Added tests for new loss functions

test/layers/stateless.jl

@@ -12,6 +12,20 @@ const ϵ = 1e-7
   @testset "mse" begin
     @test mse(ŷ, y) ≈ (.1^2 + .9^2)/2
   end
+  
+  @testset "mae" begin
+    @test Flux.mae(ŷ, y) ≈ 1/2
+  end
+  
+  @testset "huber_loss" begin
+    @test Flux.huber_loss(ŷ, y) ≈ 0.0012499999999999994
+  end       
+            
+  y = [123,456,789]
+  y1 = [345,332,789]
+  @testset "msle" begin
+    @test Flux.msle(y1, y) ≈ 0.38813985859136585
+  end
 
   # Now onehot y's
   y = onehotbatch([1, 1, 0, 0], 0:1)
@@ -64,18 +78,23 @@ const ϵ = 1e-7
     @test Flux.hinge(y, 0.5 .* y) ≈ 0.125
   end
   
+  @testset "squared_hinge" begin
+    @test Flux.squared_hinge(y, y1) ≈ 0
+    @test Flux.squared_hinge(y, 0.5 .* y) ≈ 0.0625
+  end
+  
   y = [0.1 0.2 0.3]
   y1 = [0.4 0.5 0.6]
   @testset "poisson" begin
     @test Flux.poisson(y, y1) ≈ 1.0160455586700767
     @test Flux.poisson(y, y) ≈ 0.5044459776946685
   end
-  
+            
   @testset "no spurious promotions" begin
     for T in (Float32, Float64)
       y = rand(T, 2)
       ŷ = rand(T, 2)
-      for f in (mse, crossentropy, logitcrossentropy, Flux.kldivergence, Flux.hinge, Flux.poisson)
+      for f in (mse, crossentropy, logitcrossentropy, Flux.kldivergence, Flux.hinge, Flux.poisson,Flux.mae,Flux.huber_loss,Flux.msle,Flux.squared_hinge)
         fwd, back = Flux.pullback(f, ŷ, y)
         @test fwd isa T
         @test eltype(back(one(T))[1]) == T