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