diff --git a/tac_ssl_tag.py b/tac_ssl_tag.py
index 0cb0fca..69159a3 100755
--- a/tac_ssl_tag.py
+++ b/tac_ssl_tag.py
@@ -25,7 +25,7 @@ def parse_option():
 
     parser.add_argument('--print_freq', type=int, default=10, help='print frequency')
     parser.add_argument('--save_freq', type=int, default=10, help='save frequency')
-    parser.add_argument('--batch_size', type=int, default=256, help='batch_size')
+    parser.add_argument('--batch_size', type=int, default=128, help='batch_size')
     parser.add_argument('--num_workers', type=int, default=18, help='num of workers to use')
     parser.add_argument('--epochs', type=int, default=61, help='number of training epochs')
     parser.add_argument('--num_layers', type=int, default=5, help='number of layers in resnet')
@@ -302,7 +302,7 @@ if __name__ == "__main__":
 
     # Initialize model
     nn_model = 'resnet18'
-    model = MultiModalMoCo(m=0.99, T=0.07, nn_model=nn_model).to(device)
+    model = MultiModalMoCo(m=0.99, T=0.2, nn_model=nn_model).to(device)
 
     # Initialize task
     if args.dataset == 'pretrain': 
@@ -316,12 +316,12 @@ if __name__ == "__main__":
        
     if task == "pretrain":
         # Initialize optimizer
-        modules = list(model.vision_base_q.parameters()) + list(model.tactile_base_q.parameters()) + list(model.phi_vision_q.parameters()) + list(model.phi_tactile_q.parameters())
-        optimizer = optim.Adam(modules, lr=0.03)
+        modules = list(model.vision_base_q.parameters()) + list(model.tactile_base_q.parameters()) + list(model.phi_vision_q.parameters()) + list(model.phi_tactile_q.parameters()) + \
+                 list(model.Phi_vision_q.parameters()) + list(model.Phi_tactile_q.parameters())
         classifier, criterion = None, None
     else:
         # Initialize training
-        #model.load_state_dict(torch.load('/home/cpsadmin/TAG/models/model_tag_238.pth'))
+        #model.load_state_dict(torch.load('/media/vedant/cpsDataStorageWK/Vedant/Tactile/TAG/models/model_tag_238.pth'))
         model.load_state_dict(torch.load('/media/vedant/cpsDataStorageWK/Vedant/Tactile/TAG/models/model_tag_pretrain_220.pth')['model'])
         classifier = LinearClassifierResNet(layer=args.num_layers, n_label=n_labels)
         optimizer = optim.Adam(classifier.parameters(), lr=1e-4)