AdamUpdater.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2015 Wu Lin
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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 */

#ifndef AdamUpdater_H
#define AdamUpdater_H
#include <shogun/optimization/DescendUpdaterWithCorrection.h>
namespace shogun
{
class AdamUpdater: public DescendUpdaterWithCorrection
{
public:
    /* Constructor */
    AdamUpdater();

    AdamUpdater(float64_t learning_rate,float64_t epsilon,
        float64_t first_moment_decay_factor,
        float64_t second_moment_decay_factor);


    /* Destructor */
    virtual ~AdamUpdater();

    virtual void set_learning_rate(float64_t learning_rate);

    virtual void set_epsilon(float64_t epsilon);

    virtual void set_first_moment_decay_factor(float64_t decay_factor);


    virtual void set_second_moment_decay_factor(float64_t decay_factor);

    virtual const char* get_name() const { return "AdamUpdater"; }

    virtual void update_variable(SGVector<float64_t> variable_reference,
        SGVector<float64_t> raw_negative_descend_direction, float64_t learning_rate);

protected:
    virtual float64_t get_negative_descend_direction(float64_t variable,
        float64_t gradient, index_t idx, float64_t learning_rate);

    /* learning_rate at iteration */
    float64_t m_log_learning_rate;

    /* epsilon */
    float64_t m_epsilon;

    /* counter of iteration */
    int64_t m_iteration_counter;

    /* decay_factor in first moment */
    float64_t m_decay_factor_first_moment;

    /* decay_factor in second moment */
    float64_t m_decay_factor_second_moment;

    /* weighted factor in logarithmic domain */
    float64_t m_log_scale_pre_iteration;

    /* first moment of gradient */
    SGVector<float64_t> m_gradient_first_moment;

    /* second moment of gradient */
    SGVector<float64_t> m_gradient_second_moment;
private:
    /*  Init */
    void init();
};

}
#endif