NeuralConvolutionalLayer.h

Go to the documentation of this file.

/*
 * 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
 * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 * POSSIBILITY OF SUCH DAMAGE.
 *
 * Written (W) 2014 Khaled Nasr
 */

#ifndef __NEURALCONVOLUTIONALLAYER_H__
#define __NEURALCONVOLUTIONALLAYER_H__

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

namespace shogun
{

enum EInitializationMode
{
    NORMAL=0,
    RE_NORMAL=1,
};

class CNeuralConvolutionalLayer : public CNeuralLayer
{
public:
    CNeuralConvolutionalLayer();

    CNeuralConvolutionalLayer(EConvMapActivationFunction function,
        int32_t num_maps,
        int32_t radius_x, int32_t radius_y,
        int32_t pooling_width=1, int32_t pooling_height=1,
        int32_t stride_x=1, int32_t stride_y=1,
        EInitializationMode initialization_mode=NORMAL);
    
    virtual ~CNeuralConvolutionalLayer() {}

    virtual void set_batch_size(int32_t batch_size);

    virtual void initialize_neural_layer(CDynamicObjectArray* layers,
            SGVector<int32_t> input_indices);

    virtual void initialize_parameters(SGVector<float64_t> parameters,
            SGVector<bool> parameter_regularizable,
            float64_t sigma);

    virtual void compute_activations(SGVector<float64_t> parameters,
            CDynamicObjectArray* layers);

    virtual void compute_gradients(SGVector<float64_t> parameters,
            SGMatrix<float64_t> targets,
            CDynamicObjectArray* layers,
            SGVector<float64_t> parameter_gradients);

    virtual float64_t compute_error(SGMatrix<float64_t> targets);

    virtual void enforce_max_norm(SGVector<float64_t> parameters,
            float64_t max_norm);

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

private:
    void init();

protected:
    int32_t m_num_maps;

    int32_t m_input_width;

    int32_t m_input_height;

    int32_t m_input_num_channels;

    int32_t m_radius_x;

    int32_t m_radius_y;

    int32_t m_pooling_width;

    int32_t m_pooling_height;

    int32_t m_stride_x;

    int32_t m_stride_y;

    EConvMapActivationFunction m_activation_function;

    SGMatrix<float64_t> m_convolution_output;

    SGMatrix<float64_t> m_convolution_output_gradients;

    SGMatrix<float64_t> m_max_indices;

    EInitializationMode m_initialization_mode;
};

}
#endif