NeuralNetwork.cpp

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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
 * 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
 */

#include <shogun/neuralnets/NeuralNetwork.h>
#include <shogun/mathematics/Math.h>
#include <shogun/optimization/lbfgs/lbfgs.h>
#include <shogun/features/DenseFeatures.h>
#include <shogun/lib/DynamicObjectArray.h>
#include <shogun/neuralnets/NeuralLayer.h>

using namespace shogun;

CNeuralNetwork::CNeuralNetwork()
: CMachine()
{
    init();
}

CNeuralNetwork::CNeuralNetwork(CDynamicObjectArray* layers)
{
    init();
    set_layers(layers);
}

void CNeuralNetwork::set_layers(CDynamicObjectArray* layers)
{
    REQUIRE(layers, "Layers should not be NULL")

    SG_UNREF(m_layers);
    SG_REF(layers);
    m_layers = layers;

    m_num_layers = m_layers->get_num_elements();
    m_adj_matrix = SGMatrix<bool>(m_num_layers, m_num_layers);
    m_adj_matrix.zero();

    m_num_inputs = 0;
    for (int32_t i=0; i<m_num_layers; i++)
    {
        if (get_layer(i)->is_input())
            m_num_inputs += get_layer(i)->get_num_neurons();
    }
}

void CNeuralNetwork::connect(int32_t i, int32_t j)
{
    REQUIRE("i<j", "i(%i) must be less that j(%i)\n", i, j);
    m_adj_matrix(i,j) = true;
}

void CNeuralNetwork::quick_connect()
{
    m_adj_matrix.zero();
    for (int32_t i=1; i<m_num_layers; i++)
        m_adj_matrix(i-1, i) = true;
}

void CNeuralNetwork::disconnect(int32_t i, int32_t j)
{
    m_adj_matrix(i,j) = false;
}

void CNeuralNetwork::disconnect_all()
{
    m_adj_matrix.zero();
}

void CNeuralNetwork::initialize_neural_network(float64_t sigma)
{
    for (int32_t j=0; j<m_num_layers; j++)
    {
        if (!get_layer(j)->is_input())
        {
            int32_t num_inputs = 0;
            for (int32_t i=0; i<m_num_layers; i++)
                num_inputs += m_adj_matrix(i,j);

            SGVector<int32_t> input_indices(num_inputs);

            int32_t k = 0;
            for (int i=0; i<m_num_layers; i++)
            {
                if (m_adj_matrix(i,j))
                {
                    input_indices[k] = i;
                    k++;
                }
            }

            get_layer(j)->initialize_neural_layer(m_layers, input_indices);
        }
    }

    m_index_offsets = SGVector<int32_t>(m_num_layers);

    m_total_num_parameters = get_layer(0)->get_num_parameters();
    m_index_offsets[0] = 0;
    for (int32_t i=1; i<m_num_layers; i++)
    {
        m_index_offsets[i] = m_total_num_parameters;
        m_total_num_parameters += get_layer(i)->get_num_parameters();
    }

    m_params = SGVector<float64_t>(m_total_num_parameters);
    m_param_regularizable = SGVector<bool>(m_total_num_parameters);

    m_params.zero();
    m_param_regularizable.set_const(true);

    for (int32_t i=0; i<m_num_layers; i++)
    {
        SGVector<float64_t> layer_param = get_section(m_params, i);
        SGVector<bool> layer_param_regularizable =
            get_section(m_param_regularizable, i);

        get_layer(i)->initialize_parameters(layer_param,
            layer_param_regularizable, sigma);

        get_layer(i)->set_batch_size(m_batch_size);
    }
}

CNeuralNetwork::~CNeuralNetwork()
{
    SG_UNREF(m_layers);
}

CBinaryLabels* CNeuralNetwork::apply_binary(CFeatures* data)
{
    SGMatrix<float64_t> output_activations = forward_propagate(data);
    CBinaryLabels* labels = new CBinaryLabels(m_batch_size);

