pytorch不同的层设置不同的学习率

  

   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     

      from torch import nn, optim
     
    
   

    

     

    
    

     

      from torch.autograd import Variable
     
    
   

    

     

    
    

     

      import numpy as np
     
    
   

    

     

    
    

     

      import matplotlib.pyplot as plt
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
     
    
   

    

     

    
    

     

                          [9.779], [6.182], [7.59], [2.167], [7.042],
     
    
   

    

     

    
    

     

                          [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
     
    
   

    

     

    
    

     

                          [3.366], [2.596], [2.53], [1.221], [2.827],
     
    
   

    

     

    
    

     

                          [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
     
    
   

    

     

    
    

     

      x_train = torch.from_numpy(x_train)
     
    
   

    

     

    
    

     

      y_train = torch.from_numpy(y_train)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # Linear Regression Model
     
    
   

    

     

    
    

     

      class LinearRegression(nn.Module):
     
    
   

    

     

    
    

     
    def __init__(self):
     
    
   

    

     

    
    

     
        super(LinearRegression, self).__init__()
     
    
   

    

     

    
    

     

              self.linear1 = nn.Linear(1, 5)  # input and output is 1 dimension
     
    
   

    

     

    
    

     

              self.linear2 = nn.Linear(5, 1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
    def forward(self, x):
     
    
   

    

     

    
    

     

              out = self.linear1(x)
     
    
   

    

     

    
    

     

              out = self.linear2(out)
     
    
   

    

     

    
    

     
        return out
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      model = LinearRegression()
     
    
   

    

     

    
    

     

      print(model.linear1)
     
    
   

    

     

    
    

     

      # 微调：自定义每一层的学习率
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      # 定义loss和优化函数
     
    
   

    

     

    
    

     

      criterion = nn.MSELoss()
     
    
   

    

     

    
    

     

      optimizer = optim.SGD(
     
    
   

    

     

    
    

     

          [{"params": model.linear1.parameters(), "lr": 0.01},
     
    
   

    

     

    
    

     

           {"params": model.linear2.parameters()}],
     
    
   

    

     

    
    

     

          lr=0.02)
     
    
  
 

文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。

原文链接：wanghao.blog.csdn.net/article/details/114600211