gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @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 { Reduction } from '../loss_ops_utils';
import { op } from '../operation';
import { squaredDifference } from '../squared_difference';
import { computeWeightedLoss } from './compute_weighted_loss';
/**
 * Computes the mean squared error 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 reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */
function meanSquaredError_(labels, predictions, weights, reduction = Reduction.SUM_BY_NONZERO_WEIGHTS) {
    const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');
    const $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');
    let $weights = null;
    if (weights != null) {
        $weights = convertToTensor(weights, 'weights', 'meanSquaredError');
    }
    assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');
    const losses = squaredDifference($labels, $predictions);
    return computeWeightedLoss(losses, $weights, reduction);
}
export const meanSquaredError = /* @__PURE__ */ op({ meanSquaredError_ });
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