    for (int32_t i=0; i<m_batch_size; i++)
    {
        if (get_num_outputs()==1)
        {
            if (output_activations[i]>0.5) labels->set_label(i, 1);
            else labels->set_label(i, -1);

            labels->set_value(output_activations[i], i);
        }
        else if (get_num_outputs()==2)
        {
            float64_t v1 = output_activations[2*i];
            float64_t v2 = output_activations[2*i+1];
            if (v1>v2)
                labels->set_label(i, 1);
            else labels->set_label(i, -1);

            labels->set_value(v2/(v1+v2), i);
        }
    }

    return labels;
}

CRegressionLabels* CNeuralNetwork::apply_regression(CFeatures* data)
{
    SGMatrix<float64_t> output_activations = forward_propagate(data);
    SGVector<float64_t> labels_vec(m_batch_size);

    for (int32_t i=0; i<m_batch_size; i++)
            labels_vec[i] = output_activations[i];

    return new CRegressionLabels(labels_vec);
}


CMulticlassLabels* CNeuralNetwork::apply_multiclass(CFeatures* data)
{
    SGMatrix<float64_t> output_activations = forward_propagate(data);
    SGVector<float64_t> labels_vec(m_batch_size);

    for (int32_t i=0; i<m_batch_size; i++)
    {
        labels_vec[i] = CMath::arg_max(
            output_activations.matrix+i*get_num_outputs(), 1, get_num_outputs());
    }

    CMulticlassLabels* labels = new CMulticlassLabels(labels_vec);

    labels->allocate_confidences_for(get_num_outputs());
    for (int32_t i=0; i<m_batch_size; i++)
    {
        labels->set_multiclass_confidences(i, SGVector<float64_t>(
            output_activations.matrix, get_num_outputs(), i*get_num_outputs()));
    }

    return labels;
}

CDenseFeatures< float64_t >* CNeuralNetwork::transform(
    CDenseFeatures< float64_t >* data)
{
    SGMatrix<float64_t> output_activations = forward_propagate(data);
    return new CDenseFeatures<float64_t>(output_activations);
}

bool CNeuralNetwork::train_machine(CFeatures* data)
{
    REQUIRE(m_max_num_epochs>=0,
        "Maximum number of epochs (%i) must be >= 0\n", m_max_num_epochs);

    SGMatrix<float64_t> inputs = features_to_matrix(data);
    SGMatrix<float64_t> targets = labels_to_matrix(m_labels);

    for (int32_t i=0; i<m_num_layers-1; i++)
    {
        get_layer(i)->dropout_prop =
            get_layer(i)->is_input() ? m_dropout_input : m_dropout_hidden;
    }
    get_layer(m_num_layers-1)->dropout_prop = 0.0;

    m_is_training = true;
    for (int32_t i=0; i<m_num_layers; i++)
        get_layer(i)->is_training = true;

    bool result = false;
    if (m_optimization_method==NNOM_GRADIENT_DESCENT)
        result = train_gradient_descent(inputs, targets);
    else if (m_optimization_method==NNOM_LBFGS)
        result = train_lbfgs(inputs, targets);

    for (int32_t i=0; i<m_num_layers; i++)
        get_layer(i)->is_training = false;
    m_is_training = false;

    return result;
}

bool CNeuralNetwork::train_gradient_descent(SGMatrix<float64_t> inputs,
        SGMatrix<float64_t> targets)
{
    REQUIRE(m_gd_learning_rate>0,
        "Gradient descent learning rate (%f) must be > 0\n", m_gd_learning_rate);
    REQUIRE(m_gd_momentum>=0,
        "Gradient descent momentum (%f) must be >= 0\n", m_gd_momentum);

    int32_t training_set_size = inputs.num_cols;
    if (m_gd_mini_batch_size==0) m_gd_mini_batch_size = training_set_size;
    set_batch_size(m_gd_mini_batch_size);

    int32_t n_param = get_num_parameters();
    SGVector<float64_t> gradients(n_param);

