Logistic回归（分类问题）

代码

import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt

x_data=torch.Tensor([[1.0],[2.0],[3.0]])
y_data=torch.Tensor([[0],[0],[1]])

class LogisticRegressionModel(torch.nn.Module):
    def __init__(self):
        super(LogisticRegressionModel,self).__init__()
        self.linear=torch.nn.Linear(1,1)

    def forward(self,x):
        y_pred=F.sigmoid(self.linear(x))
        return y_pred

model=LogisticRegressionModel()

criterion=torch.nn.BCELoss(size_average=False)          #二元交叉熵损失
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)   #优化器是随机梯度下降

for epoch in range(1000):
    y_pred=model(x_data)
    loss=criterion(y_pred,y_data)
    print(epoch,'loss=',loss.item())

    optimizer.zero_grad()   #权重归零
    loss.backward()         #自动求导
    optimizer.step()        #更新

p=torch.Tensor([[2.5]])
q=model(p)
print('Predict:hours=2.5 Probability of Pass:',q.data.item())

x=np.linspace(0,10,200)
#将x变换为200*1的向量
x_t=torch.Tensor(x).view((200,1))   #view相当于numpy里的reshape
y_t=model(x_t)
y=y_t.data.numpy()
plt.plot(x,y)
plt.plot([0,10],[0.5,0.5],c='r')
plt.xlabel('Hours')
plt.ylabel('Probability of Pass')
plt.grid()
plt.show()

运行结果