diff --git a/sm-model/main.py b/sm-model/main.py
index 4286a41..3ddc4a4 100644
--- a/sm-model/main.py
+++ b/sm-model/main.py
@@ -85,9 +85,9 @@ if __name__ == "__main__":
     
     # debugging arguments
     ap.add_argument('--debugSingleBatch', action="store_true", help="will stop program after training 1 input batch")
-    ap.add_argument('--no_ext_feats', action="store_true", help="will not include external features in the model")
     ap.add_argument('--num_conv_filters', help="the number of convolution channels (lesser is faster)", default=100, type=int)
-    
+    ap.add_argument('--no_ext_feats', action="store_true", help="will not include external features in the model")    
+    ap.add_argument('--no_loss_reg', help="no loss regularization", action="store_true")
 
     args = ap.parse_args()
 
@@ -106,7 +106,7 @@ if __name__ == "__main__":
 
     torch.set_num_threads(args.num_threads)
     
-    trainer = Trainer(net, args.eta, args.mom)
+    trainer = Trainer(net, args.eta, args.mom, args.no_loss_reg)
 
     best_accuracy = 0.0
     best_model = 0
diff --git a/sm-model/train.py b/sm-model/train.py
index 74b5eaa..ab42975 100644
--- a/sm-model/train.py
+++ b/sm-model/train.py
@@ -27,11 +27,12 @@ logger.addHandler(ch)
 
 class Trainer(object):
     
-    def __init__(self, model, eta, mom):
+    def __init__(self, model, eta, mom, no_loss_reg):
         self.reg = 1e-5
+        self.no_loss_reg = no_loss_reg
         self.model = model
         self.criterion = nn.CrossEntropyLoss()
-        self.optimizer = optim.SGD(self.model.parameters(), lr=eta, momentum=mom, weight_decay=self.reg)
+        self.optimizer = optim.SGD(self.model.parameters(), lr=eta, momentum=mom, weight_decay=(0 if no_loss_reg else self.reg) ) 
 
 
     def regularize_loss(self, loss):       
@@ -60,8 +61,9 @@ class Trainer(object):
         logger.debug('loss after criterion {}'.format(loss))
 
         # NOTE: regularizing location 1
-        # loss = self.regularize_loss(loss)
-        # logger.debug('loss after regularizing {}'.format(loss))
+        # if not self.no_loss_reg:
+        #     loss = self.regularize_loss(loss)
+        #     logger.debug('loss after regularizing {}'.format(loss))
         
         loss.backward()
         
@@ -69,10 +71,10 @@ class Trainer(object):
         #logger.debug('params {}'.format([p for p in self.model.parameters()]))
         logger.debug('params grads {}'.format([p.grad for p in self.model.parameters()]))
        
-
         # NOTE: regularizing location 2. It would seem that location 1 is correct?
-        loss = self.regularize_loss(loss)
-        logger.debug('loss after regularizing {}'.format(loss))
+        if not self.no_loss_reg:
+            loss = self.regularize_loss(loss)
+            logger.debug('loss after regularizing {}'.format(loss))
 
         self.optimizer.step()