossentropy(ŷ::AbstractVecOrMat, y::AbstractVecOrMat, w::AbstractVecOrMat)
+  return -sum(y .* log_fast.(ŷ) .* w) / size(y, 2)
+end
+
+
 
 @deprecate logloss(x, y) crossentropy(x, y)
 

test/layers/stateless.jl

@@ -0,0 +1,26 @@
+using Flux: onehotbatch, mse, crossentropy, weighted_crossentropy
+
+@testset "losses" begin
+  # First, regression-style y's
+  y = [1, 1, 0, 0]
+  y_hat = [.9, .1, .1, .9]
+
+  @testset "mse" begin
+    @test mse(y_hat, y) ≈ (.1^2 + .9^2)/2
+  end
+
+  # Now onehot y's
+  y = onehotbatch([1, 1, 0, 0], 0:1)
+  y_hat = [.1 .9; .9 .1; .9 .1; .1 .9]'
+  y_logloss = 1.203972804325936
+
+  @testset "crossentropy" begin
+    @test crossentropy(y_hat, y) ≈ y_logloss
+  end
+
+  @testset "weighted_crossentropy" begin
+    @test weighted_crossentropy(y_hat, y, ones(2)) ≈ y_logloss
+    @test weighted_crossentropy(y_hat, y, [.5, .5]) ≈ y_logloss/2
+    @test weighted_crossentropy(y_hat, y, [2, .5]) ≈ 1.5049660054074199
+  end
+end

test/runtests.jl

@@ -5,5 +5,6 @@ using Flux, Base.Test
 include("utils.jl")
 include("tracker.jl")
 include("layers/normalisation.jl")
+include("layers/stateless.jl")
 
 end