    // needed for momentum
    SGVector<float64_t> param_updates(n_param);
    param_updates.zero();

    float64_t error_last_time = -1.0, error = -1.0;

    float64_t c = m_gd_error_damping_coeff;
    if (c==-1.0)
        c = 0.99*(float64_t)m_gd_mini_batch_size/training_set_size + 1e-2;

    bool continue_training = true;
    float64_t alpha = m_gd_learning_rate;

    for (int32_t i=0; continue_training; i++)
    {
        if (m_max_num_epochs!=0)
            if (i>=m_max_num_epochs) break;

        for (int32_t j=0; j < training_set_size; j += m_gd_mini_batch_size)
        {
            alpha = m_gd_learning_rate_decay*alpha;

            if (j+m_gd_mini_batch_size>training_set_size)
                j = training_set_size-m_gd_mini_batch_size;

            SGMatrix<float64_t> targets_batch(targets.matrix+j*get_num_outputs(),
                get_num_outputs(), m_gd_mini_batch_size, false);

            SGMatrix<float64_t> inputs_batch(inputs.matrix+j*m_num_inputs,
                m_num_inputs, m_gd_mini_batch_size, false);

            for (int32_t k=0; k<n_param; k++)
                m_params[k] += m_gd_momentum*param_updates[k];

            float64_t e = compute_gradients(inputs_batch, targets_batch, gradients);


            for (int32_t k=0; k<m_num_layers; k++)
            {
                SGVector<float64_t> layer_gradients = get_section(gradients, k);
                if (layer_gradients.vlen > 0)
                {
                    SG_INFO("Layer %i (%s), Max Gradient: %g, Mean Gradient: %g.\n", k,get_layer(k)->get_name(),
                        CMath::max(layer_gradients.vector, layer_gradients.vlen),
                        SGVector<float64_t>::sum(layer_gradients.vector, layer_gradients.vlen)/layer_gradients.vlen);
                }
            }

            // filter the errors
            if (error==-1.0)
                error = e;
            else
                error = (1.0-c) * error + c*e;

            for (int32_t k=0; k<n_param; k++)
            {
                param_updates[k] = m_gd_momentum*param_updates[k]
                        -alpha*gradients[k];

                m_params[k] -= alpha*gradients[k];
            }

            if (error_last_time!=-1.0)
            {
                float64_t error_change = (error_last_time-error)/error;
                if (error_change< m_epsilon && error_change>=0)
                {
                    SG_INFO("Gradient Descent Optimization Converged\n");
                    continue_training = false;
                    break;
                }

                SG_INFO("Epoch %i: Error = %f\n",i, error);
            }
            error_last_time = error;
        }
    }

    return true;
}

bool CNeuralNetwork::train_lbfgs(SGMatrix<float64_t> inputs,
        const SGMatrix<float64_t> targets)
{
    int32_t training_set_size = inputs.num_cols;
    set_batch_size(training_set_size);

    lbfgs_parameter_t lbfgs_param;
    lbfgs_parameter_init(&lbfgs_param);
    lbfgs_param.max_iterations = m_max_num_epochs;
    lbfgs_param.epsilon = 0;
    lbfgs_param.past = 1;
    lbfgs_param.delta = m_epsilon;

    m_lbfgs_temp_inputs = &inputs;
    m_lbfgs_temp_targets = &targets;

    int32_t result = lbfgs(m_total_num_parameters,
            m_params,
            NULL,
            &CNeuralNetwork::lbfgs_evaluate,
            &CNeuralNetwork::lbfgs_progress,
            this,
            &lbfgs_param);

    m_lbfgs_temp_inputs = NULL;
    m_lbfgs_temp_targets = NULL;

    if (result==LBFGS_SUCCESS || 1)
    {
        SG_INFO("L-BFGS Optimization Converged\n");
    }
    else if (result==LBFGSERR_MAXIMUMITERATION)
    {
        SG_INFO("L-BFGS Max Number of Epochs reached\n");
    }
    else
    {
        SG_INFO("L-BFGS optimization ended with return code %i\n",result);
    }
    return true;
}

float64_t CNeuralNetwork::lbfgs_evaluate(void* userdata,
        const float64_t* W,
        float64_t* grad,
        const int32_t n,
        const float64_t step)
{
    CNeuralNetwork* network = static_cast<CNeuralNetwork*>(userdata);

