/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import {ENGINE} from '../engine';
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import {Tensor} from '../tensor';
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import {NamedTensorMap} from '../tensor_types';
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import {makeTypesMatch} from '../tensor_util';
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import {convertToTensor} from '../tensor_util_env';
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import {TensorLike} from '../types';
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import * as util from '../util';
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import * as broadcast_util from './broadcast_util';
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import {where} from './logical_ops';
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import {op} from './operation';
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import {scalar, zerosLike} from './tensor_ops';
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import {neg} from './unary_ops';
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/**
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* Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
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*
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* We also expose `tf.addStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 2, 3, 4]);
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* const b = tf.tensor1d([10, 20, 30, 40]);
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*
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* a.add(b).print(); // or tf.add(a, b)
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* ```
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*
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* ```js
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* // Broadcast add a with b.
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* const a = tf.scalar(5);
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* const b = tf.tensor1d([10, 20, 30, 40]);
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*
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* a.add(b).print(); // or tf.add(a, b)
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* ```
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* @param a The first `tf.Tensor` to add.
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* @param b The second `tf.Tensor` to add. Must have the same type as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
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let $a = convertToTensor(a, 'a', 'add');
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let $b = convertToTensor(b, 'b', 'add');
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[$a, $b] = makeTypesMatch($a, $b);
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const outShape =
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broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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const der = (dy: Tensor) => {
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const derA = () => {
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let res = dy;
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const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.reshape($a.shape);
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};
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const derB = () => {
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let res = dy;
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const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.reshape($b.shape);
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};
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return {a: derA, b: derB};
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};
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return ENGINE.runKernelFunc(
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backend => backend.add($a, $b), {a: $a, b: $b}, der, 'Add') as T;
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}
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/**
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* Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
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*
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* ```js
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* const a = tf.tensor1d([1, 2]);
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* const b = tf.tensor1d([3, 4]);
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* const c = tf.tensor1d([5, 6]);
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*
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* tf.addN([a, b, c]).print();
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* ```
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* @param tensors A list of tensors with the same shape and dtype.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {
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util.assert(
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Array.isArray(tensors),
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() => 'The argument passed to tf.addN() must be a list of tensors');
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util.assert(
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tensors.length >= 1,
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() => `Must pass at least one tensor to tf.addN(), but got ` +
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`${tensors.length}`);
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const $tensors =
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tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));
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const firstTensor = $tensors[0];
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$tensors.forEach(t => {
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if (t.dtype !== firstTensor.dtype) {
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throw new Error(
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'All tensors passed to tf.addN() must have the same dtype');
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}
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});
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$tensors.forEach(t => {
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if (!util.arraysEqual(t.shape, firstTensor.shape)) {
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throw new Error(
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'All tensors passed to tf.addN() must have the same shape');
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}
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});
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const der = (dy: T) => {
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const ders: {[key: string]: () => Tensor} = {};
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$tensors.forEach((t, i) => {
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ders[i] = () => dy.clone();
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});
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return ders;
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};
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const inputs: NamedTensorMap = $tensors as {} as NamedTensorMap;
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return ENGINE.runKernelFunc(
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backend => backend.addN($tensors), inputs, der, 'AddN');
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}
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/**
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* Adds two `tf.Tensor`s element-wise, A + B.
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*
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* Inputs must be the same shape. For broadcasting support, use add() instead.
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*
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* @param a The first Tensor to add element-wise.
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* @param b The second Tensor to add element-wise.
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*/
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function addStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
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const $a = convertToTensor(a, 'a', 'addStrict');
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const $b = convertToTensor(b, 'b', 'addStrict');
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util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');
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return $a.add($b);
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}
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/**
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* Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
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*
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* We also expose `tf.subStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([10, 20, 30, 40]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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*
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* a.sub(b).print(); // or tf.sub(a, b)
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* ```
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*
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* ```js
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* // Broadcast subtract a with b.
