Pytorch:将输入规范化添加到模型(分割层)

import torch
import torchvision.transforms as transforms

# 定义一个分割层，将输入数据分割成多个子集
class NormalizeLayer(torch.nn.Module):
    def __init__(self):
        super(NormalizeLayer, self).__init__()

    def forward(self, x):
        # 对每个子集进行规范化处理
        normalized_x = torch.nn.functional.normalize(x, dim=1)
        return normalized_x

# 创建一个数据转换管道，将分割层添加到管道中
transform = transforms.Compose([
    transforms.ToTensor(),
    NormalizeLayer()
])

# 加载数据集并应用数据转换管道
dataset = YourDataset(transform=transform)

# 创建模型并进行训练
model = YourModel()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = torch.nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    for images, labels in dataloader:
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()