    SGVector<float64_t> grad_vector(grad, network->get_num_parameters(), false);

    return network->compute_gradients(*network->m_lbfgs_temp_inputs,
        *network->m_lbfgs_temp_targets, grad_vector);
}

int CNeuralNetwork::lbfgs_progress(void* instance,
        const float64_t* x,
        const float64_t* g,
        const float64_t fx,
        const float64_t xnorm,
        const float64_t gnorm,
        const float64_t step,
        int n, int k, int ls)
{
    SG_SINFO("Epoch %i: Error = %f\n",k, fx);

    CNeuralNetwork* network = static_cast<CNeuralNetwork*>(instance);
    SGVector<float64_t> gradients((float64_t*)g, network->get_num_parameters(), false);
    for (int32_t i=0; i<network->m_num_layers; i++)
    {
        SGVector<float64_t> layer_gradients = network->get_section(gradients, i);
        if (layer_gradients.vlen > 0)
        {
            SG_SINFO("Layer %i (%s), Max Gradient: %g, Mean Gradient: %g.\n", i, network->get_layer(i)->get_name(),
                CMath::max(layer_gradients.vector, layer_gradients.vlen),
                SGVector<float64_t>::sum(layer_gradients.vector, layer_gradients.vlen)/layer_gradients.vlen);
        }
    }
    return 0;
}

SGMatrix<float64_t> CNeuralNetwork::forward_propagate(CFeatures* data, int32_t j)
{
    SGMatrix<float64_t> inputs = features_to_matrix(data);
    set_batch_size(data->get_num_vectors());
    return forward_propagate(inputs, j);
}

SGMatrix<float64_t> CNeuralNetwork::forward_propagate(
    SGMatrix<float64_t> inputs, int32_t j)
{
    if (j==-1)
        j = m_num_layers-1;

    for (int32_t i=0; i<=j; i++)
    {
        CNeuralLayer* layer = get_layer(i);

        if (layer->is_input())
            layer->compute_activations(inputs);
        else
            layer->compute_activations(get_section(m_params, i), m_layers);

        layer->dropout_activations();
    }

    return get_layer(j)->get_activations();
}

float64_t CNeuralNetwork::compute_gradients(SGMatrix<float64_t> inputs,
        SGMatrix<float64_t> targets, SGVector<float64_t> gradients)
{
    forward_propagate(inputs);

    for (int32_t i=0; i<m_num_layers; i++)
        get_layer(i)->get_activation_gradients().zero();

    for (int32_t i=m_num_layers-1; i>=0; i--)
    {
        if (i==m_num_layers-1)
            get_layer(i)->compute_gradients(get_section(m_params,i), targets,
                m_layers, get_section(gradients,i));
        else
            get_layer(i)->compute_gradients(get_section(m_params,i),
                SGMatrix<float64_t>(), m_layers, get_section(gradients,i));
    }

    // L2 regularization
    if (m_l2_coefficient != 0.0)
    {
        for (int32_t i=0; i<m_total_num_parameters; i++)
        {
            if (m_param_regularizable[i])
                gradients[i] += m_l2_coefficient*m_params[i];
        }
    }

    // L1 regularization
    if (m_l1_coefficient != 0.0)
    {
        for (int32_t i=0; i<m_total_num_parameters; i++)
        {
            if (m_param_regularizable[i])
                gradients[i] +=
                    m_l1_coefficient*CMath::sign<float64_t>(m_params[i]);
        }
    }

    // max-norm regularization
    if (m_max_norm != -1.0)
    {
        for (int32_t i=0; i<m_num_layers; i++)
        {
            SGVector<float64_t> layer_params = get_section(m_params,i);
            get_layer(i)->enforce_max_norm(layer_params, m_max_norm);
        }
    }

    return compute_error(targets);
}

float64_t CNeuralNetwork::compute_error(SGMatrix<float64_t> targets)
{
    float64_t error = get_layer(m_num_layers-1)->compute_error(targets);

