bceloss 和CrossEntropyLoss

  

   

    

     

    
    

     

      #!/usr/bin/env python
     
    
   

    

     

    
    

     

      # -*- coding: utf-8 -*-
     
    
   

    

     

    
    

     

      import time
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      v = 0.5  # 1-0.0001
     
    
   

    

     

    
    

     

      v1 = v - 0.01
     
    
   

    

     

    
    

     

      a = torch.FloatTensor([[v, v1, v],[v, v1, v]])
     
    
   

    

     

    
    

     

      b = torch.FloatTensor([[0, 0, 0],[1, 1, 1]])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      loss_fn = torch.nn.BCELoss()  # reduce=False, size_average=False)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      x = loss_fn(a, b).item()
     
    
   

    

     

    
    

     

      print(x)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      a=a.view(-1,2)
     
    
   

    

     

    
    

     

      print(a)
     
    
   

    

     

    
    

     

      b=b.long()
     
    
   

    

     

    
    

     

      cross= torch.nn.CrossEntropyLoss(reduction='sum')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      aaa= cross(a, b)
     
    
   

    

     

    
    

     

      print(aaa)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # tensor([[0.1933, 0.1425, 0.8572, 0.0224, 0.3811],
     
    
   

    

     

    
    

     
 # [0.6134, 0.9766, 0.6086, 0.0163, 0.1514]])
     
    
   

    

     

    
    

     
 # tensor([[2, 4, 0, 1, 3],
     
    
   

    

     

    
    

     
 # [1, 0, 2, 4, 3]])
     
    
   

    

     

    
    

     
 # tensor([[2, 3, 0, 4, 1],
     
    
   

    

     

    
    

     
 # [1, 0, 2, 4, 3]])
     
    
   

    

     

    
    

     
 #最小的索引在第2个位置,次小的索引在第3位
     
    
   

    

     

    
    

     
 # tensor([[False, False, True, False, False],
     
    
   

    

     

    
    

     
 # [True, True, True, False, False]])
     
    
   

    

     

    
    

     
 # tensor([0.8572, 0.6134, 0.9766, 0.6086])
     
    
  
 

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

原文链接：blog.csdn.net/jacke121/article/details/103266001