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* const a = tf.tensor1d([10, 20, 30, 40]);
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* const b = tf.scalar(5);
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*
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* a.sub(b).print(); // or tf.sub(a, b)
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* ```
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* @param a The first `tf.Tensor` to subtract from.
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* @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
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let $a = convertToTensor(a, 'a', 'sub');
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let $b = convertToTensor(b, 'b', 'sub');
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[$a, $b] = makeTypesMatch($a, $b);
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const outShape =
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broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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const der = (dy: Tensor) => {
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const derA = () => {
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let res = dy;
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const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.reshape($a.shape);
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};
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const derB = () => {
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let res = dy;
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const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.neg().reshape($b.shape);
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};
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return {a: derA, b: derB};
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};
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return ENGINE.runKernelFunc(
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backend => backend.subtract($a, $b), {a: $a, b: $b}, der, 'Sub') as
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T;
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}
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/**
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* Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must
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* be the same shape.
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*
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* For broadcasting support, use `tf.sub` instead.
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*
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* @param a The first Tensor to subtract element-wise.
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* @param b The second Tensor to subtract element-wise.
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*/
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function subStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
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const $a = convertToTensor(a, 'a', 'subStrict');
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const $b = convertToTensor(b, 'b', 'subStrict');
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util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');
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return $a.sub($b);
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}
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/**
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* Computes the power of one `tf.Tensor` to another. Supports broadcasting.
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*
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* Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
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* corresponding elements in x and y. The result's dtype will be the upcasted
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* type of the `base` and `exp` dtypes.
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*
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* ```js
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* const a = tf.tensor([[2, 3], [4, 5]])
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* const b = tf.tensor([[1, 2], [3, 0]]).toInt();
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*
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* a.pow(b).print(); // or tf.pow(a, b)
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* ```
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*
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* ```js
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* const a = tf.tensor([[1, 2], [3, 4]])
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* const b = tf.tensor(2).toInt();
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*
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* a.pow(b).print(); // or tf.pow(a, b)
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* ```
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* We also expose `powStrict` which has the same signature as this op and
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* asserts that `base` and `exp` are the same shape (does not broadcast).
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*
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* @param base The base `tf.Tensor` to pow element-wise.
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* @param exp The exponent `tf.Tensor` to pow element-wise.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function pow_<T extends Tensor>(
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base: Tensor|TensorLike, exp: Tensor|TensorLike): T {
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let $base = convertToTensor(base, 'base', 'pow');
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let $exp = convertToTensor(exp, 'exp', 'pow');
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[$base, $exp] = makeTypesMatch($base, $exp);
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const outShape =
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broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);
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const grad = (dy: Tensor, saved: Tensor[]) => {
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const [$base, $exp, y] = saved;
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const derBase = () => {
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const expFloat = $exp.toFloat();
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let res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));
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const reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.reshape($base.shape) as T;
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};
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const derExp = () => {
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const condition = $base.greater(0);
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const logBase = $base.log().where(condition, zerosLike($base));
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let res = dy.mul(y.mul(logBase));
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const reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes);
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}
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return res.reshape($exp.shape);
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};
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return {a: derBase, b: derExp};
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};
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const attrs = {};
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const inputsToSave = [$base, $exp];
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const outputsToSave = [true];
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return ENGINE.runKernelFunc((backend, save) => {
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const y = backend.pow($base, $exp);
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save([$base, $exp, y]);
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return y;
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}, {a: $base, b: $exp}, grad, 'Pow', attrs, inputsToSave, outputsToSave) as T;
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}
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/**
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* Computes the power of one `tf.Tensor` to another. Inputs must
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* be the same shape.
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*
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* For broadcasting support, use `tf.pow` instead.
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*
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* @param base The base tensor to pow element-wise.
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* @param exp The exponent tensor to pow element-wise.
