"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 __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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var _this = this;
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("../index");
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var jasmine_util_1 = require("../jasmine_util");
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var test_util_1 = require("../test_util");
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jasmine_util_1.describeWithFlags('RMSPropOptimizer', jasmine_util_1.ALL_ENVS, function () {
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it('basic', function () { return __awaiter(_this, void 0, void 0, function () {
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var learningRate, moment, rho, optimizer, x, f, numTensors, cost, _a, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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learningRate = 0.1;
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moment = 0.1;
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rho = 0.95;
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optimizer = tf.train.rmsprop(learningRate, rho, moment);
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x = tf.tensor1d([1, 2]).variable();
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f = function () { return x.square().sum(); };
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numTensors = tf.memory().numTensors;
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cost = optimizer.minimize(f, /* returnCost */ true);
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// Cost & 2 accumulators should be the only additional arrays.
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expect(tf.memory().numTensors).toBe(numTensors + 3);
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// epsilon = 1e-8
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// newAccumulatedMeanSquare =
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// rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = (0.2)
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// newAccumulatedMoments = momentum * accumulatedMoments +
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// learning_rate * gradient / sqrt(newAccumulatedMeanSquare +
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// epsilon) = 0.1 * 0 + ((0.1 * 2) / sqrt(0.2 + 1e-8)) = 0.44721
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// x -= learningRate * newAccumulatedMoments
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//
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// de/dx = [2, 4]
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// accumulatedMeanSquare = [0, 0]
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// newAccumulatedMeanSquare = [.2, .8]
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// accumulatedMoments = [0, 0]
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// newAccumulatedMoments = [0.44721, 0.44721]
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// x = [0.55279, 1.55279]
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 1:
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// epsilon = 1e-8
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// newAccumulatedMeanSquare =
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// rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = (0.2)
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// newAccumulatedMoments = momentum * accumulatedMoments +
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// learning_rate * gradient / sqrt(newAccumulatedMeanSquare +
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// epsilon) = 0.1 * 0 + ((0.1 * 2) / sqrt(0.2 + 1e-8)) = 0.44721
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// x -= learningRate * newAccumulatedMoments
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//
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// de/dx = [2, 4]
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// accumulatedMeanSquare = [0, 0]
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// newAccumulatedMeanSquare = [.2, .8]
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// accumulatedMoments = [0, 0]
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// newAccumulatedMoments = [0.44721, 0.44721]
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// x = [0.55279, 1.55279]
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_a.apply(void 0, [_c.sent(), [0.55279, 1.55279]]);
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cost.dispose();
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numTensors = tf.memory().numTensors;
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cost = optimizer.minimize(f, /* returnCost */ false);
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// x = [0.55279, 1.55279]
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// de/dx = [1.10558, 3.10558]
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// accumulatedMeanSquare = [0.2, 0.8]
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// newAccumulatedMeanSquare = [0.25105125, 1.242231]
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// accumulatedMoments = [0.44721, 0.44721]
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// newAccumulatedMoments = [0.26534, 0.32336]
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// x = [0.28745, 1.22943]
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// TODO: Fix numerical precision.
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 2:
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// x = [0.55279, 1.55279]
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// de/dx = [1.10558, 3.10558]
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// accumulatedMeanSquare = [0.2, 0.8]
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// newAccumulatedMeanSquare = [0.25105125, 1.242231]
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// accumulatedMoments = [0.44721, 0.44721]
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// newAccumulatedMoments = [0.26534, 0.32336]
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// x = [0.28745, 1.22943]
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// TODO: Fix numerical precision.
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_b.apply(void 0, [_c.sent(), [0.28745, 1.222943], 1e-2]);
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// There should be no new additional Tensors.
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expect(tf.memory().numTensors).toBe(numTensors);
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expect(cost).toBe(null);
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x.dispose();
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optimizer.dispose();
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// The only tensor remaining is the argument to variable().
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expect(tf.memory().numTensors).toBe(1);
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return [2 /*return*/];
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}
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});
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}); });
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it('gradient with centered momentum', function () { return __awaiter(_this, void 0, void 0, function () {
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var learningRate, moment, rho, eps, optimizer, x, f, numTensors, cost, _a, _b;
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return __generator(this, function (_c) {
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switch (_c.label) {
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case 0:
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learningRate = 0.1;
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moment = 0.1;
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rho = 0.95;
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eps = 1e-8;
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optimizer = tf.train.rmsprop(learningRate, rho, moment, eps, true);
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x = tf.tensor1d([1, 2]).variable();
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f = function () { return x.square().sum(); };
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numTensors = tf.memory().numTensors;
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cost = optimizer.minimize(f, /* returnCost */ true);
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// Cost & 3 accumulators should be the only additional arrays.
