"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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Object.defineProperty(exports, "__esModule", { value: true });
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var adadelta_optimizer_1 = require("./adadelta_optimizer");
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var adagrad_optimizer_1 = require("./adagrad_optimizer");
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var adam_optimizer_1 = require("./adam_optimizer");
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var adamax_optimizer_1 = require("./adamax_optimizer");
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var momentum_optimizer_1 = require("./momentum_optimizer");
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var rmsprop_optimizer_1 = require("./rmsprop_optimizer");
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var sgd_optimizer_1 = require("./sgd_optimizer");
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var OptimizerConstructors = /** @class */ (function () {
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function OptimizerConstructors() {
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}
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/**
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* Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.
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*
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* ```js
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* // Fit a quadratic function by learning the coefficients a, b, c.
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* const xs = tf.tensor1d([0, 1, 2, 3]);
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* const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
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*
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* const a = tf.scalar(Math.random()).variable();
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* const b = tf.scalar(Math.random()).variable();
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* const c = tf.scalar(Math.random()).variable();
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*
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* // y = a * x^2 + b * x + c.
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* const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
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* const loss = (pred, label) => pred.sub(label).square().mean();
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*
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* const learningRate = 0.01;
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* const optimizer = tf.train.sgd(learningRate);
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*
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* // Train the model.
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* for (let i = 0; i < 10; i++) {
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* optimizer.minimize(() => loss(f(xs), ys));
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* }
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*
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* // Make predictions.
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* console.log(
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* `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);
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* const preds = f(xs).dataSync();
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* preds.forEach((pred, i) => {
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* console.log(`x: ${i}, pred: ${pred}`);
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* });
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* ```
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*
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* @param learningRate The learning rate to use for the SGD algorithm.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.sgd = function (learningRate) {
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return new sgd_optimizer_1.SGDOptimizer(learningRate);
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};
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/**
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* Constructs a `tf.MomentumOptimizer` that uses momentum gradient
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* descent.
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*
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* See
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* [http://proceedings.mlr.press/v28/sutskever13.pdf](
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* http://proceedings.mlr.press/v28/sutskever13.pdf)
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*
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* @param learningRate The learning rate to use for the Momentum gradient
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* descent algorithm.
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* @param momentum The momentum to use for the momentum gradient descent
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* algorithm.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.momentum = function (learningRate, momentum, useNesterov) {
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if (useNesterov === void 0) { useNesterov = false; }
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return new momentum_optimizer_1.MomentumOptimizer(learningRate, momentum, useNesterov);
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};
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/**
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* Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient
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* descent. This implementation uses plain momentum and is not centered
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* version of RMSProp.
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*
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* See
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* [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](
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* http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
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*
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* @param learningRate The learning rate to use for the RMSProp gradient
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* descent algorithm.
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* @param decay The discounting factor for the history/coming gradient.
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* @param momentum The momentum to use for the RMSProp gradient descent
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* algorithm.
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* @param epsilon Small value to avoid zero denominator.
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* @param centered If true, gradients are normalized by the estimated
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* variance of the gradient.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.rmsprop = function (learningRate, decay, momentum, epsilon, centered) {
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if (decay === void 0) { decay = .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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return new rmsprop_optimizer_1.RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);
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};
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/**
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* Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.
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* See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
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*
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* @param learningRate The learning rate to use for the Adam gradient
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* descent algorithm.
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* @param beta1 The exponential decay rate for the 1st moment estimates.
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* @param beta2 The exponential decay rate for the 2nd moment estimates.
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* @param epsilon A small constant for numerical stability.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.adam = function (learningRate, beta1, beta2, epsilon) {
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if (learningRate === void 0) { learningRate = 0.001; }
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if (beta1 === void 0) { beta1 = 0.9; }
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if (beta2 === void 0) { beta2 = 0.999; }
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if (epsilon === void 0) { epsilon = null; }
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return new adam_optimizer_1.AdamOptimizer(learningRate, beta1, beta2, epsilon);
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};
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/**
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* Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.
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* See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)
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*
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* @param learningRate The learning rate to use for the Adadelta gradient
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* descent algorithm.
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* @param rho The learning rate decay over each update.
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* @param epsilon A constant epsilon used to better condition the grad
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* update.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.adadelta = function (learningRate, rho, epsilon) {
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if (learningRate === void 0) { learningRate = .001; }
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if (rho === void 0) { rho = .95; }
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if (epsilon === void 0) { epsilon = null; }
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return new adadelta_optimizer_1.AdadeltaOptimizer(learningRate, rho, epsilon);
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};
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/**
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* Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.
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* See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
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*
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* @param learningRate The learning rate to use for the Adamax gradient
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* descent algorithm.
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* @param beta1 The exponential decay rate for the 1st moment estimates.
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* @param beta2 The exponential decay rate for the 2nd moment estimates.
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* @param epsilon A small constant for numerical stability.
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* @param decay The learning rate decay over each update.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.adamax = function (learningRate, beta1, beta2, epsilon, decay) {
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if (learningRate === void 0) { learningRate = 0.002; }
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if (beta1 === void 0) { beta1 = 0.9; }
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if (beta2 === void 0) { beta2 = 0.999; }
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if (epsilon === void 0) { epsilon = null; }
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if (decay === void 0) { decay = 0.0; }
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return new adamax_optimizer_1.AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);
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};
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/**
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* Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.
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* See
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* [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](
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* http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
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* or
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* [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](
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* http://ruder.io/optimizing-gradient-descent/index.html#adagrad)
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*
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* @param learningRate The learning rate to use for the Adagrad gradient
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* descent algorithm.
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* @param initialAccumulatorValue Starting value for the accumulators, must be
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* positive.
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*/
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/**
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* @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
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*/
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OptimizerConstructors.adagrad = function (learningRate, initialAccumulatorValue) {
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if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
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return new adagrad_optimizer_1.AdagradOptimizer(learningRate, initialAccumulatorValue);
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};
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return OptimizerConstructors;
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}());
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exports.OptimizerConstructors = OptimizerConstructors;
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//# sourceMappingURL=optimizer_constructors.js.map
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