在CrossEntropyLoss中,weight参数起到了一种权重调节的作用,可以用于平衡不同类别的训练样本。本文将逐步解释CrossEntropyLoss及其weight参数的原理和作用。 首先,我们需要了解CrossEntropyLoss的基本原理。CrossEntropyLoss是一种用于解决分类问题的损失函数,它的计算方式基于信息论中的交叉熵概念。交叉熵用于度量两个概率...
本文通过将CrossEntropyLoss拆解为LogSoftmax和NLLLoss两步,对交叉熵损失内部计算做了深度的解析,以更清晰地理解交叉熵损失函数。需要指出的是,本文所介绍的内容,只是对于CrossEntropyLoss的target为类索引的情况,CrossEntropyLoss的target还可以是每个类别的概率(Probabilities for each class),这种情况有所不同。
^pytorchhttps://discuss.pytorch.org/t/what-is-the-weight-values-mean-in-torch-nn-crossentropylo...
使用PaddleNLP进行NER训练,设置的损失函数为:paddle.nn.loss.CrossEntropyLoss(weight=weight) 其中的weight为:weight = paddle.to_tensor( np.array(['0.000451536', '0.000451536', '0.000645355', '0.000645355', '0.000336313', '0.000336313', '0.003716059', '0.003716059', '0.003580706', '0.003580706', '0.0...
criterion=nn.CrossEntropyLoss() # train model on task A model=Model(28*28,100,10).to(device) optimizer=optim.Adam(model.parameters(),lr) for_inrange(EPOCHS): forinput,targetintqdm(train_loader): output=model(input.to(device)) loss=criterion(output,target.to(device)) ...
l2=loss2(predict,lable)loss=binary_cross_entropyloss(predict,lable,weight=weight2)print(l2,loss)...
criterion = nn.CrossEntropyLoss() # train model on task A model = Model(28 * 28, 100, 10).to(device)optimizer= optim.Adam(model.parameters(), lr) for _ in range(EPOCHS): for input, target in tqdm(train_loader): output = model(input.to(device)) ...
ce = nn.CrossEntropyLoss(ignore_index=255,weight=weight_CE)loss = ce(inputs,outputs)print(loss)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 = ...
# ce = nn.CrossEntropyLoss(ignore_index=255) loss = ce(inputs,outputs) print(loss) 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 ...
criterion=nn.CrossEntropyLoss() # train model on task A model=Model(28*28, 100, 10).to(device) optimizer=optim.Adam(model.parameters(), lr) for_inrange(EPOCHS): forinput, targetintqdm(train_loader): output=model(input.to(device)) ...