PyTorch学习之路：Logistic回归

最新推荐文章于 2025-06-22 21:34:53 发布

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最新推荐文章于 2025-06-22 21:34:53 发布

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分类专栏： PyTorch学习之路文章标签： PyTorch

本文链接：https://blog.youkuaiyun.com/MaybeForever/article/details/88639711

本代码参考廖星宇《深度学习入门之PyTorch》中示例代码，手动复现而来，仅供个人学习使用，侵删。

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

#逻辑回归模型
class LogisticRegression(nn.Module):
    def __init__(self):
        super(LogisticRegression, self).__init__()
        self.lr = nn.Linear(2, 1)
        self.sm = nn.Sigmoid()

    def forward(self, x):
        x = self.lr(x)
        x = self.sm(x)
        return x

if __name__ == '__main__':
    #从data.txt中读取数据
    with open('data.txt', 'r') as f:
        data_list = f.readlines()
        data_list = [i.split('\n')[0] for i in data_list]
        data_list = [i.split(',') for i in data_list]
        data = [(float(i[0]), float(i[1]), float(i[2])) for i in data_list]
        data = torch.Tensor(data)

    logistic_model = LogisticRegression()
    if torch.cuda.is_available():
        logistic_model.cuda()

    #定义损失函数和优化函数
    criterion