itkmultilayerneuralnetworkbase.txx

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/*=========================================================================

Program:   Insight Segmentation & Registration Toolkit
Module:    $RCSfile: itkMultilayerNeuralNetworkBase.txx,v $
Language:  C++
Date:      $Date: 2007-08-30 18:23:05 $
Version:   $Revision: 1.11 $

Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE.  See the above copyright notices for more information.

=========================================================================*/

#ifndef __MultiLayerNeuralNetworkBase_txx
#define __MultiLayerNeuralNetworkBase_txx

#include "itkMultilayerNeuralNetworkBase.h"
#include "itkErrorBackPropagationLearningFunctionBase.h"
#include "itkErrorBackPropagationLearningWithMomentum.h"
#include "itkQuickPropLearningRule.h"

namespace itk
{
  namespace Statistics
    {
    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::MultilayerNeuralNetworkBase()
        {
        typedef ErrorBackPropagationLearningWithMomentum<TLearningLayer,TTargetVector> DefaultLearningFunctionType;
        m_LearningFunction = DefaultLearningFunctionType::New();
        m_LearningRate = 0.001;
//#define __USE_OLD_INTERFACE  Comment out to ensure that new interface works
#ifdef __USE_OLD_INTERFACE
        m_NumOfLayers = 0;
        m_NumOfWeightSets=0;
#endif
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::SetLearningRate(ValueType lr)
        {
        m_LearningRate=lr;
        this->Modified();
        }
    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::SetLearningFunction(LearningFunctionInterfaceType* f)
        {
        m_LearningFunction=f;
        this->Modified();
        }


    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::~MultilayerNeuralNetworkBase()
        {
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::AddLayer(LayerInterfaceType* layer)
        {
        //Automatically set the layer Id based on position in the layer vector.
        layer->SetLayerId(m_Layers.size());
        m_Layers.push_back(layer);
//#define __USE_OLD_INTERFACE  Comment out to ensure that new interface works
#ifdef __USE_OLD_INTERFACE
        m_NumOfLayers++;
#endif
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      typename MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>::LayerInterfaceType*
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::GetLayer(int layer_id)
        {
        return m_Layers[layer_id].GetPointer();
        }


    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      const typename MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>::LayerInterfaceType*
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::GetLayer(int layer_id) const
        {
        return m_Layers[layer_id].GetPointer();
        }



    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      typename MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>::NetworkOutputType
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::GenerateOutput(TMeasurementVector samplevector)
        {
        this->m_Layers[0]->ForwardPropagate(samplevector);
        unsigned int i;
        for (i = 0; i < this->m_Layers.size() && i < this->m_Weights.size(); i++)
          {
          this->m_Weights[i]->ForwardPropagate(
            this->m_Layers[i]->GetOutputVector() );

          this->m_Layers[i + 1]->ForwardPropagate();
          }
        NetworkOutputType temp_output;
        temp_output.SetSize(this->m_Layers[i]->GetNumberOfNodes());
        for(unsigned int k=0; k<temp_output.Size(); k++)
          {
          temp_output[k]=this->m_Layers[i]->GetOutputVector()[k];
          }
        return temp_output;
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::BackwardPropagate(NetworkOutputType errors)
        {
        unsigned int i = this->m_Layers.size();
        i--;
        this->m_Layers[i]->BackwardPropagate(errors);
        i--;
        while (i > 0)
          {
          this->m_Layers[i]->BackwardPropagate();
          i--;
          }
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::InitializeWeights()
        {
        unsigned int num_wts = this->m_Weights.size();
        for(unsigned int i=0; i<num_wts; i++)
          {
          this->m_Weights[i]->InitializeWeights();
          }
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::UpdateWeights(ValueType itkNotUsed(lr))
        {
        unsigned int i = this->m_Layers.size();
        while(i>1)
          {
          i--;
          m_LearningFunction->Learn(this->m_Layers[i],m_LearningRate);
          this->m_Layers[i]->GetInputWeightSet()->UpdateWeights(m_LearningRate);
          }
        }

    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::AddWeightSet(typename LayerInterfaceType::WeightSetInterfaceType* weightset)
        {
        weightset->SetWeightSetId(m_Weights.size());
        m_Weights.push_back(weightset);
//#define __USE_OLD_INTERFACE  Comment out to ensure that new interface works
#ifdef __USE_OLD_INTERFACE
        m_NumOfWeightSets++;
#endif
        }

#ifdef __USE_OLD_INTERFACE
//Moved definition to header in attempt to fix compiler issues on MS Express 5.0 compiler.
    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      typename MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>::LayerInterfaceType::WeightSetInterfaceType*
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::GetWeightSet(unsigned int id)
        {
        return m_Weights[id].GetPointer();
        }
#endif

#ifdef __USE_OLD_INTERFACE
    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      const typename MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>::LayerInterfaceType::WeightSetInterfaceType*
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::GetWeightSet(unsigned int id) const
        {
        return m_Weights[id].GetPointer();
        }
#endif


    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::SetLearningRule(LearningFunctionInterfaceType* l)
        {
        m_LearningFunction = l;
        this->Modified();
        }

    /** Print the object */
    template<class TMeasurementVector, class TTargetVector,class TLearningLayer>
      void
      MultilayerNeuralNetworkBase<TMeasurementVector,TTargetVector,TLearningLayer>
      ::PrintSelf( std::ostream& os, Indent indent ) const
        {
        os << indent << "MultilayerNeuralNetworkBase(" << this << ")" << std::endl;
        Superclass::PrintSelf( os, indent );
        //os << indent << "m_Layers = " << m_Layers << std::endl;
        //os << indent << "m_Weights = " << m_Weights << std::endl;
        if(m_LearningFunction)
          {
          os << indent << "m_LearningFunction = " << m_LearningFunction << std::endl;
          }
        os << indent << "m_LearningRate = " << m_LearningRate << std::endl;
        os << indent << "NumOfLayers = " << m_Layers.size() << std::endl;
        os << indent << "NumOfWeightSets = " << m_Weights.size() << std::endl;
        }

    } // end namespace Statistics
} // end namespace itk

#endif