使用pytorch进行线性回归

最新推荐文章于 2025-03-25 22:21:02 发布

转载最新推荐文章于 2025-03-25 22:21:02 发布 · 107 阅读

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原文链接：http://www.cnblogs.com/dudu1992/p/8980249.html

文章标签：

#数据结构与算法 #人工智能 #python

本文通过一组具体数据，使用PyTorch实现了一个简单的线性回归模型，并展示了完整的训练过程及模型效果。从数据准备到模型搭建、训练直至最终可视化结果，详细介绍了每个步骤的具体操作。


x,y
3.3,1.7
4.4,2.76
5.5,2.09
6.71,3.19
6.93,1.694
4.168,1.573
9.779,3.366
6.182,2.596
7.59,2.53
2.167,1.221
7.042,2.827
10.791,3.465
5.313,1.65
7.997,2.904
3.1,1.3

以上是欲拟合数据

import torch
from torch import nn, optim
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

d = pd.read_csv("data.csv")
x_train = np.array(d.x[:],dtype=np.float32).reshape(15,1)

print(x_train)
y_train=np.array(d.y[:],dtype=np.float32).reshape(15,1)
print(y_train)

x_train = torch.from_numpy(x_train)

y_train = torch.from_numpy(y_train)


# Linear Regression Model
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)  # input and output is 1 dimension

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


model = LinearRegression()
# 定义loss和优化函数
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-4)

# 开始训练
num_epochs = 1000
for epoch in range(num_epochs):
    inputs = Variable(x_train)
    target = Variable(y_train)

    # forward
    out = model(inputs)
    loss = criterion(out, target)
    # backward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 20 == 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='Original data')
plt.plot(x_train.numpy(), predict, label='Fitting Line')
# 显示图例
plt.legend()
plt.show()

# 保存模型
torch.save(model.state_dict(), './linear.pth')