pytorch 深度学习入门代码（一）线性回归代码实现

最新推荐文章于 2025-06-24 20:38:10 发布

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#pytorch 线性回归 #深度学习 #torch #pytorch深度学习 #深度学习入门

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本文通过使用PyTorch框架实现了一维线性回归模型，并通过具体的训练数据集进行了模型训练与验证。演示了如何定义模型结构、损失函数、优化器，并通过迭代训练提升模型性能。

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"""一维线性回归代码实现"""

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

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 __name__ == '__main__':
    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)

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

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

    criterion = nn.MSELoss()
    optimizer = optim.SGD(model.parameters(), lr=1e-3)


    num_epochs = 1000
    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)

        # forward
        out = model.forward(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.item()))

    model.eval()
    model.cpu()
    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')
    plt.show()

结果图