BCEWithLogitsLoss_nn.bcewithlogitsloss 权重

$$BCEWithLogitsLoss$$

一 Pytorch实现

torch.nn.BCEWithLogitsLoss(
weight: Optional[torch.Tensor] = None, 
size_average=None, 
reduce=None, 
reduction: str = 'mean', 
pos_weight: Optional[torch.Tensor] = None)
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target = torch.ones([10, 64], dtype=torch.float32)  # 64 classes, batch size = 10
output = torch.full([10, 64], 1.5)  # A prediction (logit)
pos_weight = torch.ones([64])  # All weights are equal to 1
criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
criterion(output, target)  # -log(sigmoid(1.5))
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import torch
from torch import nn
target =  torch.tensor([[1,2,3]],dtype=torch.float)
output =  torch.tensor([[1,2,30]],dtype=torch.float)
criterion = torch.nn.BCEWithLogitsLoss()
criterion(output, target)  # -log(sigmoid(1.5))
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