"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __extends = (this && this.__extends) || (function () {
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var extendStatics = function (d, b) {
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extendStatics = Object.setPrototypeOf ||
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({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
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function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
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return extendStatics(d, b);
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};
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return function (d, b) {
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extendStatics(d, b);
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function __() { this.constructor = d; }
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d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
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};
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})();
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("../engine");
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var globals_1 = require("../globals");
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var ops_1 = require("../ops/ops");
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var serialization_1 = require("../serialization");
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var optimizer_1 = require("./optimizer");
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/** @doclink Optimizer */
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var RMSPropOptimizer = /** @class */ (function (_super) {
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__extends(RMSPropOptimizer, _super);
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function RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered) {
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if (decay === void 0) { decay = 0.9; }
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if (momentum === void 0) { momentum = 0.0; }
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if (epsilon === void 0) { epsilon = null; }
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if (centered === void 0) { centered = false; }
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var _this = _super.call(this) || this;
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_this.learningRate = learningRate;
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_this.decay = decay;
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_this.momentum = momentum;
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_this.epsilon = epsilon;
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_this.accumulatedMeanSquares = [];
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_this.accumulatedMoments = [];
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_this.accumulatedMeanGrads = [];
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_this.centered = centered;
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if (epsilon == null) {
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_this.epsilon = engine_1.ENGINE.backend.epsilon();
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}
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if (learningRate == null) {
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throw new Error("learningRate for RMSPropOptimizer must be defined.");
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}
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return _this;
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}
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RMSPropOptimizer.prototype.applyGradients = function (variableGradients) {
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var _this = this;
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var variableNames = Array.isArray(variableGradients) ?
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variableGradients.map(function (item) { return item.name; }) :
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Object.keys(variableGradients);
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variableNames.forEach(function (name, i) {
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var value = engine_1.ENGINE.registeredVariables[name];
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var trainable = false;
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if (_this.accumulatedMeanSquares[i] == null) {
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_this.accumulatedMeanSquares[i] = {
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originalName: name + "/rms",
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variable: globals_1.tidy(function () { return ops_1.zerosLike(value).variable(trainable); })
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};
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}
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if (_this.accumulatedMoments[i] == null) {
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_this.accumulatedMoments[i] = {
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originalName: name + "/momentum",
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variable: globals_1.tidy(function () { return ops_1.zerosLike(value).variable(trainable); })
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};
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}
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if (_this.accumulatedMeanGrads[i] == null && _this.centered) {
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_this.accumulatedMeanGrads[i] = {
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originalName: name + "/mg",
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variable: globals_1.tidy(function () { return ops_1.zerosLike(value).variable(trainable); })
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};
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}
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var gradient = Array.isArray(variableGradients) ?
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variableGradients[i].tensor :
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variableGradients[name];
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if (gradient == null) {
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return;
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}
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var accumulatedMeanSquare = _this.accumulatedMeanSquares[i].variable;
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var accumulatedMoments = _this.accumulatedMoments[i].variable;
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globals_1.tidy(function () {
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var newAccumulatedMeanSquare = accumulatedMeanSquare.mul(_this.decay)
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.add(gradient.square().mul(1 - _this.decay));
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if (_this.centered) {
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var accumulatedMeanGrad = _this.accumulatedMeanGrads[i].variable;
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// Centered gradient
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var newAccumulatedMeanGrad = accumulatedMeanGrad.mul(_this.decay)
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.add(gradient.mul(1 - _this.decay));
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var newAccumulatedMoments = accumulatedMoments.mul(_this.momentum)
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.add(gradient.mul(_this.learningRate)
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.div(newAccumulatedMeanSquare
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.sub(newAccumulatedMeanGrad.square().add(_this.epsilon))
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.sqrt()));
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accumulatedMeanSquare.assign(newAccumulatedMeanSquare);
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accumulatedMeanGrad.assign(newAccumulatedMeanGrad);
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accumulatedMoments.assign(newAccumulatedMoments);
