"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('AdamaxOptimizer', 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, beta1, beta2, decay, 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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beta1 = 0.8;
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beta2 = 0.9;
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decay = 0.1;
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optimizer = tf.train.adamax(learningRate, beta1, beta2, undefined, decay);
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x = tf.tensor1d([2, 4]).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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// new_first_m = [
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// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
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// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
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// ] = [.8, 1.6]
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//
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// ut_0 = beta2 * old_weighted_inf_norm = [0, 0]
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// u1_1 = [
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// abs(grad_w1),
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// abs(grad_w2)
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// ] = [4, 8]
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// new_weighted_inf_norm = max(ut_0, ut_1) = [4, 8]
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//
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// coefficient = alpha / (1-beta1) = 0.5
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// updates = coefficient * [
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// new_first_m1 / new_weighted_inf_norm1,
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// new_first_m2 / new_weighted_inf_norm2
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// ] = [0.1, 0.1]
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// w = [
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// w1_old - updates_1,
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// w2_old - updates_2
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// ] = [1.9, 3.9]
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//
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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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// new_first_m = [
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// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
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// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
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// ] = [.8, 1.6]
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//
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// ut_0 = beta2 * old_weighted_inf_norm = [0, 0]
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// u1_1 = [
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// abs(grad_w1),
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// abs(grad_w2)
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// ] = [4, 8]
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// new_weighted_inf_norm = max(ut_0, ut_1) = [4, 8]
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//
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// coefficient = alpha / (1-beta1) = 0.5
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// updates = coefficient * [
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// new_first_m1 / new_weighted_inf_norm1,
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// new_first_m2 / new_weighted_inf_norm2
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// ] = [0.1, 0.1]
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// w = [
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// w1_old - updates_1,
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// w2_old - updates_2
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// ] = [1.9, 3.9]
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//
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_a.apply(void 0, [_c.sent(), [1.9, 3.9]]);
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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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// gradient = [3.8, 7.8]
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// new_first_m = [
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// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
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// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
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// ] = [
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// 0.8 * 0.8 + 0.2 * 3.8,
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// 0.8 * 1.6 + 0.2 * 7.8
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// ] = [1.4, 2.84]
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//
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// ut_0 = beta2 * old_weighted_inf_norm = [
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// 0.9 * 4,
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// 0.9 * 8
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// ] = [3.6, 7.2]
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// u1_1 = [
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// abs(grad_w1),
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// abs(grad_w2)
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// ] = [3.8, 7.8]
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// new_weighted_inf_norm = max(ut_0, ut_1) = [3.8, 7.8]
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//
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// alpha = 0.1 / (1 + 0.1 * 1) = 0.0909090909
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//
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// coefficient = alpha / (1 - beta1*beta1) = 0.25252525
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// updates = coefficient * [
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// new_first_m1 / new_weighted_inf_norm1,
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// new_first_m2 / new_weighted_inf_norm2
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// ] = [0.09303, 0.09194]
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// w = [
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// w1_old - updates_1,
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// w2_old - updates_2
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// ] = [1.80697, 3.8086]
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//
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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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// gradient = [3.8, 7.8]
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// new_first_m = [
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// beta1 * old_first_m_w1 + (1-beta1) * grad_w1,
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// beta1 * old_first_m_w2 + (1-beta1) * grad_w2
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// ] = [
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// 0.8 * 0.8 + 0.2 * 3.8,
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// 0.8 * 1.6 + 0.2 * 7.8
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// ] = [1.4, 2.84]
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//
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// ut_0 = beta2 * old_weighted_inf_norm = [
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// 0.9 * 4,
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// 0.9 * 8
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// ] = [3.6, 7.2]
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// u1_1 = [
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// abs(grad_w1),
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// abs(grad_w2)
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// ] = [3.8, 7.8]
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// new_weighted_inf_norm = max(ut_0, ut_1) = [3.8, 7.8]
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//
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// alpha = 0.1 / (1 + 0.1 * 1) = 0.0909090909
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//
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// coefficient = alpha / (1 - beta1*beta1) = 0.25252525
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// updates = coefficient * [
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// new_first_m1 / new_weighted_inf_norm1,
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// new_first_m2 / new_weighted_inf_norm2
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// ] = [0.09303, 0.09194]
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// w = [
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// w1_old - updates_1,
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// w2_old - updates_2
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// ] = [1.80697, 3.8086]
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//
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_b.apply(void 0, [_c.sent(), [1.80697, 3.8086]]);
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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 should be 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('serialization round-trip', function () {
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var originalOpt = tf.train.adamax(0.1, 0.2, 0.3, 2e-8, 0.1);
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var reserialized = tf.AdamaxOptimizer.fromConfig(tf.AdamaxOptimizer, originalOpt.getConfig());
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expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
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});
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});
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//# sourceMappingURL=adamax_optimizer_test.js.map
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