    // L2 regularization
    if (m_l2_coefficient != 0.0)
    {
        for (int32_t i=0; i<m_total_num_parameters; i++)
        {
            if (m_param_regularizable[i])
                error += 0.5*m_l2_coefficient*m_params[i]*m_params[i];
        }
    }

    // L1 regularization
    if (m_l1_coefficient != 0.0)
    {
        for (int32_t i=0; i<m_total_num_parameters; i++)
        {
            if (m_param_regularizable[i])
                error += m_l1_coefficient*CMath::abs(m_params[i]);
        }
    }

    return error;
}

float64_t CNeuralNetwork::compute_error(SGMatrix<float64_t> inputs,
        SGMatrix<float64_t> targets)
{
    forward_propagate(inputs);
    return compute_error(targets);
}


float64_t CNeuralNetwork::check_gradients(float64_t approx_epsilon, float64_t s)
{
    // some random inputs and ouputs
    SGMatrix<float64_t> x(m_num_inputs,1);
    SGMatrix<float64_t> y(get_num_outputs(),1);

    for (int32_t i=0; i<x.num_rows; i++)
        x[i] = CMath::random(0.0,1.0);

    // the outputs are set up in the form of a probability distribution (in case
    // that is required by the output layer, i.e softmax)
    for (int32_t i=0; i<y.num_rows; i++)
        y[i] = CMath::random(0.0,1.0);

    float64_t y_sum = SGVector<float64_t>::sum(y.matrix, y.num_rows);
    for (int32_t i=0; i<y.num_rows; i++)
        y[i] /= y_sum;

    set_batch_size(1);

    // numerically compute gradients
    SGVector<float64_t> gradients_numerical(m_total_num_parameters);

    for (int32_t i=0; i<m_total_num_parameters; i++)
    {
        float64_t c =
            CMath::max<float64_t>(CMath::abs(approx_epsilon*m_params[i]),s);

        m_params[i] += c;
        float64_t error_plus = compute_error(x,y);
        m_params[i] -= 2*c;
        float64_t error_minus = compute_error(x,y);
        m_params[i] += c;

        gradients_numerical[i] = (error_plus-error_minus)/(2*c);
    }

    // compute gradients using backpropagation
    SGVector<float64_t> gradients_backprop(m_total_num_parameters);
    compute_gradients(x, y, gradients_backprop);

    float64_t sum = 0.0;
    for (int32_t i=0; i<m_total_num_parameters; i++)
    {
        sum += CMath::abs(gradients_backprop[i]-gradients_numerical[i]);
    }

    return sum/m_total_num_parameters;
}

void CNeuralNetwork::set_batch_size(int32_t batch_size)
{
    if (batch_size!=m_batch_size)
    {
        m_batch_size = batch_size;
        for (int32_t i=0; i<m_num_layers; i++)
            get_layer(i)->set_batch_size(m_batch_size);
    }
}

SGMatrix<float64_t> CNeuralNetwork::features_to_matrix(CFeatures* features)
{
    REQUIRE(features != NULL, "Invalid (NULL) feature pointer\n");
    REQUIRE(features->get_feature_type() == F_DREAL,
        "Feature type must be F_DREAL\n");
    REQUIRE(features->get_feature_class() == C_DENSE,
        "Feature class must be C_DENSE\n");

    CDenseFeatures<float64_t>* inputs = (CDenseFeatures<float64_t>*) features;
    REQUIRE(inputs->get_num_features()==m_num_inputs,
        "Number of features (%i) must match the network's number of inputs "
        "(%i)\n", inputs->get_num_features(), get_num_inputs());

    return inputs->get_feature_matrix();
}

SGMatrix<float64_t> CNeuralNetwork::labels_to_matrix(CLabels* labs)
{
    REQUIRE(labs != NULL, "Invalid (NULL) labels pointer\n");