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*/
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function powStrict_<T extends Tensor>(base: T, exp: Tensor): T {
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util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');
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return base.pow(exp);
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}
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/**
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* Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
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*
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* We also expose `tf.mulStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 2, 3, 4]);
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* const b = tf.tensor1d([2, 3, 4, 5]);
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*
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* a.mul(b).print(); // or tf.mul(a, b)
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* ```
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*
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* ```js
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* // Broadcast mul a with b.
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* const a = tf.tensor1d([1, 2, 3, 4]);
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* const b = tf.scalar(5);
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*
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* a.mul(b).print(); // or tf.mul(a, b)
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* ```
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* @param a The first tensor to multiply.
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* @param b The second tensor to multiply. Must have the same dtype as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
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let $a = convertToTensor(a, 'a', 'mul');
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let $b = convertToTensor(b, 'b', 'mul');
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[$a, $b] = makeTypesMatch($a, $b);
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const outShape =
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broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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const der = (dy: Tensor, saved: Tensor[]) => {
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const [$a, $b] = saved;
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const derA = () => {
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const res = dy.mul($b.toFloat());
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const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
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if (reduceAxes.length > 0) {
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return res.sum(reduceAxes).reshape($a.shape);
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}
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return res;
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};
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const derB = () => {
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const res = dy.mul($a.toFloat());
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const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
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if (reduceAxes.length > 0) {
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return res.sum(reduceAxes).reshape($b.shape);
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}
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return res;
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};
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return {a: derA, b: derB};
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};
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return ENGINE.runKernelFunc((backend, save) => {
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const res = backend.multiply($a, $b);
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save([$a, $b]);
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return res;
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}, {a: $a, b: $b}, der, 'Mul') as T;
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}
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/**
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* Multiplies two `tf.Tensor`s element-wise, A * B.
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*
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* Inputs must be the same shape. For broadcasting support, use `tf.mul`.
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*
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* @param a The first tensor to multiply.
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* @param b The first tensor to multiply. Must have the same
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* dtype as `a`.
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*/
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function mulStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
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const $a = convertToTensor(a, 'a', 'mul');
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const $b = convertToTensor(b, 'b', 'mul');
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util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');
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return $a.mul($b);
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}
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
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*
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* We also expose `tf.divStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 9, 16]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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*
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* a.div(b).print(); // or tf.div(a, b)
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* ```
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*
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* ```js
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* // Broadcast div a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(2);
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*
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* a.div(b).print(); // or tf.div(a, b)
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* ```
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*
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* @param a The first tensor as the numerator.
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* @param b The second tensor as the denominator. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
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let $a = convertToTensor(a, 'a', 'div');
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let $b = convertToTensor(b, 'b', 'div');
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[$a, $b] = makeTypesMatch($a, $b);
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if ($a.dtype === 'int32' && $b.dtype === 'int32') {
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return floorDiv($a, $b);
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}
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const outShape =
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broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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const der = (dy: Tensor, saved: Tensor[]) => {
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const [$a, $b] = saved;
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const derA = () => {
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const res = dy.div($b.toFloat());
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const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
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if (reduceAxes.length > 0) {
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return res.sum(reduceAxes).reshape($a.shape);
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}
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return res;
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};
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const derB = () => {
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let res = dy.mul($a.toFloat());
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const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
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if (reduceAxes.length > 0) {
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res = res.sum(reduceAxes).reshape($b.shape);
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}
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const tmp = $b.square();
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return res.div(tmp.toFloat()).neg();
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};
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return {a: derA, b: derB};
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};
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return ENGINE.runKernelFunc((backend, save) => {
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const res = backend.realDivide($a, $b);
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save([$a, $b]);
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return res;
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}, {a: $a, b: $b}, der, 'Div') as T;
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}
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
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* if denominator is 0.
