这篇教程pytorch交叉熵损失函数的weight参数的使用写得很实用，希望能帮到您。
首先必须将权重也转为Tensor的cuda格式； 然后将该class_weight作为交叉熵函数对应参数的输入值。 class_weight = torch.FloatTensor([0.13859937, 0.5821059, 0.63871904, 2.30220396, 7.1588294, 0]).cuda() 补充：关于pytorch的CrossEntropyLoss的weight参数 首先这个weight参数比想象中的要考虑的多你可以试试下面代码 import torchimport torch.nn as nninputs = torch.FloatTensor([0,1,0,0,0,1])outputs = torch.LongTensor([0,1])inputs = inputs.view((1,3,2))outputs = outputs.view((1,2))weight_CE = torch.FloatTensor([1,1,1])ce = nn.CrossEntropyLoss(ignore_index=255,weight=weight_CE)loss = ce(inputs,outputs)print(loss) 这里的手动计算是： loss1 = 0 + ln(e0 + e0 + e0) = 1.098 loss2 = 0 + ln(e1 + e0 + e1) = 1.86
求平均 = (loss1 *1 + loss2 *1)/ 2 = 1.4803 加权呢？import torchimport torch.nn as nninputs = torch.FloatTensor([0,1,0,0,0,1])outputs = torch.LongTensor([0,1])inputs = inputs.view((1,3,2))outputs = outputs.view((1,2))weight_CE = torch.FloatTensor([1,2,3])ce = nn.CrossEntropyLoss(ignore_index=255,weight=weight_CE)loss = ce(inputs,outputs)print(loss) 手算发现，并不是单纯的那权重相乘： loss1 = 0 + ln(e0 + e0 + e0) = 1.098 loss2 = 0 + ln(e1 + e0 + e1) = 1.86
求平均 = (loss1 * 1 + loss2 * 2)/ 2 = 2.4113 而是 loss1 = 0 + ln(e0 + e0 + e0) = 1.098 loss2 = 0 + ln(e1 + e0 + e1) = 1.86
求平均 = (loss1 *1 + loss2 *2) / 3 = 1.6075 发现了么，加权后，除以的是权重的和，不是数目的和。 我们再验证一遍：import torchimport torch.nn as nninputs = torch.FloatTensor([0,1,2,0,0,0,0,0,0,1,0,0.5])outputs = torch.LongTensor([0,1,2,2])inputs = inputs.view((1,3,4))outputs = outputs.view((1,4))weight_CE = torch.FloatTensor([1,2,3])ce = nn.CrossEntropyLoss(weight=weight_CE)# ce = nn.CrossEntropyLoss(ignore_index=255)loss = ce(inputs,outputs)print(loss) 手算： loss1 = 0 + ln(e0 + e0 + e0) = 1.098 loss2 = 0 + ln(e1 + e0 + e1) = 1.86 loss3 = 0 + ln(e2 + e0 + e0) = 2.2395 loss4 = -0.5 + ln(e0.5 + e0 + e0) = 0.7943
求平均 = (loss1 * 1 + loss2 * 2+loss3 * 3+loss4 * 3) / 9 = 1.5472 可能有人对loss的CE计算过程有疑问，我这里细致写写交叉熵的计算过程，就拿最后一个例子的loss4的计算说明 
以上为个人经验，希望能给大家一个参考，也希望大家多多支持51zixue.net。
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