BCEWithLogitsLoss¶ class torch.nn. BCEWithLogitsLoss (weight = None, size_average = None, reduce = None, reduction = 'mean', pos_weight = None) [source] ¶. This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, we take …
BCEWithLogitsLoss() loss = 0 for bi in range(logits.size(0)): for i in range(logits.size(1)): if i < length[bi]: loss += bce_criterion(logits[bi][i], ...
This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable than using a plain Sigmoid followed by a BCELoss ...
02.01.2019 · As you described the only difference is the included sigmoid activation in nn.BCEWithLogitsLoss. It’s comparable to nn.CrossEntropyLoss and nn.NLLLoss.While the former uses a nn.LogSoftmax activation function internally, you would have to add it in the latter criterion.
20.08.2019 · When using CUDA or BCELossWithLogits (), the loss always stays close to 0.6202. The decrease in mean_sigmoid_loss is directly dependent on the total size of the tensor--not just the size of the x-dimension or just the y-dimension. For some reason mean_sigmoid_loss is exactly 0.5 when the tensor has 32*1024 elements.
Indeed, in standard neural networks using a softmax layer and the cross-entropy loss, the computation needed for finding the logits of the classes (the pre- ...
Our solution is that BCELoss clamps its log function outputs to be greater than or equal to -100. This way, we can always have a finite loss value and a linear backward method. weight ( Tensor, optional) – a manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size nbatch.