diff --git a/mlpp/wgan/wgan.cpp b/mlpp/wgan/wgan.cpp
index fbf4e6d..72803a2 100644
--- a/mlpp/wgan/wgan.cpp
+++ b/mlpp/wgan/wgan.cpp
@@ -5,6 +5,9 @@
 //
 
 #include "wgan.h"
+
+#include "core/log/logger.h"
+
 #include "../activation/activation.h"
 #include "../cost/cost.h"
 #include "../lin_alg/lin_alg.h"
@@ -314,6 +317,7 @@ MLPPWGAN::DiscriminatorGradientResult MLPPWGAN::compute_discriminator_gradients(
 		Ref<MLPPHiddenLayer> layer = network[network.size() - 1];
 
 		layer->set_delta(alg.hadamard_productm(alg.outer_product(output_layer->get_delta(), output_layer->get_weights()), avn.run_activation_deriv_matrix(layer->get_activation(), layer->get_z())));
+
 		Ref<MLPPMatrix> hidden_layer_w_grad = alg.matmultm(alg.transposem(layer->get_input()), layer->get_delta());
 
 		data.cumulative_hidden_layer_w_grad.push_back(alg.additionm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well.
@@ -378,7 +382,8 @@ Vector<Ref<MLPPMatrix>> MLPPWGAN::compute_generator_gradients(const Ref<MLPPVect
 
 void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &output_set) {
 	MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, output_set));
-	std::cout << "Layer " << network.size() + 1 << ": " << std::endl;
+
+	PLOG_MSG("Layer " + itos(network.size() + 1) + ":");
 
 	MLPPUtilities::print_ui_vb(output_layer->get_weights(), output_layer->get_bias());
 
@@ -386,9 +391,9 @@ void MLPPWGAN::handle_ui(int epoch, real_t cost_prev, const Ref<MLPPVector> &y_h
 		for (int i = network.size() - 1; i >= 0; i--) {
 			Ref<MLPPHiddenLayer> layer = network[i];
 
-			std::cout << "Layer " << i + 1 << ": " << std::endl;
+			PLOG_MSG("Layer " + itos(i + 1) + ":");
 
-			MLPPUtilities::print_ui_vib(layer->get_weights(), layer->get_bias(), 0);
+			MLPPUtilities::print_ui_mb(layer->get_weights(), layer->get_bias());
 		}
 	}
 }