diff --git a/models/data_loaderFull.py b/models/data_loaderFull.py
index e157bf1043c18b1196c523f847bda4f4f40c8044..b95449d6596687d41b98189980fb8a41acc2c978 100644
--- a/models/data_loaderFull.py
+++ b/models/data_loaderFull.py
@@ -26,20 +26,14 @@ class HDF5Dataset(data.Dataset):
     def __getitem__(self, index):
         # get ECAL part
         x = self.get_data(index)
-        if self.transform:
-            x = torch.from_numpy(self.transform(x)).float()
-        else:
-            x = torch.from_numpy(x).float()
+        #x = torch.from_numpy(x).float()
         
         ## get HCAL part
         y = self.get_data_hcal(index)
-        if self.transform:
-            y = torch.from_numpy(self.transform(y)).float()
-        else:
-            y = torch.from_numpy(y).float()
+        #y = torch.from_numpy(y).float()
         
         
-        e = torch.from_numpy(self.get_energy(index))
+        e = self.get_energy(index)
         
 
         if torch.sum(x) != torch.sum(x): #checks for NANs
diff --git a/wganHCAL.py b/wganHCAL.py
index ced8009df9bd765bdd2f23dc1cb48aa009e06f32..f08fd6d1962fb3c4a24f0e627da0fab59ff7d2b9 100644
--- a/wganHCAL.py
+++ b/wganHCAL.py
@@ -57,9 +57,14 @@ def train(args, aD, aG, device, train_loader, optimizer_d, optimizer_g, epoch, e
     Tensor = torch.cuda.FloatTensor 
    
     for batch_idx, (dataE, dataH, energy) in enumerate(train_loader):
-        real_dataECAL = dataE.to(device).unsqueeze(1)
-        real_dataHCAL = dataH.to(device).unsqueeze(1)
-        real_label = energy.to(device)
+        #real_dataECAL = dataE.to(device).unsqueeze(1)
+        real_dataECAL = torch.from_numpy(dataE).to(device).unsqueeze(1).float()
+        
+        #real_dataHCAL = dataH.to(device).unsqueeze(1)
+        real_dataHCAL = torch.from_numpy(dataH).to(device).unsqueeze(1).float()
+        
+        #real_label = energy.to(device)
+        real_label = torch.from_numpy(energy).to(device).float()
         
         optimizer_d.zero_grad()
         
@@ -69,10 +74,10 @@ def train(args, aD, aG, device, train_loader, optimizer_d, optimizer_g, epoch, e
         fake_dataHCAL = aG(z, real_label, real_dataECAL).detach() ## 48 x 30 x 30        
 
         ## Critic fwd pass on Real
-        disc_real = aD(real_dataECAL.float(), real_dataHCAL.float(), real_label.float()) 
+        disc_real = aD(real_dataECAL, real_dataHCAL, real_label) 
 
         ## Calculate Gradient Penalty Term
-        gradient_penalty = calc_gradient_penalty(aD, real_dataECAL.float(), real_dataHCAL.float(), fake_dataHCAL, real_label, args.batch_size, device, layer=48, xsize=30, ysize=30)
+        gradient_penalty = calc_gradient_penalty(aD, real_dataECAL, real_dataHCAL, fake_dataHCAL, real_label, args.batch_size, device, layer=48, xsize=30, ysize=30)
 
         ## Critic fwd pass on Fake 
         disc_fake = aD(real_dataECAL, fake_dataHCAL.unsqueeze(1), real_label)