NeuralSoftmaxLayer.cpp

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/*
 * Copyright (c) 2014, Shogun Toolbox Foundation
 * All rights reserved.
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 * modification, are permitted provided that the following conditions are met:

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 * 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.
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 * 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"
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 * Written (W) 2014 Khaled Nasr
 */

#include <shogun/neuralnets/NeuralSoftmaxLayer.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/SGVector.h>

using namespace shogun;

CNeuralSoftmaxLayer::CNeuralSoftmaxLayer() : CNeuralLinearLayer()
{
}

CNeuralSoftmaxLayer::CNeuralSoftmaxLayer(int32_t num_neurons):
CNeuralLinearLayer(num_neurons)
{
}

void CNeuralSoftmaxLayer::compute_activations(SGVector<float64_t> parameters,
        CDynamicObjectArray* layers)
{
    CNeuralLinearLayer::compute_activations(parameters, layers);

    // to avoid exponentiating large numbers, the maximum activation is
    // subtracted from all the activations and the computations are done in the
    // log domain

    float64_t max = m_activations.max_single();

    for (int32_t j=0; j<m_batch_size; j++)
    {
        float64_t sum = 0;
        for (int32_t i=0; i<m_num_neurons; i++)
        {
            sum += CMath::exp(m_activations[i+j*m_num_neurons]-max);
        }
        float64_t normalizer = CMath::log(sum);
        for (int32_t k=0; k<m_num_neurons; k++)
        {
            m_activations[k+j*m_num_neurons] =
                CMath::exp(m_activations[k+j*m_num_neurons]-max-normalizer);
        }
    }
}

void CNeuralSoftmaxLayer::compute_local_gradients(SGMatrix<float64_t> targets)
{
    if (targets.num_rows == 0)
        SG_ERROR("Cannot be used as a hidden layer\n");

    int32_t len = m_num_neurons*m_batch_size;
    for (int32_t i=0; i< len; i++)
    {
        m_local_gradients[i] = (m_activations[i]-targets[i])/m_batch_size;
    }
}

float64_t CNeuralSoftmaxLayer::compute_error(SGMatrix<float64_t> targets)
{
    int32_t len = m_num_neurons*m_batch_size;
    float64_t sum = 0;
    for (int32_t i=0; i< len; i++)
    {
        // to prevent taking the log of a zero
        if (m_activations[i]==0)
            sum += targets[i]*CMath::log(1e-50);
        else
            sum += targets[i]*CMath::log(m_activations[i]);
    }
    return -1*sum/m_batch_size;
}