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expect(tf.memory().numTensors).toBe(numTensors + 4);
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// epsilon = 1e-8
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// newAccumulatedMeanSquare =
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// rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = [.2, .8]
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// newAccumulatedMeanGrad =
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// rho * accumulatedMeanGrad + (1 - rho) * grad = [0.1, 0.2]
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// newAccumulatedMoments = momentum * accumulatedMoments +
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// learning_rate * gradient / sqrt(newAccumulatedMeanSquare
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// - newAccumulatedMeanGrad * 2 +
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// epsilon) = 0.1 * 0 + ((0.1 * 2)
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// / sqrt(0.2 - 0.01 + 1e-8)) = 0.458831
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// x -= learningRate * newAccumulatedMoments
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//
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// de/dx = [2, 4]
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// accumulatedMeanSquare = [0, 0]
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// newAccumulatedMeanSquare = [.2, .8]
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// newAccumulatedMeanGrad = [.1, .2]
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// accumulatedMoments = [0, 0]
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// newAccumulatedMoments = [0.45883, 0.458831]
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// x = [0.54117, 1.541169]
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 1:
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// epsilon = 1e-8
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// newAccumulatedMeanSquare =
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// rho * accumulatedMeanSquare + (1 - rho) * grad ^ 2 = [.2, .8]
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// newAccumulatedMeanGrad =
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// rho * accumulatedMeanGrad + (1 - rho) * grad = [0.1, 0.2]
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// newAccumulatedMoments = momentum * accumulatedMoments +
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// learning_rate * gradient / sqrt(newAccumulatedMeanSquare
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// - newAccumulatedMeanGrad * 2 +
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// epsilon) = 0.1 * 0 + ((0.1 * 2)
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// / sqrt(0.2 - 0.01 + 1e-8)) = 0.458831
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// x -= learningRate * newAccumulatedMoments
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//
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// de/dx = [2, 4]
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// accumulatedMeanSquare = [0, 0]
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// newAccumulatedMeanSquare = [.2, .8]
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// newAccumulatedMeanGrad = [.1, .2]
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// accumulatedMoments = [0, 0]
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// newAccumulatedMoments = [0.45883, 0.458831]
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// x = [0.54117, 1.541169]
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_a.apply(void 0, [_c.sent(), [0.54117, 1.541169]]);
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cost.dispose();
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numTensors = tf.memory().numTensors;
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cost = optimizer.minimize(f, /* returnCost */ false);
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// x = [0.54117, 1.541169]
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// de/dx = [1.08234, 3.082338]
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// accumulatedMeanSquare = [0.2, 0.8]
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// accumulatedMeanGrad = [.1, .2]
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// newAccumulatedMeanSquare = [0.248572, 1.235040]
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// newAccumulatedMeanGrad = [0.149117, 0.3441169]
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// accumulatedMoments = [0.45883, 0.458831]
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// newAccumulatedMoments = [0.273385, 0.3375766]
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// x = [0.267785, 1.2035924]
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// TODO: Fix numerical precision.
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 2:
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// x = [0.54117, 1.541169]
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// de/dx = [1.08234, 3.082338]
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// accumulatedMeanSquare = [0.2, 0.8]
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// accumulatedMeanGrad = [.1, .2]
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// newAccumulatedMeanSquare = [0.248572, 1.235040]
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// newAccumulatedMeanGrad = [0.149117, 0.3441169]
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// accumulatedMoments = [0.45883, 0.458831]
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// newAccumulatedMoments = [0.273385, 0.3375766]
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// x = [0.267785, 1.2035924]
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// TODO: Fix numerical precision.
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_b.apply(void 0, [_c.sent(), [0.267785, 1.2035924], 1e-2]);
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// There should be no new additional Tensors.
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expect(tf.memory().numTensors).toBe(numTensors);
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expect(cost).toBe(null);
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x.dispose();
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optimizer.dispose();
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// The only tensor remaining is the argument to variable().
