diff --git a/GanAttack.py b/GanAttack.py
index 4335c6b..31a8bd6 100644
--- a/GanAttack.py
+++ b/GanAttack.py
@@ -76,7 +76,7 @@ def main(cfg: DictConfig) -> None:
     criterion = nn.CrossEntropyLoss()
     max_loss=nn.MarginRankingLoss(0.1)
     clip_loss=CLIPLoss().to(device)
-#     vgg_loss=VggLoss().to(device)
+    # vgg_loss=VggLoss().to(device)
 #     summary(model, input_size = (3, 256, 256), batch_size = 5)
 #     set_requires_grad(model.mlp.parameters())
     for p in (model.mlp.parameters()):
@@ -97,12 +97,14 @@ def main(cfg: DictConfig) -> None:
             # _, _, _, clean_refine_images, clean_latent_codes, _=inverter(inputs,img_path)
             optimizer.zero_grad()
             generated_img,adv_latent_codes=model(inputs)
-            loss_vgg=vgg_loss(inputs,generated_img)
-#             loss_l1=F.l1_loss(codes,adv_latent_codes)
+            # loss_vgg=vgg_loss(inputs,generated_img)
+            loss_l1=F.l1_loss(codes,adv_latent_codes)
             loss_clip=clip_loss(generated_img,prompt)
-            outputs = classifier(generated_img)
-            preds=criterion(outputs,labels)
-            loss_classifier=max_loss(torch.ones_like(preds),preds,-torch.ones_like(preds))
+            adv_outputs = classifier(generated_img)
+            clean_outputs=classifier(inputs)
+            adv_preds=criterion(adv_outputs,labels)
+            clean_preds=criterion(clean_outputs,labels)
+            loss_classifier=max_loss(clean_preds,adv_preds,torch.ones_like(preds))
             # _, preds = torch.max(classifier(generated_img), 1)
             # loss_classifier=max_loss(torch.ones_like(criterion(outputs, labels)),criterion(outputs, labels),criterion(outputs, labels))