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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: itkRBFBackPropagationLearningFunction.txx
Language: C++
Date: $Date$
Version: $Revision$
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 __itkRBFBackPropagationLearningFunction_txx
#define __itkRBFBackPropagationLearningFunction_txx
#include "itkRBFBackPropagationLearningFunction.h"
namespace itk
{
namespace Statistics
{
template<class LayerType, class TTargetVector>
RBFBackPropagationLearningFunction<LayerType,TTargetVector>
::RBFBackPropagationLearningFunction()
{
m_LearningRate1 = 0.05;
m_LearningRate2 = 3;
m_LearningRate3 = 0.75;
}
template<class LayerType, class TTargetVector>
void
RBFBackPropagationLearningFunction<LayerType,TTargetVector>
::Learn(LayerType* layer,ValueType lr)
{
typename LayerType::WeightSetType::Pointer outputweightset;
typename LayerType::WeightSetType::Pointer inputweightset;
outputweightset = layer->GetOutputWeightSet();
inputweightset = layer->GetInputWeightSet();
typedef typename LayerType::InputVectorType InputVectorType;
typedef typename LayerType::OutputVectorType OutputVectorType;
typedef RBFLayer<InputVectorType,OutputVectorType> RbfLayerType;
typedef typename RbfLayerType::InternalVectorType ArrayType;
typename LayerType::ValuePointer currentdeltavalues = inputweightset->GetTotalDeltaValues();
vnl_matrix<ValueType> DW_temp(currentdeltavalues,inputweightset->GetNumberOfOutputNodes(),
inputweightset->GetNumberOfInputNodes());
typename LayerType::ValuePointer DBValues = inputweightset->GetDeltaBValues();
vnl_vector<ValueType> DB;
DB.set_size(inputweightset->GetNumberOfOutputNodes());
DB.fill(0);
DB.copy_in(DBValues);
if(layer->GetLayerTypeCode() == LayerInterfaceType::OUTPUTLAYER) //If output layer do back propagation
{
DW_temp *= lr;
inputweightset->SetDWValues(DW_temp.data_block());
DB *= lr;
inputweightset->SetDBValues(DB.data_block());
}
else //else update centers, widths using gradient descent
{
DW_temp *= m_LearningRate2;
DB *= m_LearningRate3;
inputweightset->SetDWValues(DW_temp.data_block());
inputweightset->SetDBValues(DB.data_block());
}
}
template<class LayerType, class TTargetVector>
void
RBFBackPropagationLearningFunction<LayerType,TTargetVector>
::Learn(LayerType* itkNotUsed(layer), TTargetVector itkNotUsed(errors), ValueType itkNotUsed(lr))
{
}
/** Print the object */
template<class LayerType, class TTargetVector>
void
RBFBackPropagationLearningFunction<LayerType,TTargetVector>
::PrintSelf( std::ostream& os, Indent indent ) const
{
os << indent << "RBFBackPropagationLearningFunction(" << this << ")" << std::endl;
os << indent << "m_LearningRate1 = " << m_LearningRate1 << std::endl;
os << indent << "m_LearningRate2 = " << m_LearningRate2 << std::endl;
os << indent << "m_LearningRate3 = " << m_LearningRate3 << std::endl;
os << indent << "m_OutputErrors = " << m_OutputErrors << std::endl;
Superclass::PrintSelf( os, indent );
}
} // end namespace Statistics
} // end namespace itk
#endif
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