@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
;
/**
* @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 kernel_names_1 = require("../kernel_names");
var broadcast_util = require("../ops/broadcast_util");
var div_1 = require("../ops/div");
var reduction_ops_1 = require("../ops/reduction_ops");
var square_1 = require("../ops/square");
var unary_ops_1 = require("../ops/unary_ops");
exports.divGradConfig = {
kernelName: kernel_names_1.Div,
inputsToSave: ['a', 'b'],
gradFunc: function (dy, saved) {
var a = saved[0], b = saved[1];
var outShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
var derA = function () {
var res = div_1.div(dy, b.toFloat());
var reduceAxes = broadcast_util.getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
return reduction_ops_1.sum(res, reduceAxes).reshape(a.shape);
}
return res;
};
var derB = function () {
var res = dy.mul(a.toFloat());
var reduceAxes = broadcast_util.getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
res = reduction_ops_1.sum(res, reduceAxes).reshape(b.shape);
}
var tmp = square_1.square(b);
return unary_ops_1.neg(div_1.div(res, tmp.toFloat()));
};
return { a: derA, b: derB };
}
};
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