refactor test cases.

test/softmax.jl

@@ -5,8 +5,8 @@ using Statistics: mean
     @test softmax(Int[0, 0]) == [0.5, 0.5]
 end
 
-@testset "softmax dims" begin
-    xs = rand(fill(2,5)...)
+@testset "softmax on different dims" begin
+    xs = rand(fill(2, 5)...)
     out = similar(xs)
     for (fn!, fn) in [(softmax!, softmax), (logsoftmax!, logsoftmax)], i = 1:ndims(xs)
         @test fn!(out, xs; dims = i) == fn(xs; dims = i)
@@ -48,8 +48,8 @@ end
         -0.930163 0.0519798 0.0549979 0.3799 -0.477112 0.437428
         0.69246 0.569494 -0.503191 -0.925947 -0.0870738 -1.0697
     ]
-    @test isapprox(∇logsoftmax(ones(size(xs)), xs), ys; rtol = 1e-6)
-    @test isapprox(∇softmax(ones(size(xs)), xs), zeros(size(xs)); atol = 1e-6)
+    @test ∇logsoftmax(ones(size(xs)), xs) ≈ ys rtol = 1e-6
+    @test ∇softmax(ones(size(xs)), xs) ≈ zeros(size(xs)) atol = 1e-6
 end
 
 @testset "mutating softmax" begin
@@ -63,16 +63,16 @@ end
     ]) do xs
         out = similar(xs)
         softmax!(out, xs)
-        @test isapprox(out, softmax(xs); rtol = 1e-6)
+        @test out ≈ softmax(xs) rtol = 1e-6
         logsoftmax!(out, xs)
-        @test isapprox(out, logsoftmax(xs); rtol = 1e-6)
+        @test out ≈ logsoftmax(xs) rtol = 1e-6
 
         map([zeros, ones]) do fn
             Δ = fn(Float64, size(xs))
             ∇softmax!(out, Δ, xs)
-            @test isapprox(out, ∇softmax(Δ, xs); rtol = 1e-6)
+            @test out ≈ ∇softmax(Δ, xs) rtol = 1e-6
             ∇logsoftmax!(out, Δ, xs)
-            @test isapprox(out, ∇logsoftmax(Δ, xs); rtol = 1e-6)
+            @test out ≈ ∇logsoftmax(Δ, xs) rtol = 1e-6
         end
     end
 end