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。
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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
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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) |
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tensor(1.4803) |
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？
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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) |
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tensor(1.6075) |
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：
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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) |
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tensor(1.5472) |
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服务器之家。
原文链接：https://niecongchong.blog.csdn.net/article/details/86594621