    SGMatrix<float64_t> targets(get_num_outputs(), labs->get_num_labels());
    targets.zero();

    if (labs->get_label_type() == LT_MULTICLASS)
    {
        CMulticlassLabels* labels_mc = (CMulticlassLabels*) labs;
        REQUIRE(labels_mc->get_num_classes()==get_num_outputs(),
            "Number of classes (%i) must match the network's number of "
            "outputs (%i)\n", labels_mc->get_num_classes(), get_num_outputs());

        for (int32_t i=0; i<labels_mc->get_num_labels(); i++)
            targets[((int32_t)labels_mc->get_label(i))+ i*get_num_outputs()]
                = 1.0;
    }
    else if (labs->get_label_type() == LT_BINARY)
    {
        CBinaryLabels* labels_bin = (CBinaryLabels*) labs;
        if (get_num_outputs()==1)
        {
            for (int32_t i=0; i<labels_bin->get_num_labels(); i++)
                targets[i] = (labels_bin->get_label(i)==1);
        }
        else if (get_num_outputs()==2)
        {
            for (int32_t i=0; i<labels_bin->get_num_labels(); i++)
            {
                targets[i*2] = (labels_bin->get_label(i)==1);
                targets[i*2+1] = (labels_bin->get_label(i)==-1);
            }
        }
    }
    else if (labs->get_label_type() == LT_REGRESSION)
    {
        CRegressionLabels* labels_reg = (CRegressionLabels*) labs;
        for (int32_t i=0; i<labels_reg->get_num_labels(); i++)
            targets[i] = labels_reg->get_label(i);
    }

    return targets;
}

EProblemType CNeuralNetwork::get_machine_problem_type() const
{
    // problem type depends on the type of labels given to the network
    // if no labels are given yet, just return PT_MULTICLASS
    if (m_labels==NULL)
        return PT_MULTICLASS;

    if (m_labels->get_label_type() == LT_BINARY)
        return PT_BINARY;
    else if (m_labels->get_label_type() == LT_REGRESSION)
        return PT_REGRESSION;
    else return PT_MULTICLASS;
}

bool CNeuralNetwork::is_label_valid(CLabels* lab) const
{
    return (lab->get_label_type() == LT_MULTICLASS ||
        lab->get_label_type() == LT_BINARY ||
        lab->get_label_type() == LT_REGRESSION);
}

void CNeuralNetwork::set_labels(CLabels* lab)
{
    if (lab->get_label_type() == LT_BINARY)
    {
        REQUIRE(get_num_outputs() <= 2, "Cannot use %s in a neural network "
            "with more that 2 output neurons\n", lab->get_name());
    }
    else if (lab->get_label_type() == LT_REGRESSION)
    {
        REQUIRE(get_num_outputs() == 1, "Cannot use %s in a neural network "
            "with more that 1 output neuron\n", lab->get_name());
    }

    CMachine::set_labels(lab);
}

SGVector<float64_t>* CNeuralNetwork::get_layer_parameters(int32_t i)
{
    REQUIRE(i<m_num_layers && i >= 0, "Layer index (%i) out of range\n", i);

    int32_t n = get_layer(i)->get_num_parameters();
    SGVector<float64_t>* p = new SGVector<float64_t>(n);

    memcpy(p->vector, get_section(m_params, i), n*sizeof(float64_t));
    return p;
}

CNeuralLayer* CNeuralNetwork::get_layer(int32_t i)
{
    CNeuralLayer* layer = (CNeuralLayer*)m_layers->element(i);
    // needed because m_layers->element(i) increases the reference count of
    // layer i
    SG_UNREF(layer);
    return layer;
}

template <class T>
SGVector<T> CNeuralNetwork::get_section(SGVector<T> v, int32_t i)
{
    return SGVector<T>(v.vector+m_index_offsets[i],
        get_layer(i)->get_num_parameters(), false);
}

int32_t CNeuralNetwork::get_num_outputs()
{
    return get_layer(m_num_layers-1)->get_num_neurons();
}

