pytorch线性回归代码

很高兴为您服务！以下是一个简单的PyTorch线性回归代码示例：

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

# 定义输入和输出
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
            [9.779], [6.182], [7.59], [2.167], [7.042], 
            [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)

y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
            [3.366], [2.596], [2.53], [1.221], [2.827], 
            [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)

# 转换为张量
X_train = torch.from_numpy(x_train)
y_train = torch.from_numpy(y_train)

# 定义模型
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)  # 输入和输出的维度都为1

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

model = LinearRegression()

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

# 训练模型
num_epochs = 1000
for epoch in range(num_epochs):
    # forward
    outputs = model(X_train)
    loss = criterion(outputs, y_train)

    # backward
    optimizer.zero_grad()  # 梯度清零
    loss.backward()

    # update
    optimizer.step()

    if (epoch+1) % 50 == 0:
        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试模型
predicted = model(X_train).detach().numpy()
print(predicted)

# 保存模型
torch.save(model.state_dict(), 'model.ckpt')

希望可以帮到您！