"use strict";
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/**
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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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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("../engine");
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var tensor_util_1 = require("../tensor_util");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var broadcast_util = require("./broadcast_util");
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var logical_ops_1 = require("./logical_ops");
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var operation_1 = require("./operation");
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var tensor_ops_1 = require("./tensor_ops");
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var unary_ops_1 = require("./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_(a, b) {
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var _a;
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'add');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'add');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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var der = function (dy) {
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var derA = function () {
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var res = dy;
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var reduceAxes = broadcast_util.getReductionAxes($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($a.shape);
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};
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var derB = function () {
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var res = dy;
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var reduceAxes = broadcast_util.getReductionAxes($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($b.shape);
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};
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return { a: derA, b: derB };
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};
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return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.add($a, $b); }, { a: $a, b: $b }, der, 'Add');
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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_(tensors) {
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util.assert(Array.isArray(tensors), function () { return 'The argument passed to tf.addN() must be a list of tensors'; });
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util.assert(tensors.length >= 1, function () { return "Must pass at least one tensor to tf.addN(), but got " +
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("" + tensors.length); });
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var $tensors = tensors.map(function (t, i) { return tensor_util_env_1.convertToTensor(t, "tensors" + i, 'addN'); });
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var firstTensor = $tensors[0];
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$tensors.forEach(function (t) {
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if (t.dtype !== firstTensor.dtype) {
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throw new Error('All tensors passed to tf.addN() must have the same dtype');
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}
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});
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$tensors.forEach(function (t) {
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if (!util.arraysEqual(t.shape, firstTensor.shape)) {
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throw new Error('All tensors passed to tf.addN() must have the same shape');
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}
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});
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var der = function (dy) {
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var ders = {};
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$tensors.forEach(function (t, i) {
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ders[i] = function () { return dy.clone(); };
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});
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return ders;
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};
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var inputs = $tensors;
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return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.addN($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_(a, b) {
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'addStrict');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'addStrict');
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util.assertShapesMatch($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_(a, b) {
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var _a;
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'sub');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'sub');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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var der = function (dy) {
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var derA = function () {
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var res = dy;
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var reduceAxes = broadcast_util.getReductionAxes($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($a.shape);
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};
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var derB = function () {
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var res = dy;
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var reduceAxes = broadcast_util.getReductionAxes($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($b.shape);
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};
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return { a: derA, b: derB };
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};
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return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.subtract($a, $b); }, { a: $a, b: $b }, der, 'Sub');
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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_(a, b) {
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'subStrict');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'subStrict');
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util.assertShapesMatch($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_(base, exp) {
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var _a;
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var $base = tensor_util_env_1.convertToTensor(base, 'base', 'pow');
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var $exp = tensor_util_env_1.convertToTensor(exp, 'exp', 'pow');
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_a = tensor_util_1.makeTypesMatch($base, $exp), $base = _a[0], $exp = _a[1];
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var outShape = broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);
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var grad = function (dy, saved) {
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var $base = saved[0], $exp = saved[1], y = saved[2];
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var derBase = function () {
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var expFloat = $exp.toFloat();
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var res = dy.mul(expFloat.mul($base.pow(expFloat.sub(tensor_ops_1.scalar(1)))));
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var 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);
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};
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var derExp = function () {
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var condition = $base.greater(0);
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var logBase = $base.log().where(condition, tensor_ops_1.zerosLike($base));
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var res = dy.mul(y.mul(logBase));
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var 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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var attrs = {};
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var inputsToSave = [$base, $exp];
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var outputsToSave = [true];
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return engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var y = backend.pow($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);
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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_(base, exp) {
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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_(a, b) {
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var _a;
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'mul');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'mul');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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var der = function (dy, saved) {
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var $a = saved[0], $b = saved[1];
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var derA = function () {
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var res = dy.mul($b.toFloat());
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var 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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var derB = function () {
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var res = dy.mul($a.toFloat());
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var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
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if (reduceAxes.length > 0) {
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return res.sum(reduceAxes).reshape($b.shape);
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}
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return res;
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};
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return { a: derA, b: derB };
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};
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return engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.multiply($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');
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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_(a, b) {
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'mul');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'mul');
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util.assertShapesMatch($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_(a, b) {
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var _a;
