我在尝试实现一个用于识别MNIST数据集中数字的CNN，我的代码在数据加载过程中出现了错误。我不明白为什么会发生这种情况。

import torchimport torchvisionimport torchvision.transforms as transformstransform = transforms.Compose([    transforms.ToTensor(),    transforms.Normalize((0.5), (0.5))])trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)trainloader = torch.utils.data.DataLoader(trainset, batch_size=20, shuffle=True, num_workers=2)testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)testloader = torch.utils.data.DataLoader(testset, batch_size=20, shuffle=False, num_workers=2)for i, data in enumerate(trainloader, 0):    inputs, labels = data[0], data[1]

错误:

---------------------------------------------------------------------------IndexError                                Traceback (most recent call last)<ipython-input-6-b37c638b6114> in <module>      2 ----> 3     for i, data in enumerate(trainloader, 0):      4         inputs, labels = data[0], data[1]# ...IndexError: Traceback (most recent call last):  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 99, in _worker_loop    samples = collate_fn([dataset[i] for i in batch_indices])  File "/opt/conda/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 99, in <listcomp>    samples = collate_fn([dataset[i] for i in batch_indices])  File "/opt/conda/lib/python3.6/site-packages/torchvision/datasets/mnist.py", line 95, in __getitem__    img = self.transform(img)  File "/opt/conda/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 61, in __call__    img = t(img)  File "/opt/conda/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 164, in __call__    return F.normalize(tensor, self.mean, self.std, self.inplace)  File "/opt/conda/lib/python3.6/site-packages/torchvision/transforms/functional.py", line 208, in normalize    tensor.sub_(mean[:, None, None]).div_(std[:, None, None])IndexError: too many indices for tensor of dimension 0

回答：

问题在于mean和std必须是序列（例如元组），因此您应该在这些值后面添加一个逗号:

transform = transforms.Compose([    transforms.ToTensor(),    transforms.Normalize((0.5,), (0.5,))])

注意(0.5)和(0.5,)之间的区别。您可以在这里查看这些值是如何使用的。如果您应用相同的过程，您会看到:

import torchx1 = torch.as_tensor((0.5))x2 = torch.as_tensor((0.5,))print(x1.shape, x1.ndim)  # output: torch.Size([]) 0print(x2.shape, x2.ndim)  # output: torch.Size([1]) 1

您可能不知道，但它们在Python中也是不同的:

type((0.5))   # <type 'float'>type((0.5,))  # <type 'tuple'>