Loss function names in lowercase

src/layers/stateless.jl

@@ -59,9 +59,8 @@ end
     Kullback Leibler Divergence(KL Divergence)
 KLDivergence is a measure of how much one probability distribution is different from the other.
 It is always non-negative and zero only when both the distributions are equal everywhere.
-
 """
-function KLDivergence(ŷ, y)
+function kldivergence(ŷ, y)
   entropy = sum(y .* log.(y)) *1 //size(y,2)
   cross_entropy = crossentropy(ŷ, y)
   return entropy + cross_entropy
@@ -70,15 +69,13 @@ end
 """
     Poisson Loss function
 Poisson loss function is a measure of how the predicted distribution diverges from the expected distribution.
-
 """
-Poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) *1 // size(y,2)
+poisson(ŷ, y) = sum(ŷ .- y .* log.(ŷ)) *1 // size(y,2)
 
 """
     Logcosh Loss function
 """
-
 logcosh(ŷ, y) = sum(log.(cosh.(ŷ .- y)))
 
-Hinge(ŷ, y) = sum(max.(0.0, 1 .-  ŷ .* y)) *1 // size(y,2)
+hinge(ŷ, y) = sum(max.(0.0, 1 .-  ŷ .* y)) *1 // size(y,2)
 

test/layers/stateless.jl

@@ -52,23 +52,23 @@ const ϵ = 1e-7
   
   y = [1 2 3]
   y1 = [4.0 5.0 6.0]
-  @testset "KLDivergence" begin
-    @test Flux.KLDivergence(y, y1) ≈ 4.761838062403337
-    @test Flux.KLDivergence(y, y) ≈ 0 
+  @testset "kldivergence" begin
+    @test Flux.kldivergence(y, y1) ≈ 4.761838062403337
+    @test Flux.kldivergence(y, y) ≈ 0 
   end
   
   y = [1 2 3 4]
   y1 = [5.0 6.0 7.0 8.0]
-  @testset "Hinge" begin
-    @test Flux.Hinge(y, y1) ≈ 0
-    @test Flux.Hinge(y, 0.5 .* y) ≈ 0.125
+  @testset "hinge" begin
+    @test Flux.hinge(y, y1) ≈ 0
+    @test Flux.hinge(y, 0.5 .* y) ≈ 0.125
   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
+  @testset "poisson" begin
+    @test Flux.poisson(y, y1) ≈ 1.0160455586700767
+    @test Flux.poisson(y, y) ≈ 0.5044459776946685
   end
 
   @testset "logcosh" begin