diff --git a/mlpp/wgan/wgan.cpp b/mlpp/wgan/wgan.cpp
index f46de94..fbf4e6d 100644
--- a/mlpp/wgan/wgan.cpp
+++ b/mlpp/wgan/wgan.cpp
@@ -636,18 +636,6 @@ std::tuple<std::vector<std::vector<std::vector<real_t>>>, std::vector<real_t>> M
 
 	if (!network.empty()) {
 		auto hiddenLayerAvn = network[network.size() - 1].activation_map[network[network.size() - 1].activation];
-
-		//std::cout << "=-------=--==-=-=-=" << std::endl;
-		//alg.printVector(outputLayer->delta);
-		//std::cout << "=-------=--==-=-=-=" << std::endl;
-		//alg.printVector(outputLayer->weights);
-
-		//std::cout << "=-------=--==-=-=-=" << std::endl;
-		//alg.printMatrix(alg.outerProduct(outputLayer->delta, outputLayer->weights));
-		//std::cout << "=-------=--==-=-=-=" << std::endl;
-		//alg.printMatrix((avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
-		//CRASH_NOW();
-
 		network[network.size() - 1].delta = alg.hadamard_product(alg.outerProduct(outputLayer->delta, outputLayer->weights), (avn.*hiddenLayerAvn)(network[network.size() - 1].z, 1));
 		std::vector<std::vector<real_t>> hiddenLayerWGrad = alg.matmult(alg.transpose(network[network.size() - 1].input), network[network.size() - 1].delta);
 
diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp
index e26b1fb..3c3edad 100644
--- a/test/mlpp_tests.cpp
+++ b/test/mlpp_tests.cpp
@@ -475,7 +475,7 @@ void MLPPTests::test_dynamically_sized_ann(bool ui) {
 	alg.printVector(ann.modelSetTest(alg.transpose(inputSet)));
 	std::cout << "ACCURACY: " << 100 * ann.score() << "%" << std::endl;
 }
-void MLPPTests::test_wgan(bool ui) {
+void MLPPTests::test_wgan_old(bool ui) {
 	//MLPPStat stat;
 	MLPPLinAlg alg;
 	//MLPPActivation avn;
@@ -496,6 +496,19 @@ void MLPPTests::test_wgan(bool ui) {
 	gan_old.gradientDescent(0.1, 55000, ui);
 	std::cout << "GENERATED INPUT: (Gaussian-sampled noise):" << std::endl;
 	alg.printMatrix(gan_old.generateExample(100));
+}
+void MLPPTests::test_wgan(bool ui) {
+	//MLPPStat stat;
+	MLPPLinAlg alg;
+	//MLPPActivation avn;
+	//MLPPCost cost;
+	//MLPPData data;
+	//MLPPConvolutions conv;
+
+	std::vector<std::vector<real_t>> outputSet = {
+		{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 },
+		{ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40 }
+	};
 
 	Ref<MLPPMatrix> output_set;
 	output_set.instance();
@@ -1250,6 +1263,7 @@ void MLPPTests::_bind_methods() {
 	ClassDB::bind_method(D_METHOD("test_soft_max_network", "ui"), &MLPPTests::test_soft_max_network, false);
 	ClassDB::bind_method(D_METHOD("test_autoencoder", "ui"), &MLPPTests::test_autoencoder, false);
 	ClassDB::bind_method(D_METHOD("test_dynamically_sized_ann", "ui"), &MLPPTests::test_dynamically_sized_ann, false);
+	ClassDB::bind_method(D_METHOD("test_wgan_old", "ui"), &MLPPTests::test_wgan_old, false);
 	ClassDB::bind_method(D_METHOD("test_wgan", "ui"), &MLPPTests::test_wgan, false);
 	ClassDB::bind_method(D_METHOD("test_ann", "ui"), &MLPPTests::test_ann, false);
 	ClassDB::bind_method(D_METHOD("test_dynamically_sized_mann", "ui"), &MLPPTests::test_dynamically_sized_mann, false);
diff --git a/test/mlpp_tests.h b/test/mlpp_tests.h
index 4124e93..a88d1b0 100644
--- a/test/mlpp_tests.h
+++ b/test/mlpp_tests.h
@@ -44,6 +44,7 @@ public:
 	void test_soft_max_network(bool ui = false);
 	void test_autoencoder(bool ui = false);
 	void test_dynamically_sized_ann(bool ui = false);
+	void test_wgan_old(bool ui = false);
 	void test_wgan(bool ui = false);
 	void test_ann(bool ui = false);
 	void test_dynamically_sized_mann(bool ui = false);