From 99100b85545d01faec58f44570651cf86eec0d8e Mon Sep 17 00:00:00 2001
From: "Anthony D. Blaom" <anthony.blaom@gmail.com>
Date: Thu, 2 May 2024 08:57:02 +1200
Subject: [PATCH] use default_rng when comparing acceleration methods

---
 test/classifier.jl | 3 +--
 test/image.jl      | 6 +++---
 test/regressor.jl  | 4 ++--
 test/test_utils.jl | 1 -
 4 files changed, 6 insertions(+), 8 deletions(-)

diff --git a/test/classifier.jl b/test/classifier.jl
index 3c9f3fe9..df2d2f55 100644
--- a/test/classifier.jl
+++ b/test/classifier.jl
@@ -62,9 +62,8 @@ losses = []
     # check flux model is an improvement on predicting constant
     # distribution
     # (GPUs only support `default_rng`):
-    rng = accel == CPU1() ? StableRNGs.StableRNG(123) : Random.default_rng()
+    rng = Random.default_rng()
     seed!(rng, 123)
-    rng = StableRNGs.StableRNG(123)
     model = MLJFlux.NeuralNetworkClassifier(epochs=50,
                                             builder=builder,
                                             optimiser=optimiser,
diff --git a/test/image.jl b/test/image.jl
index 18aef194..5c0bb5bd 100644
--- a/test/image.jl
+++ b/test/image.jl
@@ -33,7 +33,7 @@ losses = []
 @testset_accelerated "ImageClassifier basic tests" accel begin
 
     # GPUs only support `default_rng`:
-    rng = accel == CPU1() ? StableRNGs.StableRNG(123) : Random.default_rng()
+    rng = Random.default_rng()
     seed!(rng, 123)
 
     model = MLJFlux.ImageClassifier(builder=builder,
@@ -86,7 +86,7 @@ losses = []
 @testset_accelerated "ColorImages" accel begin
 
     # GPUs only support `default_rng`:
-    rng = accel == CPU1() ? StableRNGs.StableRNG(123) : Random.default_rng()
+    rng = Random.default_rng()
     seed!(rng, 123)
 
     model = MLJFlux.ImageClassifier(builder=builder,
@@ -129,7 +129,7 @@ noise=0.2, color=true);
 @testset_accelerated "ImageClassifier basic tests" accel begin
 
     # GPUs only support `default_rng`:
-    rng = accel == CPU1() ? StableRNGs.StableRNG(123) : Random.default_rng()
+    rng = Random.default_rng()
     seed!(rng, 123)
 
     model = MLJFlux.ImageClassifier(epochs=5,
diff --git a/test/regressor.jl b/test/regressor.jl
index f5d1f783..f40ea171 100644
--- a/test/regressor.jl
+++ b/test/regressor.jl
@@ -42,7 +42,7 @@ train, test = MLJBase.partition(1:N, 0.7)
 
     # test model is a bit better than constant predictor:
     # (GPUs only support `default_rng`):
-    rng = accel == CPU1() ? StableRNGs.StableRNG(123) : Random.default_rng()
+    rng = Random.default_rng()
     seed!(rng, 123)
     model = MLJFlux.NeuralNetworkRegressor(builder=builder,
                                            acceleration=accel,
@@ -108,7 +108,7 @@ losses = []
 
     # test model is a bit better than constant predictor
     # (GPUs only support `default_rng`):
-    rng = accel == CPU1() ? StableRNGs.StableRNG(123) : Random.default_rng()
+    rng = Random.default_rng()
     seed!(rng, 123)
     model = MLJFlux.MultitargetNeuralNetworkRegressor(
         acceleration=accel,
diff --git a/test/test_utils.jl b/test/test_utils.jl
index 5a652bbf..63341e2d 100644
--- a/test/test_utils.jl
+++ b/test/test_utils.jl
@@ -164,7 +164,6 @@ function optimisertest(ModelType, X, y, builder, optimiser, accel)
                  @test isapprox(l1, l2)
              else
                  @test_broken isapprox(l1, l2, rtol=1e-8)
-                 @show l1/l2
              end
 
              # USING USER SPECIFIED RNG SEED (unsupported on GPU)