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var newValue = value.sub(newAccumulatedMoments);
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value.assign(newValue);
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}
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else {
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// Plain gradient
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var newAccumulatedMeanSquare_1 = accumulatedMeanSquare.mul(_this.decay)
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.add(gradient.square().mul(1 - _this.decay));
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var newAccumulatedMoments = accumulatedMoments.mul(_this.momentum)
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.add(gradient.mul(_this.learningRate)
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.div(newAccumulatedMeanSquare_1.add(_this.epsilon)
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.sqrt()));
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accumulatedMeanSquare.assign(newAccumulatedMeanSquare_1);
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accumulatedMoments.assign(newAccumulatedMoments);
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var newValue = value.sub(newAccumulatedMoments);
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value.assign(newValue);
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}
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});
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});
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this.incrementIterations();
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};
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RMSPropOptimizer.prototype.dispose = function () {
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if (this.accumulatedMeanSquares != null) {
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globals_1.dispose(this.accumulatedMeanSquares.map(function (v) { return v.variable; }));
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}
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if (this.accumulatedMeanGrads != null && this.centered) {
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globals_1.dispose(this.accumulatedMeanGrads.map(function (v) { return v.variable; }));
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}
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if (this.accumulatedMoments != null) {
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globals_1.dispose(this.accumulatedMoments.map(function (v) { return v.variable; }));
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}
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};
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RMSPropOptimizer.prototype.getWeights = function () {
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return __awaiter(this, void 0, void 0, function () {
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var variables;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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variables = this.accumulatedMeanSquares.concat(this.accumulatedMoments);
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if (this.centered) {
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variables.push.apply(variables, this.accumulatedMeanGrads);
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}
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return [4 /*yield*/, this.saveIterations()];
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case 1: return [2 /*return*/, [_a.sent()].concat(variables.map(function (v) { return ({ name: v.originalName, tensor: v.variable }); }))];
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}
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});
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});
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};
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RMSPropOptimizer.prototype.setWeights = function (weightValues) {
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return __awaiter(this, void 0, void 0, function () {
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var variableCount, trainable;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0: return [4 /*yield*/, this.extractIterations(weightValues)];
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case 1:
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weightValues = _a.sent();
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variableCount = this.centered ? weightValues.length / 3 : weightValues.length / 2;
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trainable = false;
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this.accumulatedMeanSquares =
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weightValues.slice(0, variableCount).map(function (v) { return ({
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originalName: v.name,
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variable: v.tensor.variable(trainable)
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}); });
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this.accumulatedMoments =
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weightValues.slice(variableCount, variableCount * 2)
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.map(function (v) { return ({
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originalName: v.name,
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variable: v.tensor.variable(trainable)
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}); });
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if (this.centered) {
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this.accumulatedMeanGrads =
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weightValues.slice(variableCount * 2, variableCount * 3)
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.map(function (v) { return ({
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originalName: v.name,
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variable: v.tensor.variable(trainable)
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}); });
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}
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return [2 /*return*/];
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}
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});
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});
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};
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RMSPropOptimizer.prototype.getConfig = function () {
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return {
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'learningRate': this.learningRate,
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'decay': this.decay,
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'momentum': this.momentum,
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'epsilon': this.epsilon,
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'centered': this.centered
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};
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};
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/** @nocollapse */
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RMSPropOptimizer.fromConfig = function (cls, config) {
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return new cls(config['learningRate'], config['decay'], config['momentum'], config['epsilon'], config['centered']);
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};
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/** @nocollapse */
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RMSPropOptimizer.className = 'RMSProp'; // Note: Name matters for Python compatibility.
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return RMSPropOptimizer;
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}(optimizer_1.Optimizer));
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exports.RMSPropOptimizer = RMSPropOptimizer;
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serialization_1.registerClass(RMSPropOptimizer);
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//# sourceMappingURL=rmsprop_optimizer.js.map
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