Autoencoder.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
 * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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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 __AUTOENCODER_H__
#define __AUTOENCODER_H__

#include <shogun/lib/common.h>
#include <shogun/neuralnets/NeuralNetwork.h>
#include <shogun/neuralnets/NeuralLayer.h>

namespace shogun
{
template <class T> class CDenseFeatures;
class CNeuralConvolutionalLayer;

enum EAENoiseType
{
    AENT_NONE=0,

    AENT_DROPOUT=1,

    AENT_GAUSSIAN=2
};

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

    CAutoencoder(int32_t num_inputs, CNeuralLayer* hidden_layer,
        CNeuralLayer* decoding_layer=NULL, float64_t sigma = 0.01);

    CAutoencoder(int32_t input_width, int32_t input_height, int32_t input_num_channels,
        CNeuralConvolutionalLayer* hidden_layer,
        CNeuralConvolutionalLayer* decoding_layer, float64_t sigma = 0.01);

    virtual bool train(CFeatures* data);

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

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

    virtual void set_contraction_coefficient(float64_t coeff)
    {
        m_contraction_coefficient = coeff;
        get_layer(1)->contraction_coefficient = coeff;
    }

    virtual ~CAutoencoder() {}

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

    void set_noise_type(EAENoiseType noise_type)
    {
        m_noise_type = noise_type;
    }

    EAENoiseType get_noise_type()
    {
        return m_noise_type;
    }

    void set_noise_parameter(float64_t noise_parameter)
    {
        m_noise_parameter = noise_parameter;
    }

    float64_t get_noise_parameter()
    {
        return m_noise_parameter;
    }

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);

protected:
    float64_t m_contraction_coefficient;

    EAENoiseType m_noise_type;

    float64_t m_noise_parameter;
};
}
#endif