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