NeuralLogisticLayer.cpp

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/*
 * Copyright (c) 2014, Shogun Toolbox Foundation
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 * 2. Redistributions in binary form must reproduce the above copyright notice,
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 * and/or other materials provided with the distribution.
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 * 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/NeuralLogisticLayer.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/SGVector.h>

using namespace shogun;

CNeuralLogisticLayer::CNeuralLogisticLayer() : CNeuralLinearLayer()
{
}

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

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

    // apply logistic activation function
    int32_t length = m_num_neurons*m_batch_size;
    for (int32_t i=0; i<length; i++)
        m_activations[i] = 1.0/(1.0+CMath::exp(-1.0*m_activations[i]));
}

float64_t CNeuralLogisticLayer::compute_contraction_term(
    SGVector< float64_t > parameters)
{
    int32_t num_inputs = SGVector<int32_t>::sum(m_input_sizes.vector, m_input_sizes.vlen);

    SGMatrix<float64_t> W(parameters.vector+m_num_neurons,
        m_num_neurons, num_inputs, false);

    float64_t contraction_term = 0;
    for (int32_t i=0; i<m_num_neurons; i++)
    {
        float64_t sum_j = 0;
        for (int32_t j=0; j<num_inputs; j++)
            sum_j += W(i,j)*W(i,j);

        for (int32_t k=0; k<m_batch_size; k++)
        {
            float64_t h_ = m_activations(i,k)*(1-m_activations(i,k));
            contraction_term += h_*h_*sum_j;
        }
    }

    return (contraction_coefficient/m_batch_size) * contraction_term;
}

void CNeuralLogisticLayer::compute_contraction_term_gradients(
    SGVector< float64_t > parameters, SGVector< float64_t > gradients)
{
    int32_t num_inputs = SGVector<int32_t>::sum(m_input_sizes.vector, m_input_sizes.vlen);

    SGMatrix<float64_t> W(parameters.vector+m_num_neurons,
        m_num_neurons, num_inputs, false);
    SGMatrix<float64_t> WG(gradients.vector+m_num_neurons,
        m_num_neurons, num_inputs, false);

    for (int32_t k = 0; k<m_batch_size; k++)
    {
        for (int32_t i=0; i<m_num_neurons; i++)
        {
            for (int32_t j=0; j<num_inputs; j++)
            {
                float64_t h = m_activations(i,k);
                float64_t w = W(i,j);
                float64_t h_ = w*h*(1-h);

                float64_t g = 2*w*(h-1)*h*(h*(2*w*h_-1)-w*h_+h*h);

                WG(i,j) += (contraction_coefficient/m_batch_size)*g;
            }
        }
    }
}


void CNeuralLogisticLayer::compute_local_gradients(SGMatrix<float64_t> targets)
{
    CNeuralLinearLayer::compute_local_gradients(targets);

    // multiply by the derivative of the logistic function
    int32_t length = m_num_neurons*m_batch_size;
    for (int32_t i=0; i<length; i++)
        m_local_gradients[i] *= m_activations[i] * (1.0-m_activations[i]);
}