pytorch实现一阶线性回归_torch 一阶;linear-优快云博客

本文链接：https://blog.youkuaiyun.com/qq_21944445/article/details/84862176

本文档详细介绍了如何使用PyTorch库来实现一阶线性回归模型，参考了《Deep Learning with PyTorch》这本书，涵盖了模型构建、训练过程和损失函数的计算等关键步骤。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

# -*- coding: utf-8 -*-
        
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt

x_train = np.array([range(20)], dtype=np.float32).T
y_train = x_train * 4.5 + 3 * np.array([[random.random() for i in range(20)]], dtype=np.float32).T

x_train = torch.from_numpy(x_train)
y_train = torch.from_numpy(y_train)


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


if torch.cuda.is_available():
    model = LinearRegression().cuda()
else:
    model = LinearRegression()

criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-4)

num_epochs = 2000
for epoch in range(num_epochs):
    if torch.cuda.is_available():
        inputs = Variable(x_train).cuda()
        target = Variable(y_train).cuda()
    else:
        inputs = Variable(x_train)
        target = Variable(y_train)

    out = model(inputs)
    loss = criterion(out, target)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (epoch+1) % 100 == 0:
        print('Epoch[{}/{}], loss: {:.6f}'.format(epoch+1, num_epochs, loss.data[0]))

model.eval()
predict = model(Variable(x_train))
predict = predict.data.numpy()
plt.plot(x_train.numpy(), y_train.numpy(), 'ro', label='data')
plt.plot(x_train.numpy(), predict, label='regression')
plt.legend()
plt.show()

参考文献：《deep learning with pytorch》