反向传播权重调整函数不起作用

import torch
import torch.nn as nn
import torch.optim as optim

# 定义一个简单的线性模型
class LinearModel(nn.Module):
    def __init__(self):
        super(LinearModel, self).__init__()
        self.linear = nn.Linear(1, 1)  # 输入和输出都是1维

    def forward(self, x):
        return self.linear(x)

# 创建模型实例
model = LinearModel()

# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)  # 初始学习率为0.01

# 模拟一些数据
x_train = torch.tensor([[1.0], [2.0], [3.0]])
y_train = torch.tensor([[2.0], [4.0], [6.0]])

# 训练模型
for epoch in range(100):
    # 前向传播
    outputs = model(x_train)
    loss = criterion(outputs, y_train)

    # 反向传播和优化
    optimizer.zero_grad()  # 清空梯度
    loss.backward()        # 计算梯度
    optimizer.step()       # 更新权重

    if (epoch+1) % 10 == 0:
        print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}')

# 测试模型
with torch.no_grad():
    predicted = model(x_train)
    print('Predicted:', predicted)