@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2018 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 { assertTypesMatch } from '../tensor_util';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { add } from './add';
import { div } from './div';
import { mul } from './mul';
import { op } from './operation';
import { pow } from './pow';
import { scalar } from './scalar';
import { sub } from './sub';
/**
* Compute the moving average of a variable.
*
* Without zeroDebias, the moving average operation is defined by:
* `v += delta`
* where
* `delta = (1 - decay) * (x - v)`
*
* With zeroDebias (default), the `delta` term is scaled to debias the
* effect of the (assumed) zero-initialization of `v`.
* `delta /= (1 - decay ^ step)`
*
* For more details on the zero-debiasing algorithm, see:
* https://arxiv.org/abs/1412.6980
*
* Note that this function is completely stateless and does not keep track of
* step count. The step count needs to be maintained by the caller and passed
* in as `step`.
*
* @param v The current moving average value.
* @param x New input value, must have the same shape and dtype as `v`.
* @param decay The decay factor. Typical values are 0.95 and 0.99.
* @param step Step count.
* @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).
* @returns The new moving average value.
*
* @doc {heading: 'Operations', subheading: 'Moving Average'}
*/
function movingAverage_(v, x, decay, step, zeroDebias = true) {
const $v = convertToTensor(v, 'v', 'movingAverage');
const $x = convertToTensor(x, 'x', 'movingAverage');
const $decay = convertToTensor(decay, 'decay', 'movingAverage');
assertTypesMatch($v, $x);
util.assert(util.arraysEqual($v.shape, $x.shape), () => 'Shape mismatch in v and x');
const one = scalar(1);
const oneMinusDecay = sub(one, $decay);
let update = mul(sub($x, $v), oneMinusDecay);
if (zeroDebias) {
util.assert(step != null, () => 'When using zeroDebias: true, step is required.');
const $step = convertToTensor(step, 'step', 'movingAverage');
update = div(update, sub(one, pow($decay, $step)));
}
return add($v, update);
}
export const movingAverage = op({ movingAverage_ });
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