PyTorch深度学习实践 （六）---逻辑斯蒂回归

 1 import torch
 2 
 3 #data
 4 x_data = torch.Tensor([[1.0], [2.0], [3.0]])
 5 y_data = torch.Tensor([[0], [0], [1]])
 6 
 7 #model
 8 class LogisticRegressionModel(torch.nn.Module):
 9     def __init__(self):
10         super(LogisticRegressionModel, self).__init__()
11         self.linear = torch.nn.Linear(1, 1)
12 
13     def forward(self, x):
14         y_pred = torch.sigmoid(self.linear(x))
15         return y_pred
16 
17 
18 
19 model=LogisticRegressionModel()
20 
21 # loss & optimizer
22 criterion=torch.nn.BCELoss(size_average=False)
23 optimizer=torch.optim.SGD(model.parameters(),lr=0.01)
24 
25 #forward & backward & updata
26 for epoch in range(100):
27     y_pred=model(x_data)
28     l=criterion(y_pred,y_data)
29     print("epoch=",epoch,"loss=",l.item())
30 
31     optimizer.zero_grad()
32     l.backward()
33     optimizer.step()
34 
35 print (‘w = ‘,model.linear.weight.item())
36 print(‘b = ‘,model.linear.bias.item())
37 
38 x_test = torch.Tensor([[4.0]])
39 y_test = model(x_test)
40 print(‘y_pred = ‘, y_test.data)