在PyTorch中打印来自数据加载器的随机样本

import torch
from torchvision import datasets, transforms

# 定义数据转换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

# 打印随机样本
data_iter = iter(train_loader)
images, labels = data_iter.next()
print(images.shape)  # 打印样本的形状
print(labels)  # 打印样本的标签