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*
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* We also expose `tf.divStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 9, 16]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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* const c = tf.tensor1d([0, 0, 0, 0]);
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*
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* a.divNoNan(b).print(); // or tf.divNoNan(a, b)
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* a.divNoNan(c).print(); // or tf.divNoNan(a, c)
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* ```
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*
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* ```js
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* // Broadcast div a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(2);
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* const c = tf.scalar(0);
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*
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* a.divNoNan(b).print(); // or tf.divNoNan(a, b)
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* a.divNoNan(c).print(); // or tf.divNoNan(a, c)
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* ```
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*
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* @param a The first tensor as the numerator.
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* @param b The second tensor as the denominator. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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function divNoNan_<T extends Tensor>(
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a: Tensor|TensorLike, b: Tensor|TensorLike): T {
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let $a = convertToTensor(a, 'a', 'div');
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let $b = convertToTensor(b, 'b', 'div');
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[$a, $b] = makeTypesMatch($a, $b);
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const divResult = div($a, $b);
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const zeros = zerosLike(divResult);
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const bEqualsZero = $b.equal(zeros);
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return where(bEqualsZero, zeros, divResult) as T;
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}
|
|
/**
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* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
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* The result is rounded with floor function.
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*
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 9, 16]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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*
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* a.floorDiv(b).print(); // or tf.div(a, b)
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* ```
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*
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* ```js
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* // Broadcast div a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(2);
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*
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* a.floorDiv(b).print(); // or tf.floorDiv(a, b)
|
* ```
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*
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* @param a The first tensor as the numerator.
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* @param b The second tensor as the denominator. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
|
function floorDiv_<T extends Tensor>(
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a: Tensor|TensorLike, b: Tensor|TensorLike): T {
|
let $a = convertToTensor(a, 'a', 'floorDiv');
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let $b = convertToTensor(b, 'b', 'floorDiv');
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[$a, $b] = makeTypesMatch($a, $b);
|
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const outShape =
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broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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const der = (dy: Tensor, saved: Tensor[]) => {
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const [$a, $b] = saved;
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const derA = () => {
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const res = dy.div($b.toFloat());
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const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
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if (reduceAxes.length > 0) {
|
return res.sum(reduceAxes).reshape($a.shape);
|
}
|
return res;
|
};
|
const derB = () => {
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let res = dy.mul($a.toFloat());
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const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
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if (reduceAxes.length > 0) {
|
res = res.sum(reduceAxes).reshape($b.shape);
|
}
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const tmp = $b.square();
|
return res.div(tmp.toFloat()).neg();
|
};
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return {a: derA, b: derB};
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};
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return ENGINE.runKernelFunc((backend, save) => {
|
const res = backend.floorDiv($a, $b);
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save([$a, $b]);
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return res;
|
}, {a: $a, b: $b}, der, 'FloorDiv') as T;
|
}
|
|
/**
|
* Divides two `tf.Tensor`s element-wise, A / B. Inputs must
|
* be the same shape.
|
*
|
* @param a The first tensor as the numerator for element-wise division.
|
* @param b The second tensor as the denominator for element-wise division.
|
*/
|
function divStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
|
const $a = convertToTensor(a, 'a', 'div');
|
const $b = convertToTensor(b, 'b', 'div');
|
util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');
|
return $a.div($b);
|
}
|
|
/**
|
* Returns the mod of a and b element-wise.
|
* `floor(x / y) * y + mod(x, y) = x`
|
* Supports broadcasting.
|
*
|
* We also expose `tf.modStrict` 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.mod(b).print(); // or tf.mod(a, b)
|
* ```
|
*
|
* ```js
|
* // Broadcast a mod b.
