Target: If containing class indices, shape ( ) () ( ), ( N ) (N) ( N ) or ( N, d 1, d 2. Rethinking the Inception Architecture for Computer Vision. The targetsīecome a mixture of the original ground truth and a uniform distribution as described in Of smoothing when computing the loss, where 0.0 means no smoothing. Label_smoothing ( float, optional) – A float in. The meantime, specifying either of those two args will override Note: size_averageĪnd reduce are in the process of being deprecated, and in 'none': no reduction willīe applied, 'mean': the weighted mean of the output is taken, Reduction ( str, optional) – Specifies the reduction to apply to the output: When reduce is False, returns a loss perīatch element instead and ignores size_average. Losses are averaged or summed over observations for each minibatch depending Reduce ( bool, optional) – Deprecated (see reduction). Ignore_index is only applicable when the target contains class indices.
True, the loss is averaged over non-ignored targets. Ignore_index ( int, optional) – Specifies a target value that is ignoredĪnd does not contribute to the input gradient. Is set to False, the losses are instead summed for each minibatch. Some losses, there are multiple elements per sample. The losses are averaged over each loss element in the batch. Size_average ( bool, optional) – Deprecated (see reduction).
#PYTORCH CROSS ENTROPY LOSS MANUAL#
Weight ( Tensor, optional) – a manual rescaling weight given to each class. Consider providing target asĬlass probabilities only when a single class label per minibatch item is too restrictive. Indices, as this allows for optimized computation. The performance of this criterion is generally better when target contains class Set 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 [ 0, C ) [0, C) [ 0, C ) where C C C is the number of classes if The last being 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 . ( m i n i b a t c h, C ) (minibatch, C) ( miniba t c h, C ) or ( m i n i b a t c h, C, d 1, d 2. Input has to be a Tensor of size ( C ) (C) ( C ) for unbatched input, The input is expected to contain the unnormalized logits for each class (which do not need 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 logits CrossEntropyLoss ( weight = None, size_average = None, ignore_index = - 100, reduce = None, reduction = 'mean', label_smoothing = 0.0 ) ¶ Extending torch.func with autograd.FunctionĬrossEntropyLoss ¶ class torch.nn.
CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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