4/7/2024 0 Comments Cross-entropySet to 'none') loss for this case can be described as: ![]() May not necessarily be in the class range). Ignore_index is specified, this loss also accepts this class index (this index The target that this criterion expects should contain either:Ĭlass indices in the range where C C C is the number of classes if The latter is useful for higher dimension inputs, suchĪs computing cross entropy loss per-pixel for 2D images. , d K ) with K ≥ 1 K \geq 1 K ≥ 1 for the , d_K) ( miniba t c h, C, d 1 , d 2 . Input has to be a Tensor of size either ( m i n i b a t c h, C ) (minibatch, C) ( miniba t c h, C ) or The input is expected to contain raw, unnormalized scores for each class. This is particularly useful when you have an unbalanced training set. If provided, the optional argument weight should be a 1D Tensor It is useful when training a classification problem with C classes. ![]() This criterion computes the cross entropy loss between input and target. CrossEntropyLoss ( weight = None, size_average = None, ignore_index = - 100, reduce = None, reduction = 'mean', label_smoothing = 0.0 ) ¶ PyTorch Governance | Persons of InterestĬrossEntropyLoss ¶ class torch.nn.CPU threading and TorchScript inference.
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