/// <amd-module name="@tensorflow/tfjs-core/dist/ops/losses/softmax_cross_entropy" />
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import { Tensor } from '../../tensor';
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import { TensorLike } from '../../types';
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import { Reduction } from '../loss_ops_utils';
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/**
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* Computes the softmax cross entropy loss between two tensors.
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*
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* If labelSmoothing is nonzero, smooth the labels towards 1/2:
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*
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* newOnehotLabels = onehotLabels * (1 - labelSmoothing)
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* + labelSmoothing / numClasses
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*
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* @param onehotLabels One hot encoded labels
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* [batch_size, num_classes], same dimensions as 'predictions'.
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* @param logits The predicted outputs.
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* @param weights Tensor whose rank is either 0, or 1, and must be
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* broadcastable to `loss` of shape [batch_size]
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* @param labelSmoothing If greater than 0, then smooth the labels.
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* @param reduction Type of reduction to apply to loss. Should be of type
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* `Reduction`
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*
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* @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' }
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*/
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declare function softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(onehotLabels: T | TensorLike, logits: T | TensorLike, weights?: Tensor | TensorLike, labelSmoothing?: number, reduction?: Reduction): O;
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export declare const softmaxCrossEntropy: typeof softmaxCrossEntropy_;
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export {};
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