DeepAutoencoder.h

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/*
 * Copyright (c) 2014, Shogun Toolbox Foundation
 * 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.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from this
 * software without specific prior written permission.

 * 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 HOLDER OR CONTRIBUTORS BE
 * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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 *
 * Written (W) 2014 Khaled Nasr
 */

#ifndef __DEEPAUTOENCODER_H__
#define __DEEPAUTOENCODER_H__

#include <shogun/lib/common.h>
#include <shogun/neuralnets/Autoencoder.h>

namespace shogun
{
template <class T> class CDenseFeatures;

class CDeepAutoencoder : public CAutoencoder
{
public:
    CDeepAutoencoder();

    CDeepAutoencoder(CDynamicObjectArray* layers, float64_t sigma = 0.01);

    virtual ~CDeepAutoencoder() {}

    virtual void pre_train(CFeatures* data);

    virtual CDenseFeatures<float64_t>* transform(
        CDenseFeatures<float64_t>* data);

    virtual CDenseFeatures<float64_t>* reconstruct(
        CDenseFeatures<float64_t>* data);

    virtual CNeuralNetwork* convert_to_neural_network(
        CNeuralLayer* output_layer=NULL, float64_t sigma = 0.01);

    virtual void set_contraction_coefficient(float64_t coeff);

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

protected:
    virtual float64_t compute_error(SGMatrix<float64_t> targets);

private:
    void init();

    template<class T>
    SGVector<T> get_section(SGVector<T> v, int32_t i);

public:
    SGVector<int32_t> pt_noise_type;

    SGVector<float64_t> pt_noise_parameter;

    SGVector<float64_t> pt_contraction_coefficient;

    SGVector<int32_t> pt_optimization_method;

    SGVector<float64_t> pt_l2_coefficient;

    SGVector<float64_t> pt_l1_coefficient;

    SGVector<float64_t> pt_epsilon;

    SGVector<int32_t> pt_max_num_epochs;

    SGVector<int32_t> pt_gd_mini_batch_size;

    SGVector<float64_t> pt_gd_learning_rate;

    SGVector<float64_t> pt_gd_learning_rate_decay;

    SGVector<float64_t> pt_gd_momentum;

    SGVector<float64_t> pt_gd_error_damping_coeff;

protected:
    float64_t m_sigma;
};
}
#endif