|
* const a = tf.tensor1d([2, 4, 6, 8]);
|
* const b = tf.scalar(5);
|
*
|
* a.mod(b).print(); // or tf.mod(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 mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {
|
let $a = convertToTensor(a, 'a', 'mod');
|
let $b = convertToTensor(b, 'b', 'mod');
|
[$a, $b] = makeTypesMatch($a, $b);
|
|
const outShape =
|
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
const der = (dy: Tensor, saved: Tensor[]) => {
|
const [$a, $b] = saved;
|
const derA = () => {
|
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
|
if (reduceAxes.length > 0) {
|
return dy.sum(reduceAxes).reshape($a.shape);
|
}
|
return dy;
|
};
|
const derB = () => {
|
const res = dy.mul($a.div($b).floor().neg());
|
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
|
if (reduceAxes.length > 0) {
|
return res.sum(reduceAxes).reshape($b.shape);
|
}
|
return res;
|
};
|
return {$a: derA, $b: derB};
|
};
|
return ENGINE.runKernelFunc((backend, save) => {
|
const res = backend.mod($a, $b);
|
save([$a, $b]);
|
return res;
|
}, {$a, $b}, der) as T;
|
}
|
|
/**
|
* Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must
|
* be the same shape. For broadcasting support, use mod().
|
*
|
* @param a The first tensor.
|
* @param b The second tensor. Must have the same dtype as `a`.
|
*/
|
function modStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
|
const $a = convertToTensor(a, 'a', 'modStrict');
|
const $b = convertToTensor(b, 'b', 'modStrict');
|
util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');
|
return $a.mod($b);
|
}
|
|
/**
|
* Returns the min of a and b (`a < b ? a : b`) element-wise.
|
* Supports broadcasting.
|
*
|
* We also expose `minimumStrict` 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.minimum(b).print(); // or tf.minimum(a, b)
|
* ```
|
*
|
* ```js
|
* // Broadcast minimum a with b.
|
* const a = tf.tensor1d([2, 4, 6, 8]);
|
* const b = tf.scalar(5);
|
*
|
* a.minimum(b).print(); // or tf.minimum(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 minimum_<T extends Tensor>(
|
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
|
let $a = convertToTensor(a, 'a', 'minimum');
|
let $b = convertToTensor(b, 'b', 'minimum');
|
[$a, $b] = makeTypesMatch($a, $b);
|
|
if ($a.dtype === 'bool') {
|
$a = $a.toInt();
|
$b = $b.toInt();
|
}
|
|
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
const der = (dy: Tensor, saved: Tensor[]) => {
|
const [$a, $b] = saved;
|
const derA = () => dy.mul($a.lessEqual($b).toFloat());
|
const derB = () => dy.mul($a.greater($b).toFloat());
|
return {a: derA, b: derB};
|
};
|
return ENGINE.runKernelFunc((backend, save) => {
|
const res = backend.minimum($a, $b);
|
save([$a, $b]);
|
return res;
|
}, {a: $a, b: $b}, der, 'Minimum') as T;
|
}
|
|
/**
|
* Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must
|
* be the same shape. For broadcasting support, use minimum().
|
*
|
* @param a The first tensor.
|
* @param b The second tensor. Must have the same dtype as `a`.
|
*/
|
function minimumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
|
const $a = convertToTensor(a, 'a', 'minimumStrict');
|
const $b = convertToTensor(b, 'b', 'minimumStrict');
|
util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');
|
return $a.minimum($b);
|
}
|
|
/**
|
* Returns the max of a and b (`a > b ? a : b`) element-wise.
|
* Supports broadcasting.
|
*
|
* We also expose `tf.maximumStrict` 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.maximum(b).print(); // or tf.maximum(a, b)
|
* ```
|
*
|
* ```js
|
* // Broadcast maximum a with b.
|
* const a = tf.tensor1d([2, 4, 6, 8]);
|
* const b = tf.scalar(5);
|
*
|
* a.maximum(b).print(); // or tf.maximum(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 maximum_<T extends Tensor>(
|
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
|
let $a = convertToTensor(a, 'a', 'maximum');
|
let $b = convertToTensor(b, 'b', 'maximum');
|
[$a, $b] = makeTypesMatch($a, $b);
|
|
if ($a.dtype === 'bool') {
|
$a = $a.toInt();
|
$b = $b.toInt();
|
}
|
|
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
const der = (dy: Tensor, saved: Tensor[]) => {
|
const [$a, $b] = saved;
|
const derA = () => dy.mul($a.greaterEqual($b).toFloat());
|
const derB = () => dy.mul($a.less($b).toFloat());
|
return {a: derA, b: derB};
|
};
|
return ENGINE.runKernelFunc((backend, save) => {
|
const res = backend.maximum($a, $b);
|
save([$a, $b]);
|
return res;
|
}, {a: $a, b: $b}, der, 'Maximum') as T;
|
}
|
|
/**
|
* Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must
|
* be the same shape. For broadcasting support, use maximum().
