pytorch实战（一）-----逻辑回归

最新推荐文章于 2025-05-06 15:08:19 发布

转载最新推荐文章于 2025-05-06 15:08:19 发布 · 739 阅读

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本文通过使用PyTorch实现逻辑回归模型，并基于文本文件中的数据进行训练，展示了如何完成数据预处理、模型定义、损失函数设置及优化器选择等步骤。通过5万次迭代训练后，最终绘制了决策边界。

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

with open("data.txt") 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]
x_data=[[i[0],i[1]] for i in data]
x_data=torch.from_numpy(np.array(x_data)).float()
y_data=[i[-1] for i in data]
y_data=torch.from_numpy(np.array(y_data)).float()

x0=list(filter(lambda x:x[-1]==0.0,data))
x1=list(filter(lambda x:x[-1]==1.0,data))

plot_x0_0=[i[0] for i in x0]
plot_x0_1=[i[1] for i in x0]
plot_x1_0=[i[0] for i in x1]
plot_x1_1=[i[1] for i in x1]

plt.plot(plot_x0_0,plot_x0_1,'ro',label='x_0')
plt.plot(plot_x1_0,plot_x1_1,'bo',label='x_1')
plt.legend(loc='best')
# plt.show()

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

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

criterion=nn.BCELoss()
optimizer=optim.SGD(logistic_model.parameters(),lr=1e-3,momentum=0.9)


for epoch in range(50000):
    if torch.cuda.is_available():
        x=Variable(x_data).cuda()
        y=Variable(y_data).cuda()
    else:
        x = Variable(x_data)
        y = Variable(y_data)

    out=logistic_model(x)
    loss=criterion(out,y)
    print_loss=loss.data[0]
    mask=out.ge(0.5).float().squeeze()
    correct= (mask==y).sum()
    acc=correct.data[0].numpy()/x.size(0)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if (epoch+1)%1000==0:
        print('*'*10)
        print('epoch {}'.format(epoch+1))
        print('loss is {:.4f}'.format(print_loss))
        print('acc is {:.4f}'.format(acc))

w0,w1=logistic_model.lr.weight[0]
w0=w0.data.numpy()
w1=w1.data.numpy()
b=logistic_model.lr.bias.data[0].numpy()
plot_x=np.arange(30,100,0.1)
plot_y=(-w0*plot_x-b)/w1
plt.plot(plot_x,plot_y)
plt.show()