帮我用pytorch写一个伪标签学习的代码

以下是一个简单的伪标签学习代码的示例，可作为参考：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义模型
class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.fc1 = nn.Linear(10, 5)
        self.fc2 = nn.Linear(5, 1)
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.sigmoid(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        return x

# 定义训练函数
def train(model, optimizer, criterion, x_train, y_train, x_pseudo, y_pseudo):
    model.train()
    optimizer.zero_grad()
    # 使用真实标签训练
    output = model(x_train)
    loss = criterion(output, y_train)
    loss.backward()
    # 使用伪标签训练
    if len(x_pseudo) > 0:
        output_pseudo = model(x_pseudo)
        loss_pseudo = criterion(output_pseudo, y_pseudo)
        loss_pseudo.backward()
    optimizer.step()
    return loss.item()

# 定义测试函数
def test(model, criterion, x_test, y_test):
    model.eval()
    with torch.no_grad():
        output = model(x_test)
        loss = criterion(output, y_test)
        pred = np.round(output.cpu().numpy())
        acc = np.mean(pred == y_test.cpu().numpy())
    return loss.item(), acc

# 加载数据集
x_train = torch.randn(100, 10)
y_train = torch.randint(0, 2, (100, 1)).float()
x_test = torch.randn(50, 10)
y_test = torch.randint(0, 2, (50, 1)).float()

# 初始化模型、损失函数、优化器
model = Model()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# 开始训练
for epoch in range(10):
    # 生成伪标签
    with torch.no_grad():
        output = model(x_train)
        pred = np.round(output.cpu().numpy())
        x_pseudo = x_train[pred != y_train]
        y_pseudo = torch.tensor(pred[pred != y_train]).float().unsqueeze(-1)
    # 训练并输出损失
    loss = train(model, optimizer, criterion, x_train, y_train, x_pseudo, y_pseudo)
    print('Epoch {}, Loss: {:.4f}'.format(epoch, loss))
    # 测试并输出准确率
    test_loss, test_acc = test(model, criterion, x_test, y_test)
    print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(test_loss, test_acc))

在每个 epoch 中，我们先使用训练集的真实标签训练模型，然后使用模型预测出的伪标签训练模型。注意，伪标签的生成应该在不优化模型的情况下进行，以避免将错误的伪标签传递给模型。在训练完成后，使用测试集评估模型性能。