PyTorch实现—Logistic回归，loss和acc可视化

冷不防 2023-06-09 07:58 20阅读 0赞

import matplotlib.pyplot as plt 
    import torch
    from torch import nn
    from torch.autograd import Variable
    import numpy as np
    import visdom
    
    viz = visdom.Visdom(env='train')
    loss_win = viz.line(np.arange(0.8))
    acc_win = viz.line(np.arange(0.8))
    
    #从data.txt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]
        x_data = np.array([(float(i[0]),float(i[1])) for i in data_list])
        y_data = np.array([[(float(i[2]))] for i in data_list])
        
    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_0')
    #plt.show()
    
    #获取训练数据
    x = torch.from_numpy(x_data).float()
    #y = torch.from_numpy(y_data)
    y = torch.FloatTensor(y_data)
    #print(x.size())
    #print(y)
    #定义模型
    class logisticRegression(nn.Module):
        def __init__(self):
            super().__init__()
            self.line = nn.Linear(2,1)
            self.smd = nn.Sigmoid()
            
        def forward(self,x):
            x = self.line(x)
            return self.smd(x) 
        
    logistic_model = logisticRegression()
    
    criterion = nn.BCELoss()
    optimizer = torch.optim.SGD(logistic_model.parameters(),lr = 1e-3)
    
    for epoch in range(80000):
        x = Variable(x)
        
        y = Variable(y)
        #print('****')
        #print(x)
        
        #==========forward=========
        out = logistic_model(x)
        
        loss = criterion(out,y)
        print_loss = loss.item()    
        #判断输出结果大于0.5就等于1，小于0.5就等于0
        #通过这个来计算模型分类的准确率
        mask = out.ge(0.5).float() 
        correct = (mask == y).sum()
        acc = correct.item()/x.size(0)
        #==========backward========
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        if(epoch + 1) % 100 == 0:
            print('*'*10)
            print('epoch{}'.format(epoch+1))
            print('loss is {:.4f}'.format(print_loss))
            print('acc is {:.4f}'.format(acc))
            viz.line(Y=np.array([print_loss]), X=np.array([epoch+1]), update='append', opts={'title':'loss'}, win=loss_win)
            viz.line(Y=np.array([acc]), X=np.array([epoch+1]), update='append', opts={'title':'acc'}, win=acc_win)
            
    weight = logistic_model.line.weight.data[0]
    w0, w1 = weight[0], weight[1]
    b = logistic_model.line.bias.data[0]
    plt.plot(plot_x0_0,plot_x0_1,'ro',label='x_0')
    plt.plot(plot_x1_0,plot_x1_1,'bo',label='x_0')
    plt.legend(loc = 'best')
    plot_x = torch.from_numpy(np.arange(30, 100, 0.1))
    plot_y = (-w0 * plot_x - b) / w1
    plt.plot(plot_x, plot_y)
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Fuc2hpZGFzaGVu_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Fuc2hpZGFzaGVu_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Fuc2hpZGFzaGVu_size_16_color_FFFFFF_t_70 2][]

本次实验分别测试训练60000次和80000的效果，以上loss和acc是训练60000之后的可视化数据图。训练80000次之后可视化效果更明显。

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Fuc2hpZGFzaGVu_size_16_color_FFFFFF_t_70]: /images/20230531/9d84947a81bf48519ff3824a3749e172.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Fuc2hpZGFzaGVu_size_16_color_FFFFFF_t_70 1]: /images/20230531/f3fa92135d464da7a145daf762373091.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Fuc2hpZGFzaGVu_size_16_color_FFFFFF_t_70 2]: /images/20230531/16bc3cc36844444d87f6c1fe6d842c2c.png