"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 globals_1 = require("../globals");
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var gradients_1 = require("../gradients");
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var ops_1 = require("../ops/ops");
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var serialization_1 = require("../serialization");
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/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */
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var Optimizer = /** @class */ (function (_super) {
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__extends(Optimizer, _super);
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function Optimizer() {
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return _super !== null && _super.apply(this, arguments) || this;
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}
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/**
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* Executes `f()` and minimizes the scalar output of `f()` by computing
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* gradients of y with respect to the list of trainable variables provided by
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* `varList`. If no list is provided, it defaults to all trainable variables.
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*
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* @param f The function to execute and whose output to minimize.
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* @param returnCost Whether to return the scalar cost value produced by
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* executing `f()`.
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* @param varList An optional list of variables to update. If specified, only
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* the trainable variables in varList will be updated by minimize. Defaults to
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* all trainable variables.
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*/
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/** @doc {heading: 'Training', subheading: 'Optimizers'} */
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Optimizer.prototype.minimize = function (f, returnCost, varList) {
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if (returnCost === void 0) { returnCost = false; }
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var _a = this.computeGradients(f, varList), value = _a.value, grads = _a.grads;
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if (varList != null) {
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var gradArray = varList.map(function (v) { return ({ name: v.name, tensor: grads[v.name] }); });
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this.applyGradients(gradArray);
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}
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else {
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this.applyGradients(grads);
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}
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// Dispose gradients.
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globals_1.dispose(grads);
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if (returnCost) {
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return value;
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}
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else {
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value.dispose();
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return null;
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}
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};
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Object.defineProperty(Optimizer.prototype, "iterations", {
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/**
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* The number of iterations that this optimizer instance has been invoked for.
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*/
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get: function () {
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if (this.iterations_ == null) {
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this.iterations_ = 0;
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}
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return this.iterations_;
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},
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enumerable: true,
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configurable: true
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});
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Optimizer.prototype.incrementIterations = function () {
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this.iterations_ = this.iterations + 1;
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};
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/**
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* Executes f() and computes the gradient of the scalar output of f() with
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* respect to the list of trainable variables provided by `varList`. If no
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* list is provided, it defaults to all trainable variables.
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*
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* @param f The function to execute and whose output to use for computing
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* gradients with respect to variables.
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* @param varList An optional list of variables to compute gradients with
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* respect to. If specified, only the trainable variables in varList will have
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* gradients computed with respect to. Defaults to all trainable variables.
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*/
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Optimizer.prototype.computeGradients = function (f, varList) {
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return gradients_1.variableGrads(f, varList);
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};
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/**
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* Dispose the variables (if any) owned by this optimizer instance.
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*/
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Optimizer.prototype.dispose = function () {
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if (this.iterations_ != null) {
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globals_1.dispose(this.iterations_);
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}
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};
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Optimizer.prototype.saveIterations = function () {
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return __awaiter(this, void 0, void 0, function () {
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return __generator(this, function (_a) {
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if (this.iterations_ == null) {
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this.iterations_ = 0;
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}
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return [2 /*return*/, {
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name: 'iter',
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// TODO(cais): Use 'int64' type when available.
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tensor: ops_1.scalar(this.iterations_, 'int32')
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}];
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});
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});
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};
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Optimizer.prototype.getWeights = function () {
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return __awaiter(this, void 0, void 0, function () {
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return __generator(this, function (_a) {
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throw new Error('getWeights() is not implemented for this optimizer yet.');
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});
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});
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};
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Optimizer.prototype.setWeights = function (weightValues) {
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return __awaiter(this, void 0, void 0, function () {
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return __generator(this, function (_a) {
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throw new Error("setWeights() is not implemented for this optimizer class " +
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("" + this.getClassName()));
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});
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});
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};
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/**
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* Extract the first element of the weight values and set it
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* as the iterations counter variable of this instance of optimizer.
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*
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* @param weightValues
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* @returns Weight values with the first element consumed and excluded.
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*/
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Optimizer.prototype.extractIterations = function (weightValues) {
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return __awaiter(this, void 0, void 0, function () {
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var _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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_a = this;
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return [4 /*yield*/, weightValues[0].tensor.data()];
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case 1:
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_a.iterations_ = (_b.sent())[0];
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return [2 /*return*/, weightValues.slice(1)];
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}
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});
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});
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};
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return Optimizer;
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}(serialization_1.Serializable));
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exports.Optimizer = Optimizer;
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Object.defineProperty(Optimizer, Symbol.hasInstance, {
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value: function (instance) {
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return instance.minimize != null && instance.computeGradients != null &&
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instance.applyGradients != null;
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}
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});
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//# sourceMappingURL=optimizer.js.map
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