|
*
|
* @param a The first tensor.
|
* @param b The second tensor. Must have the same dtype as `a`.
|
*/
|
function maximumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {
|
const $a = convertToTensor(a, 'a', 'maximumStrict');
|
const $b = convertToTensor(b, 'b', 'maximumStrict');
|
util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');
|
return $a.maximum($b);
|
}
|
|
/**
|
* Returns (a - b) * (a - b) element-wise.
|
*
|
* Inputs must be the same shape. For broadcasting support, use
|
* `tf.squaredDifference` instead.
|
*
|
* @param a The first tensor.
|
* @param b The second tensor. Must have the same type as `a`.
|
*/
|
function squaredDifferenceStrict_<T extends Tensor>(
|
a: T|TensorLike, b: T|TensorLike): T {
|
const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');
|
const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');
|
util.assertShapesMatch(
|
$a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');
|
return $a.squaredDifference($b);
|
}
|
|
/**
|
* Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
|
* Supports broadcasting.
|
*
|
* ```js
|
* const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
|
* const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
|
*
|
* tf.atan2(a, b).print()
|
* ```
|
*
|
* @param a The first tensor.
|
* @param b The second tensor. Must have the same dtype as `a`.
|
*
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Basic math'} */
|
function atan2_<T extends Tensor>(
|
a: Tensor|TensorLike, b: Tensor|TensorLike): T {
|
let $a = convertToTensor(a, 'a', 'atan2');
|
let $b = convertToTensor(b, 'b', 'atan2');
|
[$a, $b] = makeTypesMatch($a, $b);
|
|
const outShape =
|
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
|
const der = (dy: Tensor, saved: Tensor[]) => {
|
const [$a, $b] = saved;
|
const derA = () => {
|
const d = add($a.square(), $b.square());
|
let res = dy.mul($b.div(d));
|
const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
|
if (reduceAxes.length > 0) {
|
res = res.sum(reduceAxes);
|
}
|
return res.reshape($a.shape);
|
};
|
const derB = () => {
|
const d = add($a.square(), $b.square());
|
let res = neg(dy.mul($a.div(d)));
|
const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
|
if (reduceAxes.length > 0) {
|
res = res.sum(reduceAxes);
|
}
|
return res.reshape($b.shape);
|
};
|
return {$a: derA, $b: derB};
|
};
|
return ENGINE.runKernelFunc((backend, save) => {
|
const res = backend.atan2($a, $b);
|
save([$a, $b]);
|
return res;
|
}, {$a, $b}, der) as T;
|
}
|
|
export const add = op({add_});
|
export const addN = op({addN_});
|
export const addStrict = op({addStrict_});
|
export const atan2 = op({atan2_});
|
export const div = op({div_});
|
export const divNoNan = op({divNoNan_});
|
export const divStrict = op({divStrict_});
|
export const floorDiv = op({floorDiv_});
|
export const maximum = op({maximum_});
|
export const maximumStrict = op({maximumStrict_});
|
export const minimum = op({minimum_});
|
export const minimumStrict = op({minimumStrict_});
|
export const mod = op({mod_});
|
export const modStrict = op({modStrict_});
|
export const mul = op({mul_});
|
export const mulStrict = op({mulStrict_});
|
export const pow = op({pow_});
|
export const powStrict = op({powStrict_});
|
export const squaredDifferenceStrict = op({squaredDifferenceStrict_});
|
export const sub = op({sub_});
|
export const subStrict = op({subStrict_});
|
