diff --git a/CIFAR10_playground.py b/CIFAR10_playground.py
index c725731..171140a 100755
--- a/CIFAR10_playground.py
+++ b/CIFAR10_playground.py
@@ -20,10 +20,6 @@ train_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=True,download=F
 test_dset = torchvision.datasets.CIFAR10(root='./CIFAR10',train=False,download=False,transform=transforms.ToTensor())
 train_loader = torch.utils.data.DataLoader(train_dset, batch_size=128, shuffle=True, num_workers=0)
 test_loader = torch.utils.data.DataLoader(test_dset, batch_size=128, shuffle=False, num_workers=0)
-train_dset.data.to(device)
-train_dset.target.to(device)
-test_dset.data.to(device)
-test_dset.target.to(device)
 #######################################################
 
 
@@ -252,7 +248,7 @@ for epoch in range(n_epochs):
     valid_loss = 0.0
     model.train()
     for idx,(img,label) in tqdm(enumerate(train_loader)):
-        # img, label=img.to(device), label.to(device)
+        img, label=img.to(device), label.to(device)
         optimizer.zero_grad()
         output = model(img)
         loss = criterion(output,label)
@@ -264,7 +260,7 @@ for epoch in range(n_epochs):
     correct = 0
     total = 0
     for idx,(img,label) in tqdm(enumerate(test_loader)):
-        # img, label=img.to(device), label.to(device)
+        img, label=img.to(device), label.to(device)
         output = model(img)
         loss = criterion(output, label)
         valid_loss += loss.item() * img.shape[0]