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expect(tf.memory().numTensors).toBe(1);
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return [2 /*return*/];
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}
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});
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}); });
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it('Save and load weights: centered = false', function () { return __awaiter(_this, void 0, void 0, function () {
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var learningRate, moment, rho, optimizer1, x, f, cost, _a, _b, weights, optimizer2, _c, _d;
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return __generator(this, function (_e) {
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switch (_e.label) {
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case 0:
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learningRate = 0.1;
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moment = 0.1;
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rho = 0.95;
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optimizer1 = tf.train.rmsprop(learningRate, rho, moment);
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x = tf.tensor1d([1, 2]).variable();
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f = function () { return x.square().sum(); };
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cost = optimizer1.minimize(f, /* returnCost */ true);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, cost.data()];
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case 1:
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_a.apply(void 0, [_e.sent(), 5]);
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 2:
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_b.apply(void 0, [_e.sent(), [0.5527865, 1.5527864]]);
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return [4 /*yield*/, optimizer1.getWeights()];
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case 3:
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weights = _e.sent();
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// An iteration variable and two optimizer state variables.
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expect(weights.length).toEqual(3);
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optimizer2 = tf.train.rmsprop(learningRate, rho, moment);
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return [4 /*yield*/, optimizer2.setWeights(weights)];
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case 4:
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_e.sent();
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cost = optimizer2.minimize(f, /* returnCost */ true);
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_c = test_util_1.expectArraysClose;
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return [4 /*yield*/, cost.data()];
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case 5:
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_c.apply(void 0, [_e.sent(), 2.7167187]);
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_d = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 6:
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_d.apply(void 0, [_e.sent(), [0.2874418, 1.2294267]]);
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expect(optimizer2.iterations).toEqual(2);
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return [2 /*return*/];
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}
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});
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}); });
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it('Save, load weights and continue training: centered = true', function () { return __awaiter(_this, void 0, void 0, function () {
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var learningRate, moment, rho, epsilon, centered, optimizer1, x, f, cost, _a, _b, weights, optimizer2, _c, _d, optimizer3, _e, _f, _g;
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return __generator(this, function (_h) {
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switch (_h.label) {
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case 0:
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learningRate = 0.1;
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moment = 0.1;
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rho = 0.95;
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epsilon = undefined;
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centered = true;
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optimizer1 = tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
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x = tf.tensor1d([1, 2]).variable();
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f = function () { return x.square().sum(); };
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cost = optimizer1.minimize(f, /* returnCost */ true);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, cost.data()];
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case 1:
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_a.apply(void 0, [_h.sent(), 5]);
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 2:
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_b.apply(void 0, [_h.sent(), [0.5411684, 1.5411685]]);
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return [4 /*yield*/, optimizer1.getWeights()];
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case 3:
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weights = _h.sent();
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// An iteration variable and three optimizer state variables.
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expect(weights.length).toEqual(4);
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optimizer2 = tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
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return [4 /*yield*/, optimizer2.setWeights(weights)];
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case 4:
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_h.sent();
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cost = optimizer2.minimize(f, /* returnCost */ true);
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_c = test_util_1.expectArraysClose;
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return [4 /*yield*/, cost.data()];
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case 5:
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_c.apply(void 0, [_h.sent(), 2.668063]);
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_d = test_util_1.expectArraysClose;
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return [4 /*yield*/, x.data()];
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case 6:
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_d.apply(void 0, [_h.sent(), [0.2677834, 1.2035918]]);
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expect(optimizer2.iterations).toEqual(2);
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optimizer3 = tf.train.rmsprop(learningRate, rho, moment, epsilon, centered);
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_f = (_e = optimizer3).setWeights;
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return [4 /*yield*/, optimizer2.getWeights()];
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case 7: return [4 /*yield*/, _f.apply(_e, [_h.sent()])];
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case 8:
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_h.sent();
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cost = optimizer3.minimize(f, /* returnCost */ true);
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_g = test_util_1.expectArraysClose;
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return [4 /*yield*/, cost.data()];
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case 9:
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_g.apply(void 0, [_h.sent(), 1.520341]);
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expect(optimizer3.iterations).toEqual(3);
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return [2 /*return*/];
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}
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});
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}); });
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it('serialization round-trip', function () {
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var originalOpt = tf.train.rmsprop(0.1, 0.5, 0.1, 1e-7, true);
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var reserialized = tf.RMSPropOptimizer.fromConfig(tf.RMSPropOptimizer, originalOpt.getConfig());
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expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
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});
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it('must define learning rate', function () {
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var learningRate = undefined;
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expect(function () { return tf.train.rmsprop(learningRate); })
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.toThrowError(/learningRate for RMSPropOptimizer must be defined./);
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});
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});
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//# sourceMappingURL=rmsprop_optimizer_test.js.map
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