自定义数据集使用框架的线性回归方法对其进行拟合

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

# 生成示例数据
np.random.seed(42)
x = np.random.rand(100, 1)
y = 3 * x + 2 + np.random.randn(100, 1) * 0.1

# 转换为PyTorch张量
x_train = torch.tensor(x, dtype=torch.float32)
y_train = torch.tensor(y, dtype=torch.float32)


# 定义线性回归模型
class LinearRegressionModel(nn.Module):
    def __init__(self):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(1, 1)

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


# 实例化模型
model = LinearRegressionModel()

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

# 训练模型
num_epochs = 1000

for epoch in range(num_epochs):
    outputs = model(x_train)
    loss = criterion(outputs, y_train)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if