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Super Kai (Kazuya Ito)
Super Kai (Kazuya Ito)

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BCELoss in PyTorch

#python #pytorch #bceloss #lossfunction

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*Memos:

BCELoss() can get the 0D or more D tensor of the zero or more values(float) computed by BCE Loss from the 0D or more D tensor of zero or more elements as shown below:

*Memos:

  • The 1st argument for initialization is weight(Optional-Default:None-Type:tensor of int, float or bool): *Memos:
    • If it's not given, it's 1.
    • It must be the 0D or more D tensor of zero or more elements.
  • There is reduction argument for initialization(Optional-Default:'mean'-Type:str). *'none', 'mean' or 'sum' can be selected.
  • There are size_average and reduce argument for initialization but they are deprecated.
  • The 1st argument is input(Required-Type:tensor of float). *It must be 0<=x<=1.
  • The 2nd argument is target(Required-Type:tensor of float). *It must be 0<=y<=1.
  • input and target must be the same size otherwise there is error.
  • The empty 1D or more D input and target tensor with reduction='mean' return nan.
  • The empty 1D or more D input and target tensor with reduction='sum' return 0.. Image description 
import torch from torch import nn tensor1 = torch.tensor([0.4, 0.8, 0.6, 0.3, 0.0, 0.5]) tensor2 = torch.tensor([0.2, 0.9, 0.4, 0.1, 0.8, 0.5]) # -w*(y*log(x)+(1-y)*log(1-x))  # -1*(0.2*log(0.4)+(1-0.2)*log(1-0.4))  # ↓↓↓↓↓↓  # 0.5919 + 0.3618 + 0.7541 + 0.4414 + 80.0 + 0.6931 = 82.8423  # 82.8423 / 6 = 13.8071 bceloss = nn.BCELoss() bceloss(input=tensor1, target=tensor2) # tensor(7.2500)  bceloss # BCELoss()  print(bceloss.weight) # None  bceloss.reduction # 'mean'  bceloss = nn.BCELoss(weight=None, reduction='mean') bceloss(input=tensor1, target=tensor2) # tensor(13.8071)  bceloss = nn.BCELoss(reduction='sum') bceloss(input=tensor1, target=tensor2) # tensor(82.8423)  bceloss = nn.BCELoss(reduction='none') bceloss(input=tensor1, target=tensor2) # tensor([0.5919, 0.3618, 0.7541, 0.4414, 80.0000, 0.6931])  bceloss = nn.BCELoss(weight=torch.tensor([0., 1., 2., 3., 4., 5.])) bceloss(input=tensor1, target=tensor2) # tensor(54.4433)  bceloss = nn.BCELoss(weight=torch.tensor([0.])) bceloss(input=tensor1, target=tensor2) # tensor(0.)  bceloss = nn.BCELoss(weight=torch.tensor([0, 1, 2, 3, 4, 5])) bceloss(input=tensor1, target=tensor2) # tensor(54.4433)  bceloss = nn.BCELoss(weight=torch.tensor([0])) bceloss(input=tensor1, target=tensor2) # tensor(0.)  bceloss = nn.BCELoss( weight=torch.tensor([True, False, True, False, True, False]) ) bceloss(input=tensor1, target=tensor2) # tensor(13.5577)  bceloss = nn.BCELoss(weight=torch.tensor([False])) bceloss(input=tensor1, target=tensor2) # tensor(0.)  tensor1 = torch.tensor([[0.4, 0.8, 0.6], [0.3, 0.0, 0.5]]) tensor2 = torch.tensor([[0.2, 0.9, 0.4], [0.1, 0.8, 0.5]]) bceloss = nn.BCELoss() bceloss(input=tensor1, target=tensor2) # tensor(13.8071)  tensor1 = torch.tensor([[[0.4], [0.8], [0.6]], [[0.3], [0.0], [0.5]]]) tensor2 = torch.tensor([[[0.2], [0.9], [0.4]], [[0.1], [0.8], [0.5]]]) bceloss = nn.BCELoss() bceloss(input=tensor1, target=tensor2) # tensor(13.8071)  tensor1 = torch.tensor([]) tensor2 = torch.tensor([]) bceloss = nn.BCELoss(reduction='mean') bceloss(input=tensor1, target=tensor2) # tensor(nan)  bceloss = nn.BCELoss(reduction='sum') bceloss(input=tensor1, target=tensor2) # tensor(0.)
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