text: /**
   * @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 = op({ huberLoss_ });
  //# sourceMappingURL=huber_loss.js.map