@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
92 lines • 3.75 kB
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 { FusedBatchNorm } from '../kernel_names';
import { add } from '../ops/add';
import { getReductionAxes } from '../ops/broadcast_util';
import { mul } from '../ops/mul';
import { reshape } from '../ops/reshape';
import { rsqrt } from '../ops/rsqrt';
import { scalar } from '../ops/scalar';
import { sub } from '../ops/sub';
import { sum } from '../ops/sum';
import { tile } from '../ops/tile';
export const fusedBatchNormGradConfig = {
kernelName: FusedBatchNorm,
inputsToSave: ['x', 'mean', 'variance', 'scale'],
gradFunc: (dy, saved, attrs) => {
const { varianceEpsilon } = attrs;
const [x, mean, variance, scale] = saved;
const scaleValue = scale == null ? scalar(1) : scale;
const reductionAxes = getReductionAxes(mean.shape, x.shape);
const tileShape = [];
if (mean.rank === 1) {
for (let i = 0; i < x.shape.length - 1; ++i) {
tileShape.push(x.shape[i]);
}
tileShape.push(1);
}
const xMinusMean = sub(x, mean);
const dyTimesScaleValue = mul(dy, scaleValue);
const oneOverSqrtVariance = rsqrt(add(variance, scalar(varianceEpsilon)));
const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
const derX = () => {
if (mean.rank === 1) {
return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean.shape[0]]), tileShape)), scaleValue), x.shape);
}
else {
return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
}
};
const derMean = () => {
let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
if (mean.rank === 1) {
meanDer = sum(meanDer, reductionAxes);
}
return reshape(meanDer, mean.shape);
};
const derVariance = () => {
let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
if (mean.rank === 1) {
varianceDer = sum(varianceDer, reductionAxes);
}
return reshape(varianceDer, mean.shape);
};
const derScale = () => {
const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
if (mean.rank === 1) {
scaleDer = sum(scaleDer, reductionAxes);
}
return reshape(scaleDer, mean.shape);
};
const derOffset = () => {
let offsetDer = dy;
if (mean.rank === 1) {
offsetDer = sum(offsetDer, reductionAxes);
}
return reshape(offsetDer, mean.shape);
};
return {
x: derX,
mean: derMean,
variance: derVariance,
scale: derScale,
offset: derOffset
};
}
};
//# sourceMappingURL=FusedBatchNorm_grad.js.map