e6c8024fcceb9b47b82425a588988a63a365d544,09_softmax_mnist.py,,,#,12

Before Change

    ])),
    batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST("../data", train=False, transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])),
    batch_size=batch_size, shuffle=True)

After Change

                               transform=transforms.ToTensor(),
                               download=True)

test_dataset = datasets.MNIST(root="./data/",
                              train=False,
                              transform=transforms.ToTensor())

// Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,

Instances