CDynamicObjectArray* CNeuralNetwork::get_layers()
{
    SG_REF(m_layers);
    return m_layers;
}

void CNeuralNetwork::init()
{
    m_optimization_method = NNOM_LBFGS;
    m_dropout_hidden = 0.0;
    m_dropout_input = 0.0;
    m_max_norm = -1.0;
    m_l2_coefficient = 0.0;
    m_l1_coefficient = 0.0;
    m_gd_mini_batch_size = 0;
    m_max_num_epochs = 0;
    m_gd_learning_rate = 0.1;
    m_gd_learning_rate_decay = 1.0;
    m_gd_momentum = 0.9;
    m_gd_error_damping_coeff = -1.0;
    m_epsilon = 1.0e-5;
    m_num_inputs = 0;
    m_num_layers = 0;
    m_layers = NULL;
    m_total_num_parameters = 0;
    m_batch_size = 1;
    m_lbfgs_temp_inputs = NULL;
    m_lbfgs_temp_targets = NULL;
    m_is_training = false;

    SG_ADD((machine_int_t*)&m_optimization_method, "optimization_method",
           "Optimization Method", MS_NOT_AVAILABLE);
    SG_ADD(&m_gd_mini_batch_size, "gd_mini_batch_size",
           "Gradient Descent Mini-batch size", MS_NOT_AVAILABLE);
    SG_ADD(&m_max_num_epochs, "max_num_epochs",
           "Max number of Epochs", MS_NOT_AVAILABLE);
    SG_ADD(&m_gd_learning_rate, "gd_learning_rate",
           "Gradient descent learning rate", MS_NOT_AVAILABLE);
    SG_ADD(&m_gd_learning_rate_decay, "gd_learning_rate_decay",
           "Gradient descent learning rate decay", MS_NOT_AVAILABLE);
    SG_ADD(&m_gd_momentum, "gd_momentum",
           "Gradient Descent Momentum", MS_NOT_AVAILABLE);
    SG_ADD(&m_gd_error_damping_coeff, "gd_error_damping_coeff",
           "Gradient Descent Error Damping Coeff", MS_NOT_AVAILABLE);
    SG_ADD(&m_epsilon, "epsilon",
           "Epsilon", MS_NOT_AVAILABLE);
    SG_ADD(&m_num_inputs, "num_inputs",
           "Number of Inputs", MS_NOT_AVAILABLE);
    SG_ADD(&m_num_layers, "num_layers",
           "Number of Layers", MS_NOT_AVAILABLE);
    SG_ADD(&m_adj_matrix, "adj_matrix",
           "Adjacency Matrix", MS_NOT_AVAILABLE);
    SG_ADD(&m_l2_coefficient, "l2_coefficient",
           "L2 regularization coeff", MS_NOT_AVAILABLE);
    SG_ADD(&m_l1_coefficient, "l1_coefficient",
           "L1 regularization coeff", MS_NOT_AVAILABLE);
    SG_ADD(&m_dropout_hidden, "dropout_hidden",
           "Hidden neuron dropout probability", MS_NOT_AVAILABLE);
    SG_ADD(&m_dropout_input, "dropout_input",
           "Input neuron dropout probability", MS_NOT_AVAILABLE);
    SG_ADD(&m_max_norm, "max_norm",
           "Max Norm", MS_NOT_AVAILABLE);
    SG_ADD(&m_total_num_parameters, "total_num_parameters",
           "Total number of parameters", MS_NOT_AVAILABLE);
    SG_ADD(&m_index_offsets, "index_offsets",
        "Index Offsets", MS_NOT_AVAILABLE);
    SG_ADD(&m_params, "params",
        "Parameters", MS_NOT_AVAILABLE);
    SG_ADD(&m_param_regularizable, "param_regularizable",
        "Parameter Regularizable", MS_NOT_AVAILABLE);
    SG_ADD((CSGObject**)&m_layers, "layers",
        "DynamicObjectArray of NeuralNetwork objects",
        MS_NOT_AVAILABLE);
    SG_ADD(&m_is_training, "is_training",
        "is_training", MS_NOT_AVAILABLE);
}