pytorch构建线性回归模型

好的，以下是使用PyTorch构建线性回归模型的步骤：

1. 导入必要的库和模块

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

2. 准备数据

# 生成随机数据
np.random.seed(2021)
x = np.random.rand(100, 1)
y = 3 * x + 2 + 0.2 * np.random.rand(100, 1)

# 转换为Tensor
x_tensor = torch.from_numpy(x).float()
y_tensor = torch.from_numpy(y).float()

3. 定义模型

class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)

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

model = LinearRegression()

4. 定义损失函数和优化器

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

5. 训练模型

num_epochs = 1000
for epoch in range(num_epochs):
    # 前向传播
    outputs = model(x_tensor)
    loss = criterion(outputs, y_tensor)

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

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

6. 测试模型

# 测试模型
model.eval()
with torch.no_grad():
    predicted = model(x_tensor).detach().numpy()

# 可视化结果
import matplotlib.pyplot as plt
plt.plot(x, y, 'ro', label='Original data')
plt.plot(x, predicted, label='Fitted line')
plt.legend()
plt.show()