@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
/**
* @license
* Copyright 2018 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 tensor_util_1 = require("../tensor_util");
var tensor_util_env_1 = require("../tensor_util_env");
var util = require("../util");
var binary_ops_1 = require("./binary_ops");
var operation_1 = require("./operation");
var tensor_ops_1 = require("./tensor_ops");
/**
* 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) {
if (zeroDebias === void 0) { zeroDebias = true; }
var $v = tensor_util_env_1.convertToTensor(v, 'v', 'movingAverage');
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'movingAverage');
var $decay = tensor_util_env_1.convertToTensor(decay, 'decay', 'movingAverage');
tensor_util_1.assertTypesMatch($v, $x);
util.assert(util.arraysEqual($v.shape, $x.shape), function () { return 'Shape mismatch in v and x'; });
var one = tensor_ops_1.scalar(1);
var oneMinusDecay = one.sub($decay);
var update = $x.sub($v).mul(oneMinusDecay);
if (zeroDebias) {
util.assert(step != null, function () { return 'When using zeroDebias: true, step is required.'; });
var $step = tensor_util_env_1.convertToTensor(step, 'step', 'movingAverage');
update = update.div(one.sub(binary_ops_1.pow($decay, $step)));
}
return $v.add(update);
}
exports.movingAverage = operation_1.op({ movingAverage_: movingAverage_ });
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