diff --git a/mu_map/training/cgan.py b/mu_map/training/cgan.py
index 3aa83ec2d64990ffe46b7e5593402517648b1cff..f970f8841b8aeefed63f064e60e74ab6fe99c921 100644
--- a/mu_map/training/cgan.py
+++ b/mu_map/training/cgan.py
@@ -12,48 +12,48 @@ LABEL_REAL = 1.0
 LABEL_FAKE = 0.0
 
 
-class GeneratorLoss(torch.nn.Module):
-    def __init__(
-        self,
-        # l2_weight: float = 1.0,
-        # gdl_weight: float = 1.0,
-        # adv_weight: float = 20.0,
-        # logger=None,
-    ):
-        super().__init__()
-
-        # self.l2 = torch.nn.MSELoss(reduction="mean")
-        self.l2 = torch.nn.L1Loss(reduction="mean")
-        self.l2_weight = l2_weight
-
-        self.gdl = GradientDifferenceLoss()
-        self.gdl_weight = gdl_weight
-
-        self.adv = torch.nn.MSELoss(reduction="mean")
-        self.adv_weight = adv_weight
-
-        if logger:
-            logger.debug(f"GeneratorLoss: {self}")
-
-    def __repr__(self):
-        return f"{self.l2_weight:.3f} * MSELoss + {self.gdl_weight:.3f} * GDLLoss + {self.adv_weight:.3f} * AdversarialLoss"
-
-    def forward(
-        self,
-        mu_maps_real: Tensor,
-        outputs_g: Tensor,
-        targets_d: Tensor,
-        outputs_d: Tensor,
-    ):
-        loss_l2 = self.l2(outputs_g, mu_maps_real)
-        loss_gdl = self.gdl(outputs_g, mu_maps_real)
-        loss_adv = self.adv(outputs_d, targets_d)
-
-        return (
-            self.l2_weight * loss_l2
-            + self.gdl_weight * loss_gdl
-            + self.adv_weight * loss_adv
-        )
+# class GeneratorLoss(torch.nn.Module):
+    # def __init__(
+        # self,
+        # # l2_weight: float = 1.0,
+        # # gdl_weight: float = 1.0,
+        # # adv_weight: float = 20.0,
+        # # logger=None,
+    # ):
+        # super().__init__()
+
+        # # self.l2 = torch.nn.MSELoss(reduction="mean")
+        # self.l2 = torch.nn.L1Loss(reduction="mean")
+        # self.l2_weight = l2_weight
+
+        # self.gdl = GradientDifferenceLoss()
+        # self.gdl_weight = gdl_weight
+
+        # self.adv = torch.nn.MSELoss(reduction="mean")
+        # self.adv_weight = adv_weight
+
+        # if logger:
+            # logger.debug(f"GeneratorLoss: {self}")
+
+    # def __repr__(self):
+        # return f"{self.l2_weight:.3f} * MSELoss + {self.gdl_weight:.3f} * GDLLoss + {self.adv_weight:.3f} * AdversarialLoss"
+
+    # def forward(
+        # self,
+        # mu_maps_real: Tensor,
+        # outputs_g: Tensor,
+        # targets_d: Tensor,
+        # outputs_d: Tensor,
+    # ):
+        # loss_l2 = self.l2(outputs_g, mu_maps_real)
+        # loss_gdl = self.gdl(outputs_g, mu_maps_real)
+        # loss_adv = self.adv(outputs_d, targets_d)
+
+        # return (
+            # self.l2_weight * loss_l2
+            # + self.gdl_weight * loss_gdl
+            # + self.adv_weight * loss_adv
+        # )
 
 
 class cGANTraining:
@@ -104,12 +104,12 @@ class cGANTraining:
         # )
 
         self.criterion_d = torch.nn.MSELoss(reduction="mean")
-        self.criterion_g = GeneratorLoss(
-            l2_weight=l2_weight,
-            gdl_weight=gdl_weight,
-            adv_weight=adv_weight,
-            logger=self.logger,
-        )
+        # self.criterion_g = GeneratorLoss(
+            # l2_weight=l2_weight,
+            # gdl_weight=gdl_weight,
+            # adv_weight=adv_weight,
+            # logger=self.logger,
+        # )
         self.criterion_l1 = torch.nn.L1Loss(reduction="mean")
 
     def run(self):
@@ -180,7 +180,7 @@ class cGANTraining:
             loss_d_fake = self.criterion_d(outputs_d_fake, labels_fake)
 
             # compute discriminator loss for real mu maps
-            inputs_d_real = torch.cat((recons, mu_maps), dim=1)
+            inputs_d_real = torch.cat((recons, mu_maps_real), dim=1)
             outputs_d_real = self.discriminator(inputs_d_real)  # note the detach, so that gradients are not computed for the generator
             loss_d_real = self.criterion_d(outputs_d_real, labels_real)