在 PyTorch 中规范化 MNIST

Question

我正在尝试将 PyTorch 1.9 和 Python 3.8 中的 MNIST 数据集规范化到范围 [0, 1] 之间，代码为 (batch_size = 32)。

# Specify path to MNIST dataset-
path_to_data = "path_to_dataset"

# Define transformation(s) to be applied to dataset-
transforms_MNIST = transforms.Compose(
    [
        transforms.ToTensor(),
        transforms.Normalize(mean = (0.1307,), std = (0.3081,))
    ]
)

# Load MNIST dataset-
train_dataset = torchvision.datasets.MNIST(
        # root = './data', train = True,
        root = path_to_data + "data", train = True,
        transform = transforms_MNIST, download = True
        )

test_dataset = torchvision.datasets.MNIST(
        # root = './data', train = False,
        root = path_to_data + "data", train = False,
        transform = transforms_MNIST
        )

# Create training and testing dataloaders-
train_loader = torch.utils.data.DataLoader(
        dataset = train_dataset, batch_size = batch_size,
        shuffle = True
        )

test_loader = torch.utils.data.DataLoader(
        dataset = test_dataset, batch_size = batch_size,
        shuffle = False
        )

print(f"Sizes of train_dataset: {len(train_dataset)} and test_dataet: {len(test_dataset)}")
print(f"Sizes of train_loader: {len(train_loader)} and test_loader: {len(test_loader)}")
# Sizes of train_dataset: 60000 and test_dataet: 10000
# Sizes of train_loader: 1875 and test_loader: 313

# Sanity check-
print(f"train_dataset: min pixel value = {train_dataset.data.min().numpy():.3f} &"
      f" max pixel value = {train_dataset.data.max().numpy():.3f}")
# train_dataset: min pixel value = 0.000 & max pixel value = 255.000

print(f"test_dataset: min pixel value = {test_dataset.data.min().numpy():.3f} &"
      f" max pixel value = {test_dataset.data.max().numpy():.3f}")
# test_dataset: min pixel value = 0.000 & max pixel value = 255.000

print(f"len(train_loader) = {len(train_loader)} & len(test_loader) = {len(test_loader)}")
# len(train_loader) = 1875 & len(test_loader) = 313

# Sanity check-
len(train_dataset) / batch_size, len(test_dataset) / batch_size
# (1875.0, 312.5)

# Get some random batch of training images & labels-
images, labels = next(iter(train_loader))

# You get x images due to the specified batch size-
print(f"images.shape: {images.shape} & labels.shape: {labels.shape}")
# images.shape: torch.Size([32, 1, 28, 28]) & labels.shape: torch.Size([32])

# Get min and max values for normalized pixels in mini-batch-
images.min(), images.max()
# (tensor(-0.4242), tensor(2.8215))

'images' 的最小值和最大值应介于 0 和 1 之间，而是 0.4242 和 2.8215。出了什么问题？

Answer 1

发生这种情况是因为 Normalize 应用了实际上（也）称为 标准化的东西 ：output = (input - mean) / std.

加载图像时会自动执行您想要实现的规范化，因此您可以发表评论Normalize。

在 PyTorch 中规范化 MNIST

Normalize MNIST in PyTorch

mnist

pytorch

pytorch-dataloader