From 9367b9577168caa1e766b096db204c754d3128b3 Mon Sep 17 00:00:00 2001
From: Carlo Lucibello <carlo.lucibello@gmail.com>
Date: Sat, 12 Oct 2024 19:27:06 +0200
Subject: [PATCH] fixes

---
 test/ext_cuda/layers.jl | 4 +---
 test/ext_cuda/losses.jl | 5 +++--
 test/test_utils.jl      | 3 ++-
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/test/ext_cuda/layers.jl b/test/ext_cuda/layers.jl
index c148334dcf..0d985e59d1 100644
--- a/test/ext_cuda/layers.jl
+++ b/test/ext_cuda/layers.jl
@@ -11,9 +11,7 @@
 end
 
 
-const ACTIVATIONS = [identity, relu, tanh,
-                     sigmoid, exp, softplus,
-                     elu, selu]
+const ACTIVATIONS = [identity, tanh, softplus, elu]
 
 function gpu_gradtest(name::String, layers::Vector, x_cpu, args...; test_cpu = true, test_mode = false)
   @testset "$name GPU grad tests" begin
diff --git a/test/ext_cuda/losses.jl b/test/ext_cuda/losses.jl
index b5ba4d2f4d..e787e64881 100644
--- a/test/ext_cuda/losses.jl
+++ b/test/ext_cuda/losses.jl
@@ -27,8 +27,9 @@ y = [1  0  0  0  1
 @test focal_loss(x, y) ≈ focal_loss(gpu(x), gpu(y))
 
 @testset "GPU: $loss" for loss in ALL_LOSSES
-  x = rand(Float32, 3,4)
-  y = rand(Float32, 3,4)
+  # let's stay far from the boundaries to avoid problems with finite differences gradients
+  x = 0.1f0 + 0.8f0 .* rand(Float32, 3, 4)
+  y = 0.1f0 + 0.8f0 .* rand(Float32, 3, 4)
   @test loss(x, y) ≈ loss(gpu(x), gpu(y))
 
   test_gradients(loss, x, y, test_gpu=true, test_grad_f = false)
diff --git a/test/test_utils.jl b/test/test_utils.jl
index 522def9f96..a12d5fed66 100644
--- a/test/test_utils.jl
+++ b/test/test_utils.jl
@@ -14,7 +14,8 @@ const ALL_LOSSES = [Flux.Losses.mse, Flux.Losses.mae, Flux.Losses.msle,
 
 function finitediff_withgradient(f, x...)
     y = f(x...)
-    fdm = FiniteDifferences.central_fdm(5, 1)
+    # We set a range to avoid domain errors
+    fdm = FiniteDifferences.central_fdm(5, 1, max_range=1e-2)
     return y, FiniteDifferences.grad(fdm, f, x...)
 end