m0_51032217的博客

原本的bcelossbce_loss = nn.BCELoss(size_average=True)分别给目标0.8，背景0.2 的权重后def bce_loss_w(input,target): # bce_loss = nn.BCELoss(size_average=True) weight=torch.zeros_like(target) weight=torch.fill_(weight,0.3) weight[target>0]=0.7

pytorch的交叉熵损失函数是如何计算outputs和 labels之间的损失的？对于一个分类问题的CNN模型，最后一层的代码一般如下：nn.Linear(2048, num_classes)然后计算一次迭代损失的代码一般如下：loss_function = nn.CrossEntropyLoss()outputs = net(images.to(device))loss = loss_function(outputs, labels.to(device))1、假设条件：