@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
/**
* @license
* Copyright 2020 Google Inc. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var engine_1 = require("../engine");
var kernel_names_1 = require("../kernel_names");
var tensor_util_1 = require("../tensor_util");
var tensor_util_env_1 = require("../tensor_util_env");
var binary_ops_1 = require("./binary_ops");
var operation_1 = require("./operation");
/**
* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
*
* We also expose `tf.divStrict` 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, 9, 16]);
* const b = tf.tensor1d([1, 2, 3, 4]);
*
* a.div(b).print(); // or tf.div(a, b)
* ```
*
* ```js
* // Broadcast div a with b.
* const a = tf.tensor1d([2, 4, 6, 8]);
* const b = tf.scalar(2);
*
* a.div(b).print(); // or tf.div(a, b)
* ```
*
* @param a The first tensor as the numerator.
* @param b The second tensor as the denominator. Must have the same dtype as
* `a`.
*/
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function div_(a, b) {
var _a;
var $a = tensor_util_env_1.convertToTensor(a, 'a', 'div');
var $b = tensor_util_env_1.convertToTensor(b, 'b', 'div');
_a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
if ($a.dtype === 'int32' && $b.dtype === 'int32') {
return binary_ops_1.floorDiv($a, $b);
}
var forward = function (backend, save) {
var res = backend.realDivide($a, $b);
save([$a, $b]);
return res;
};
var inputs = { a: $a, b: $b };
var attrs = {};
return engine_1.ENGINE.runKernelFunc(forward, inputs, null /* gradient */, kernel_names_1.Div, attrs);
}
exports.div = operation_1.op({ div_: div_ });
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