Pytorch实现线性回归

1. import torch
3. x_data = torch.Tensor([[1.0],[2.0],[3.0]])
4. y_data = torch.Tensor([[2.0],[4.0],[6.0]])
6. class MyLinear(torch.nn.Module):
7.     def __init__(self):
8.         super().__init__()
9.         self.linear = torch.nn.Linear(1,1)
10.         
11.     def forward(self, x):
12.         y_pred = self.linear(x)
13.         return y_pred
14.    
15. model = MyLinear()
16. criterion = torch.nn.MSELoss()
17. optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
19. for epoch in range(1000):
20.     y_pred = model(x_data)
21.     loss = criterion(y_pred,y_data)
22.     print('epoch==' + str(epoch), 'loss==' + str(loss.item()))
23.     optimizer.zero_grad()
24.     loss.backward()
25.     optimizer.step()
26.    
27. print('w=',model.linear.weight.item())
28. print('b=',model.linear.bias.item())
30. x_test = torch.tensor([[4.0]])
31. y_test = model(x_test)
32. print("y_test=",y_test.data)

复制代码

输出结果：


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