Batched, Sequential, Multi-hot Vectors

[Caruso (π)]

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What kind of loss is better to use in multilabel classification?

Sure! Given your example target, you could use scatter to create the multi-hot target: labels = torch.tensor([1, 4, 1, 0, 5, 2]) labels = labels.unsqueeze(0) target = torch.zeros(labels.size(0), 15).scatter_(1, labels, 1.) print(target) > tensor([[1., 1., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]) nn.BCEWithLogitsLoss takes the raw logits of your model (without any non-linearity) and applies the sigmoid internally. If you would like to add the sigmoid activation to your model, you…