24.11.2019 · The loss you're looking at is designed for situations where each example can belong to multiple classes (say a person can be classified as both female and old). I think it's this "multi" that confuses you - it stands for the multiple possible classifications per example , not just multiple potential labels in the whole "universe".
class torch.nn.MultiLabelSoftMarginLoss(weight=None, size_average=None, reduce=None, reduction='mean') Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x x and target y y of size …
Abstract Multilabel classification (ML) aims to assign a set of labels to an instance. This generalization of multiclass classification yields to the ...
MultiLabelMarginLoss class torch.nn.MultiLabelMarginLoss(size_average=None, reduce=None, reduction='mean') [source] Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x x (a 2D mini-batch Tensor ) and output y y (which is a 2D Tensor of target class indices).
This paper presents a new criterion, PRO LOSS, concerning the prediction on all labels as well as the rankings of only relevant labels, and proposes ProSVM ...
MultiLabelSoftMarginLoss class torch.nn.MultiLabelSoftMarginLoss(weight=None, size_average=None, reduce=None, reduction='mean') [source] Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input x x and target y y of size (N, C) (N,C) . For each sample in the minibatch:
It corresponds to Kendall's τ, which measures the correlation between two rankings. Margin The margin loss returns the number of positions between the worst.
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15.03.2018 · MultiLabelSoftMargin’s fomula is also same wi… I think there is no difference between BCEWithLogitsLoss and MultiLabelSoftMarginLoss. BCEWithLogitsLoss = One Sigmoid Layer + BCELoss (solved numerically unstable problem) MultiLabelSoftMargin’s fomula is also same with BCEWithLogitsLoss.
MultiLabelSoftMarginLoss (weight=None, size_average=None, reduce=None, ... Creates a criterion that optimizes a multi-label one-versus-all loss based on ...