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'div');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'div');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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if ($a.dtype === 'int32' && $b.dtype === 'int32') {
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return exports.floorDiv($a, $b);
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}
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var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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var der = function (dy, saved) {
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var $a = saved[0], $b = saved[1];
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var derA = function () {
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var res = dy.div($b.toFloat());
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var 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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var derB = function () {
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var res = dy.mul($a.toFloat());
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var reduceAxes = broadcast_util.getReductionAxes($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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var tmp = $b.square();
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return res.div(tmp.toFloat()).neg();
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};
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return { a: derA, b: derB };
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};
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return engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.realDivide($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');
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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_(a, b) {
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var _a;
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'div');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'div');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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var divResult = exports.div($a, $b);
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var zeros = tensor_ops_1.zerosLike(divResult);
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var bEqualsZero = $b.equal(zeros);
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return logical_ops_1.where(bEqualsZero, zeros, divResult);
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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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* 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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*
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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 floorDiv_(a, b) {
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var _a;
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var $a = tensor_util_env_1.convertToTensor(a, 'a', 'floorDiv');
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var $b = tensor_util_env_1.convertToTensor(b, 'b', 'floorDiv');
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_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
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var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
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var der = function (dy, saved) {
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var $a = saved[0], $b = saved[1];
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var derA = function () {
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var res = dy.div($b.toFloat());
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var 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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var derB = function () {
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var res = dy.mul($a.toFloat());
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var reduceAxes = broadcast_util.getReductionAxes($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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var tmp = $b.square();
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return res.div(tmp.toFloat()).neg();
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};
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return { a: derA, b: derB };
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};
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return engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.floorDiv($a, $b);
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save([$a, $b]);
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return res;
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}, { a: $a, b: $b }, der, 'FloorDiv');
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}
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Inputs must
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* 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_(a, b) {
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'div');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'div');
|
util.assertShapesMatch($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_(a, b) {
|
var _a;
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'mod');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'mod');
|
_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
|
var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
var der = function (dy, saved) {
|
var $a = saved[0], $b = saved[1];
|
var derA = function () {
|
var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
|
if (reduceAxes.length > 0) {
|
return dy.sum(reduceAxes).reshape($a.shape);
|
}
|
return dy;
|
};
|
var derB = function () {
|
var res = dy.mul($a.div($b).floor().neg());
|
var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
|
if (reduceAxes.length > 0) {
|
return res.sum(reduceAxes).reshape($b.shape);
|
}
|
return res;
|
};
|
return { $a: derA, $b: derB };
|
};
|
return engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.mod($a, $b);
|
save([$a, $b]);
|
return res;
|
}, { $a: $a, $b: $b }, der);
|
}
|
/**
|
* 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_(a, b) {
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'modStrict');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'modStrict');
|
util.assertShapesMatch($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_(a, b) {
|
var _a;
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'minimum');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'minimum');
|
_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
|
if ($a.dtype === 'bool') {
|
$a = $a.toInt();
|
$b = $b.toInt();
|
}
|
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
var der = function (dy, saved) {
|
var $a = saved[0], $b = saved[1];
|
var derA = function () { return dy.mul($a.lessEqual($b).toFloat()); };
|
var derB = function () { return dy.mul($a.greater($b).toFloat()); };
|
return { a: derA, b: derB };
|
};
|
return engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.minimum($a, $b);
|
save([$a, $b]);
|
return res;
|
}, { a: $a, b: $b }, der, 'Minimum');
|
}
|
/**
|
* 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_(a, b) {
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'minimumStrict');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'minimumStrict');
|
util.assertShapesMatch($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_(a, b) {
|
var _a;
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'maximum');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'maximum');
|
_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
|
if ($a.dtype === 'bool') {
|
$a = $a.toInt();
|
$b = $b.toInt();
|
}
|
broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
var der = function (dy, saved) {
|
var $a = saved[0], $b = saved[1];
|
var derA = function () { return dy.mul($a.greaterEqual($b).toFloat()); };
|
var derB = function () { return dy.mul($a.less($b).toFloat()); };
|
return { a: derA, b: derB };
|
};
|
return engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.maximum($a, $b);
|
save([$a, $b]);
|
return res;
|
}, { a: $a, b: $b }, der, 'Maximum');
|
}
|
/**
|
* 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_(a, b) {
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'maximumStrict');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'maximumStrict');
|
util.assertShapesMatch($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_(a, b) {
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'squaredDifferenceStrict');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'squaredDifferenceStrict');
|
util.assertShapesMatch($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_(a, b) {
|
var _a;
|
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'atan2');
|
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'atan2');
|
_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
|
var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
|
var der = function (dy, saved) {
|
var $a = saved[0], $b = saved[1];
|
var derA = function () {
|
var d = exports.add($a.square(), $b.square());
|
var res = dy.mul($b.div(d));
|
var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
|
if (reduceAxes.length > 0) {
|
res = res.sum(reduceAxes);
|
}
|
return res.reshape($a.shape);
|
};
|
var derB = function () {
|
var d = exports.add($a.square(), $b.square());
|
var res = unary_ops_1.neg(dy.mul($a.div(d)));
|
var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
|
if (reduceAxes.length > 0) {
|
res = res.sum(reduceAxes);
|
}
|
return res.reshape($b.shape);
|
};
|
return { $a: derA, $b: derB };
|
};
|
return engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.atan2($a, $b);
|
save([$a, $b]);
|
return res;
|
}, { $a: $a, $b: $b }, der);
|
}
|
exports.add = operation_1.op({ add_: add_ });
|
exports.addN = operation_1.op({ addN_: addN_ });
|
exports.addStrict = operation_1.op({ addStrict_: addStrict_ });
|
exports.atan2 = operation_1.op({ atan2_: atan2_ });
|
exports.div = operation_1.op({ div_: div_ });
|
exports.divNoNan = operation_1.op({ divNoNan_: divNoNan_ });
|
exports.divStrict = operation_1.op({ divStrict_: divStrict_ });
|
exports.floorDiv = operation_1.op({ floorDiv_: floorDiv_ });
|
exports.maximum = operation_1.op({ maximum_: maximum_ });
|
exports.maximumStrict = operation_1.op({ maximumStrict_: maximumStrict_ });
|
exports.minimum = operation_1.op({ minimum_: minimum_ });
|
exports.minimumStrict = operation_1.op({ minimumStrict_: minimumStrict_ });
|
exports.mod = operation_1.op({ mod_: mod_ });
|
exports.modStrict = operation_1.op({ modStrict_: modStrict_ });
|
exports.mul = operation_1.op({ mul_: mul_ });
|
exports.mulStrict = operation_1.op({ mulStrict_: mulStrict_ });
|
exports.pow = operation_1.op({ pow_: pow_ });
|
exports.powStrict = operation_1.op({ powStrict_: powStrict_ });
|
exports.squaredDifferenceStrict = operation_1.op({ squaredDifferenceStrict_: squaredDifferenceStrict_ });
|
exports.sub = operation_1.op({ sub_: sub_ });
|
exports.subStrict = operation_1.op({ subStrict_: subStrict_ });
|
//# sourceMappingURL=binary_ops.js.map
|
