pytorch深度学习：线性回归

深度学习版线性回归，哈哈哈哈。

在潘神的嘲笑下，我发了这篇博客，呜呜。

 1 import torch
 2 from torch import nn,optim
 3 
 4 class LR(nn.Module):
 5     def __init__(self):
 6         super(LR, self).__init__()
 7         self.linear=nn.Linear(1,1)
 8 
 9     def forward(self, x):
10         out = self.linear(x)
11         return out
12 
13     def train(self,inputs,target,criterion,optimizer,epoches):
14         for epoch in range(epoches):
15             output = model.forward(inputs)
16             loss = criterion(output, target)
17             optimizer.zero_grad()
18             loss.backward()
19             optimizer.step()
20         return model, loss
21 
22 x=torch.Tensor([1,2,3,4,5,6])
23 y=x+torch.rand(6)
24 print(x)
25 print(y)
26 model=LR()
27 print(list(model.parameters()))
28 
29 inputs=torch.unsqueeze(x,dim=1)
30 target=torch.unsqueeze(y,dim=1)
31 criterion=nn.MSELoss()
32 optimizer = optim.SGD(model.parameters(), lr=1e-3)
33 
34 new_model,loss=model.train(inputs=inputs,target=target,criterion=criterion,optimizer=optimizer,epoches=10000)
35 print(list(new_model.parameters()))
36 print(loss.item())

 最后%一下来自华科的陈巨。

posted @ 2020-09-16 13:25  Lovaer  阅读(179)  评论(0)  编辑  收藏  举报