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Loss总结

L1Loss

mean absolute error

MSELoss

NLLLoss

negative log likelihood loss，用于多分类问题

• 输入
  • x： size = （batch，class，* ）， x储存每个类的log-probabilities，在神经网络添加LogSoftmax层
  • y：size = （batch，* ）
• 参数
  • weight：1D Tensor，每个类的weight
  • ignore_index：忽略类的index
  • reduction：'mean’和‘sum’是标量，对所有元素进行mean或者sum

CrossEntropyLoss

结合了LogSoftmax和NLLLoss

输入和NLLLoss一样，参数也相同。

BCELoss

Binary Cross Entropy

• 输入
  • x：size = （N，*），由于是2分类问题，所以没有类的信息，神经网络的最后一层应是Sigmoid，将x归入[0,1]
  • y：size = （N，*），元素非0即1
• 参数
  • reduction
  • weight：下面代码实验weight的选取

import torch.nn as nnimport torchm = nn.Sigmoid()input = torch.randn(4, 5, requires_grad=True)target = torch.empty(4, 5).random_(2)input = m(input)loss1 = nn.BCELoss(reduction='none')output1 = loss1(input, target)print("output1:\n", output1)#w2 = torch.empty(4).random_(2)#loss2 = nn.BCELoss(reduction='none', weight=w2)#output2 = loss2(input, target)#print(w2, output2)w3 = torch.empty(4, 1).random_(2)loss3 = nn.BCELoss(reduction='none', weight=w3)output3 = loss3(input, target)print("w3:\n", w3, "\noutput3:\n", output3)w4 = torch.empty(5).random_(2)loss4 = nn.BCELoss(reduction='none', weight=w4)output4 = loss4(input, target)print("w4:\n", w4, "\noutput4:\n", output4)w5 = torch.empty(1, 5).random_(2)loss5 = nn.BCELoss(reduction='none', weight=w5)output5 = loss5(input, target)print("w5:\n", w5, "\noutput5:\n", output5)w6 = torch.empty(4, 5).random_(2)loss6 = nn.BCELoss(reduction='none', weight=w6)output6 = loss6(input, target)print("w6:\n", w6, "\noutput6:\n", output6)

结果：

output1: tensor([[1.2874, 1.0905, 2.1070, 0.3826, 2.0119],        [0.3760, 0.3198, 1.0668, 0.8324, 1.1959],        [1.1589, 0.7380, 0.7686, 0.7132, 0.5279],        [1.0708, 1.2377, 0.3109, 0.8353, 0.4442]],       grad_fn=
   
    )w3: tensor([[0.],        [0.],        [1.],        [1.]]) output3: tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],        [1.1589, 0.7380, 0.7686, 0.7132, 0.5279],        [1.0708, 1.2377, 0.3109, 0.8353, 0.4442]],       grad_fn=
    
     )w4: tensor([1., 1., 0., 0., 0.]) output4: tensor([[1.2874, 1.0905, 0.0000, 0.0000, 0.0000],        [0.3760, 0.3198, 0.0000, 0.0000, 0.0000],        [1.1589, 0.7380, 0.0000, 0.0000, 0.0000],        [1.0708, 1.2377, 0.0000, 0.0000, 0.0000]],       grad_fn=
     
      )w5: tensor([[1., 1., 1., 0., 1.]]) output5: tensor([[1.2874, 1.0905, 2.1070, 0.0000, 2.0119],        [0.3760, 0.3198, 1.0668, 0.0000, 1.1959],        [1.1589, 0.7380, 0.7686, 0.0000, 0.5279],        [1.0708, 1.2377, 0.3109, 0.0000, 0.4442]],       grad_fn=
      
       )w6: tensor([[0., 1., 1., 1., 0.],        [1., 0., 0., 1., 0.],        [0., 1., 0., 1., 0.],        [0., 1., 1., 0., 1.]]) output6: tensor([[0.0000, 1.0905, 2.1070, 0.3826, 0.0000],        [0.3760, 0.0000, 0.0000, 0.8324, 0.0000],        [0.0000, 0.7380, 0.0000, 0.7132, 0.0000],        [0.0000, 1.2377, 0.3109, 0.0000, 0.4442]],       grad_fn=
       
        )
       
      
     
    
   

在代码中被注释的部分报错，结果说明，BECLoss的weight是可以对每个元素设置的。

BCEWithLogitsLoss

将sigmoid与BCELoss结合在一起，比两者单独使用更具有数值稳定性。其他一样

SmoothL1Loss

计算方式有差别外，其他与L1Loss没差别。

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