如何在假设策略上施加相对约束？

假设策略是指用于构建假设的函数或模型，而相对约束则是指在多个假设之间强制一些关系或条件。在机器学习中，相对约束通常指在分类任务中，同一输入样本的不同类别预测之间的关系。

下面是一个使用PyTorch的示例，其中对两个分类器施加相对约束来提高其性能：

import torch
import torch.nn as nn

class Classifier1(nn.Module):
    def __init__(self):
        super(Classifier1, self).__init__()
        self.fc1 = nn.Linear(10, 100)
        self.fc2 = nn.Linear(100, 2)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x
    
class Classifier2(nn.Module):
    def __init__(self):
        super(Classifier2, self).__init__()
        self.fc1 = nn.Linear(10, 100)
        self.fc2 = nn.Linear(100, 2)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# define loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer1 = torch.optim.SGD(classifier1.parameters(), lr=0.1)
optimizer2 = torch.optim.SGD(classifier2.parameters(), lr=0.1)

# train the models
for epoch in range(num_epochs):
    for inputs, labels in dataloader:
        # forward pass and compute loss for classifier 1
        optimizer1.zero_grad()
        outputs1 = classifier1(inputs)
        loss1 = criterion(outputs1, labels)
        loss1.backward()
        optimizer1.step()
        
        # forward pass and compute loss for classifier 2
        optimizer2.zero_grad()
        outputs2 = classifier2(inputs)
        loss2 = criterion(outputs2, labels)
        loss2.backward()
        optimizer2.step()
        
        # enforce the