@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
61 lines • 8.9 kB
JavaScript
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import { convertToTensor } from '../../tensor_util_env';
import { assertShapesMatch } from '../../util';
import { abs } from '../abs';
import { add } from '../add';
import { Reduction } from '../loss_ops_utils';
import { minimum } from '../minimum';
import { mul } from '../mul';
import { op } from '../operation';
import { scalar } from '../scalar';
import { square } from '../square';
import { sub } from '../sub';
import { computeWeightedLoss } from './compute_weighted_loss';
/**
* Computes the Huber loss between two tensors.
*
* @param labels The ground truth output tensor, same dimensions as
* 'predictions'.
* @param predictions The predicted outputs.
* @param weights Tensor whose rank is either 0, or the same rank as
* `labels`, and must be broadcastable to `labels` (i.e., all dimensions
* must be either `1`, or the same as the corresponding `losses`
* dimension).
* @param delta Point where Huber loss changes from quadratic to linear.
* @param reduction Type of reduction to apply to loss. Should be of type
* `Reduction`.
*
* @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
*/
function huberLoss_(labels, predictions, weights, delta = 1.0, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
const $labels = convertToTensor(labels, 'labels', 'huberLoss');
const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');
let $weights = null;
if (weights != null) {
$weights = convertToTensor(weights, 'weights', 'huberLoss');
}
assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');
const deltaScalar = scalar(delta);
const error = abs(sub($predictions, $labels));
const quadratic = minimum(error, deltaScalar);
const linear = sub(error, quadratic);
const losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear));
return computeWeightedLoss(losses, $weights, reduction);
}
export const huberLoss = /* @__PURE__ */ op({ huberLoss_ });
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