@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import { Pow } from '../kernel_names';
import * as broadcast_util from '../ops/broadcast_util';
import { cast } from '../ops/cast';
import { greater } from '../ops/greater';
import { log } from '../ops/log';
import { mul } from '../ops/mul';
import { pow } from '../ops/pow';
import { reshape } from '../ops/reshape';
import { scalar } from '../ops/scalar';
import { sub } from '../ops/sub';
import { sum } from '../ops/sum';
import { where } from '../ops/where';
import { zerosLike } from '../ops/zeros_like';
export const powGradConfig = {
kernelName: Pow,
inputsToSave: ['a', 'b'],
outputsToSave: [true],
gradFunc: (dy, saved) => {
const [a, b, y] = saved;
const base = a;
const exp = b;
const outShape = broadcast_util.assertAndGetBroadcastShape(base.shape, exp.shape);
const derBase = () => {
const expFloat = cast(exp, 'float32');
let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
const reduceAxes = broadcast_util.getReductionAxes(base.shape, outShape);
if (reduceAxes.length > 0) {
res = sum(res, reduceAxes);
}
return reshape(res, base.shape);
};
const derExp = () => {
const condition = greater(base, 0);
const logBase = where(condition, log(base), zerosLike(base));
let res = mul(dy, mul(y, logBase));
const reduceAxes = broadcast_util.getReductionAxes(exp.shape, outShape);
if (reduceAxes.length > 0) {
res = sum(res, reduceAxes);
}
return reshape(res, exp.shape);
};
return { a: derBase, b: derExp };
}
};
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