tensor和lable转化成DataLoader

import torch
from torch.utils.data import TensorDataset, DataLoader
 
# 假设有一些张量和对应的标签
tensors = [torch.randn(32, 10) for _ in range(100)]  # 示例张量列表
labels = torch.tensor([0, 1] * 50)  # 示例标签
 
# 创建TensorDataset
dataset = TensorDataset(tensors, labels)
 
# 创建DataLoader
batch_size = 16
train_dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
 
# 使用dataloader
for batch_index, (data, target) in enumerate(train_dataloader):
    # 在这里处理每个批次的数据和标签
    # 例如，可以将数据送入模型进行训练
    print(f"Batch Index: {batch_index}, Data Size: {data.size()}, Label Size: {target.size()}")