Merge pull request #14 from JuliaSmoothOptimizers/PR-Codecov

Pr codecov
JuliaSmoothOptimizers · Jul 6, 2023 · 54a840d · 54a840d
2 parents 5ebf17d + cd5defe
commit 54a840d
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Showing 2 changed files with 31 additions and 4 deletions.
diff --git a/src/FluxNLPModels.jl b/src/FluxNLPModels.jl
@@ -55,8 +55,8 @@ function FluxNLPModel(
   chain_ANN::T,
   data_train,
   data_test;
-  current_training_minibatch = first(data_train),
-  current_test_minibatch = first(data_test),
+  current_training_minibatch = [],
+  current_test_minibatch = [],
   size_minibatch::Int = 100,
   loss_f::F = Flux.mse, #Flux.crossentropy,
 ) where {T <: Chain, F <: Function}
@@ -66,6 +66,10 @@ function FluxNLPModel(
   if (isempty(data_train) || isempty(data_test))
     error("train data or test is empty")
   end
+  if (isempty(current_training_minibatch) || isempty(current_test_minibatch))
+    current_training_minibatch = first(data_train)
+    current_test_minibatch = first(data_test)
+  end
 
   return FluxNLPModel(
     meta,

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -70,11 +70,29 @@ device = cpu #TODO should we test on GPU?
   println(norm(grad_x1 - grad_x1_2))
   @test norm(grad_x1 - grad_x1_2) ≈ 0.0
 
-  # @test grad_x1 ≈ grad_x1_2
-  # @test all(grad_x1  .≈ grad_x1_2)
   @test x1 == DNNLPModel.w
   @test Flux.params(DNNLPModel.chain)[1][1] == x1[1]
   @test Flux.params(DNNLPModel.chain)[1][2] == x1[2]
+
+  @test_throws Exception FluxNLPModel(DN, [], test_data) # if the train data is empty
+  @test_throws Exception FluxNLPModel(DN, train_data, []) # if the test data is empty
+  @test_throws Exception FluxNLPModel(DN, [], []) # if the both data is empty
+
+  # Testing if the value of the first batch was passed it
+  DNNLPModel_2 = FluxNLPModel(
+    DN,
+    train_data,
+    test_data,
+    current_training_minibatch = first(train_data),
+    current_test_minibatch = first(test_data),
+  )
+
+  #checking if we can call accuracy
+  train_acc = FluxNLPModels.accuracy(DNNLPModel_2; data_loader = train_data) # accuracy on train data
+  test_acc = FluxNLPModels.accuracy(DNNLPModel_2) # on the test data
+
+  @test train_acc >= 0.0
+  @test train_acc <= 1.0
 end
 
 @testset "minibatch tests" begin
@@ -88,5 +106,10 @@ end
   @test nlp.current_training_minibatch_status === nothing
   buffer_minibatch = deepcopy(nlp.current_training_minibatch)
   @test minibatch_next_train!(nlp) # should return true 
+  @test minibatch_next_train!(nlp) # should return true 
   @test !isequal(nlp.current_training_minibatch, buffer_minibatch)
+
+  reset_minibatch_test!(nlp)
+  @test minibatch_next_test!(nlp) # should return true 
+  @test minibatch_next_test!(nlp) # should return true 
 end