torch bceloss nan

当数据为空时，loss会为nan

  

   

    

     

    
    

     

      import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      a = torch.FloatTensor([])
     
    
   

    

     

    
    

     

      b = torch.FloatTensor([])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

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

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      if a.size(0)>0:
     
    
   

    

     

    
    

     

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

    

     

    
    

     

      print(x)
     
    
  
 

这个loss为0

  

   

    

     

    
    

     
 import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       a = torch.FloatTensor([])
     
    
   

    

     

    
    

     

       b = torch.FloatTensor([])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

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

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # if a.size(0) > 0:
     
    
   

    

     

    
    

     

       x = loss_fn(a, b)
     
    
   

    

     

    
    

     

       print(x.item())
     
    
  
 

这个loss也为nan

  

   

    

     

    
    

     
   import torch
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       a = torch.FloatTensor([]).cuda()
     
    
   

    

     

    
    

     

       b = torch.FloatTensor([]).cuda()
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

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

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # if a.size(0) > 0:
     
    
   

    

     

    
    

     

       x = loss_fn(a, b)
     
    
   

    

     

    
    

     

       print(x.item())
     
    
  
 

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/84030569