@tensorflow/tfjs-core/src/ops/losses/log_loss.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @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 {Tensor} from '../../tensor';
import {convertToTensor} from '../../tensor_util_env';
import {TensorLike} from '../../types';
import {assertShapesMatch} from '../../util';
import {add} from '../add';
import {log} from '../log';
import {Reduction} from '../loss_ops_utils';
import {mul} from '../mul';
import {neg} from '../neg';
import {op} from '../operation';
import {scalar} from '../scalar';
import {sub} from '../sub';

import {computeWeightedLoss} from './compute_weighted_loss';

/**
 * Computes the log 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 epsilon A small increment to avoid taking log of zero
 * @param reduction Type of reduction to apply to loss. Should be of type
 *    `Reduction`
 *
 * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'}
 */
function logLoss_<T extends Tensor, O extends Tensor>(
    labels: T|TensorLike, predictions: T|TensorLike,
    weights?: Tensor|TensorLike, epsilon = 1e-7,
    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {
  const $labels = convertToTensor(labels, 'labels', 'logLoss');
  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');
  let $weights: Tensor = null;
  if (weights != null) {
    $weights = convertToTensor(weights, 'weights', 'logLoss');
  }
  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');

  const one = scalar(1);
  const epsilonScalar = scalar(epsilon);

  const l1 = neg(mul($labels, log(add($predictions, epsilonScalar))));
  const l2 =
      mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar)));
  const losses = sub(l1, l2);
  return computeWeightedLoss(losses, $weights, reduction);
}
export const logLoss = op({logLoss_});