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在Pytorch中，BCELoss和BCEWithLogitsLoss是一组常用的二元交叉熵损失函数，常用于二分类问题，其区别在于前者的输入为已进行
sigmoid处理过的值，而后者为sigmoid函数 1 1 + exp ( − x ) \frac{1}{1+\exp(-x)} 1+exp(−x)1
中的 x x x。
下面为一个简单的示例：
import torch import torch.nn as nn predicts = torch.tensor([[0.4,0.7,1.2,0.3],
[1.1,0.6,0.9,1.6]]) labels = torch.tensor([[1,0,1,0],[0,1,1,0]],
dtype=torch.float) # 通过BCELoss计算sigmoid处理后的值 criterion1 = nn.BCELoss() loss1=
criterion1(torch.sigmoid(predicts), labels) # 通过BCEWithLogitsLoss直接计算输入值
criterion2 = nn.BCEWithLogitsLoss() loss2 = criterion2(predicts, labels) #
会发现loss1=loss2
BCELoss和BCEWithLogitsLoss还提供了两个重要参数：
* weight：可用于控制各样本的权重，常用作对对齐后的数据进行mask操作（设为0）
* reduction
：控制损失输出模式。设为"sum"表示对样本进行求损失和；设为"mean"表示对样本进行求损失的平均值；而设为"none"表示对样本逐个求损失，输出与输入的shape一样。
此外BCEWithLogitsLoss还提供了参数pos_weight用于设置损失的class权重，用于缓解样本的不均衡问题。
