@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.d.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors" />
import { AdadeltaOptimizer } from './adadelta_optimizer';
import { AdagradOptimizer } from './adagrad_optimizer';
import { AdamOptimizer } from './adam_optimizer';
import { AdamaxOptimizer } from './adamax_optimizer';
import { MomentumOptimizer } from './momentum_optimizer';
import { RMSPropOptimizer } from './rmsprop_optimizer';
import { SGDOptimizer } from './sgd_optimizer';
export declare class OptimizerConstructors {
    /**
     * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.
     *
     * ```js
     * // Fit a quadratic function by learning the coefficients a, b, c.
     * const xs = tf.tensor1d([0, 1, 2, 3]);
     * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);
     *
     * const a = tf.scalar(Math.random()).variable();
     * const b = tf.scalar(Math.random()).variable();
     * const c = tf.scalar(Math.random()).variable();
     *
     * // y = a * x^2 + b * x + c.
     * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);
     * const loss = (pred, label) => pred.sub(label).square().mean();
     *
     * const learningRate = 0.01;
     * const optimizer = tf.train.sgd(learningRate);
     *
     * // Train the model.
     * for (let i = 0; i < 10; i++) {
     *   optimizer.minimize(() => loss(f(xs), ys));
     * }
     *
     * // Make predictions.
     * console.log(
     *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);
     * const preds = f(xs).dataSync();
     * preds.forEach((pred, i) => {
     *   console.log(`x: ${i}, pred: ${pred}`);
     * });
     * ```
     *
     * @param learningRate The learning rate to use for the SGD algorithm.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static sgd(learningRate: number): SGDOptimizer;
    /**
     * Constructs a `tf.MomentumOptimizer` that uses momentum gradient
     * descent.
     *
     * See
     * [http://proceedings.mlr.press/v28/sutskever13.pdf](
     * http://proceedings.mlr.press/v28/sutskever13.pdf)
     *
     * @param learningRate The learning rate to use for the Momentum gradient
     * descent algorithm.
     * @param momentum The momentum to use for the momentum gradient descent
     * algorithm.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static momentum(learningRate: number, momentum: number, useNesterov?: boolean): MomentumOptimizer;
    /**
     * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient
     * descent. This implementation uses plain momentum and is not centered
     * version of RMSProp.
     *
     * See
     * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](
     * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
     *
     * @param learningRate The learning rate to use for the RMSProp gradient
     * descent algorithm.
     * @param decay The discounting factor for the history/coming gradient.
     * @param momentum The momentum to use for the RMSProp gradient descent
     * algorithm.
     * @param epsilon Small value to avoid zero denominator.
     * @param centered If true, gradients are normalized by the estimated
     * variance of the gradient.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static rmsprop(learningRate: number, decay?: number, momentum?: number, epsilon?: number, centered?: boolean): RMSPropOptimizer;
    /**
     * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.
     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
     *
     * @param learningRate The learning rate to use for the Adam gradient
     * descent algorithm.
     * @param beta1 The exponential decay rate for the 1st moment estimates.
     * @param beta2 The exponential decay rate for the 2nd moment estimates.
     * @param epsilon A small constant for numerical stability.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static adam(learningRate?: number, beta1?: number, beta2?: number, epsilon?: number): AdamOptimizer;
    /**
     * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.
     * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)
     *
     * @param learningRate The learning rate to use for the Adadelta gradient
     * descent algorithm.
     * @param rho The learning rate decay over each update.
     * @param epsilon A constant epsilon used to better condition the grad
     * update.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static adadelta(learningRate?: number, rho?: number, epsilon?: number): AdadeltaOptimizer;
    /**
     * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.
     * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)
     *
     * @param learningRate The learning rate to use for the Adamax gradient
     * descent algorithm.
     * @param beta1 The exponential decay rate for the 1st moment estimates.
     * @param beta2 The exponential decay rate for the 2nd moment estimates.
     * @param epsilon A small constant for numerical stability.
     * @param decay The learning rate decay over each update.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static adamax(learningRate?: number, beta1?: number, beta2?: number, epsilon?: number, decay?: number): AdamaxOptimizer;
    /**
     * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.
     * See
     * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](
     * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
     * or
     * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](
     * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)
     *
     * @param learningRate The learning rate to use for the Adagrad gradient
     * descent algorithm.
     * @param initialAccumulatorValue Starting value for the accumulators, must be
     * positive.
     *
     * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}
     */
    static adagrad(learningRate: number, initialAccumulatorValue?: number): AdagradOptimizer;
}