@tensorflow/tfjs-core/src/ops/squared_difference.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2020 Google Inc. 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 {ENGINE, ForwardFunc} from '../engine';
import {SquaredDifference, SquaredDifferenceInputs} from '../kernel_names';
import {Tensor} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import {makeTypesMatch} from '../tensor_util';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';

import {assertAndGetBroadcastShape} from './broadcast_util';
import {op} from './operation';
import {scalar} from './tensor_ops';

/**
 * Returns (a - b) * (a - b) element-wise.
 * Supports broadcasting.
 *
 * We also expose `tf.squaredDifferenceStrict` which has the same signature as
 * this op and asserts that `a` and `b` are the same shape (does not
 * broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
 * ```
 *
 * ```js
 * // Broadcast squared difference  a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function squaredDifference_<T extends Tensor>(
    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
  let $a = convertToTensor(a, 'a', 'squaredDifference');
  let $b = convertToTensor(b, 'b', 'squaredDifference');
  [$a, $b] = makeTypesMatch($a, $b);

  assertAndGetBroadcastShape($a.shape, $b.shape);
  const der = (dy: Tensor, saved: Tensor[]) => {
    const [$a, $b] = saved;
    const two = scalar(2);
    const derA = () => dy.mul($a.sub($b).mul(two));
    const derB = () => dy.mul($b.sub($a).mul(two));
    return {a: derA, b: derB};
  };
  const forward: ForwardFunc<Tensor> = (backend, save) => {
    const res = backend.squaredDifference($a, $b);
    save([$a, $b]);
    return res;
  };

  const inputs: SquaredDifferenceInputs = {a: $a, b: $b};
  const attrs = {};

  const inputsToSave = [$a, $b];
  const outputToSave: boolean[] = [];
  return ENGINE.runKernelFunc(
             forward, inputs as unknown as NamedTensorMap, der,
             SquaredDifference, attrs, inputsToSave, outputToSave) as T;
}

export const squaredDifference = op({squaredDifference_});