diff --git a/IN_dataGenerator.py b/IN_dataGenerator.py
index 15bbea2..a111110 100644
--- a/IN_dataGenerator.py
+++ b/IN_dataGenerator.py
@@ -187,12 +187,12 @@ def main(args):
 
         for sub_X,sub_Y,sub_Z in tqdm.tqdm(data_train.generate_data(),total=n_train/batch_size):
             training = sub_X[2]
-            training_neu = sub_X[1]
+            #training_neu = sub_X[1]
             training_sv = sub_X[3]
             target = sub_Y[0]
             spec = sub_Z[0]
             trainingv = (torch.FloatTensor(training)).cuda()
-            trainingv_neu = (torch.FloatTensor(training_neu)).cuda()
+            #trainingv_neu = (torch.FloatTensor(training_neu)).cuda()
             trainingv_sv = (torch.FloatTensor(training_sv)).cuda()
             targetv = (torch.from_numpy(np.argmax(target, axis = 1)).long()).cuda()
             optimizer.zero_grad()
@@ -200,7 +200,7 @@ def main(args):
             
             #Input training dataset 
             if sv_branch: 
-                out = gnn(trainingv.cuda(), trainingv_sv.cuda(), trainingv_neu.cuda())
+                out = gnn(trainingv.cuda(), trainingv_sv.cuda())
             else: 
                 out = gnn(trainingv.cuda())
                 
@@ -213,18 +213,18 @@ def main(args):
         
         for sub_X,sub_Y,sub_Z in tqdm.tqdm(data_val.generate_data(),total=n_val/batch_size):
             training = sub_X[2]
-            training_neu = sub_X[1]
+            #training_neu = sub_X[1]
             training_sv = sub_X[3]
             target = sub_Y[0]
             spec = sub_Z[0]
             trainingv = (torch.FloatTensor(training)).cuda()
-            trainingv_neu = (torch.FloatTensor(training_neu)).cuda()
+            #trainingv_neu = (torch.FloatTensor(training_neu)).cuda()
             trainingv_sv = (torch.FloatTensor(training_sv)).cuda()
             targetv = (torch.from_numpy(np.argmax(target, axis = 1)).long()).cuda()
             
             #Input validation dataset 
             if sv_branch: 
-                out = gnn(trainingv.cuda(), trainingv_sv.cuda(), trainingv_neu.cuda())
+                out = gnn(trainingv.cuda(), trainingv_sv.cuda())
             else: 
                 out = gnn(trainingv.cuda())