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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkBatchSupervisedTrainingFunction_hxx
#define itkBatchSupervisedTrainingFunction_hxx
#include "itkBatchSupervisedTrainingFunction.h"
#include <fstream>
#include <algorithm>
namespace itk
{
namespace Statistics
{
template<typename TSample, typename TTargetVector, typename ScalarType>
BatchSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>//,f>
::BatchSupervisedTrainingFunction()
{
this->m_LearningRate = 0.1; //0.5 multilayer test 0.1 perceptron
m_Threshold = 0;
m_Stop = false; //stop condition
}
template<typename TSample, typename TTargetVector, typename ScalarType>
void BatchSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::SetNumOfIterations(SizeValueType i)
{
this->SetIterations(i);
}
template<typename TSample, typename TTargetVector, typename ScalarType>
void BatchSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::Train(typename BatchSupervisedTrainingFunction<TSample, TTargetVector, ScalarType>::NetworkType* net,
TSample* samples, TTargetVector* targets)
{
this->SetTrainingSamples(samples);
this->SetTargetValues(targets);
InternalVectorType outputvector;
InternalVectorType errorvector;
outputvector.SetSize(targets->GetMeasurementVectorSize());
errorvector.SetSize(targets->GetMeasurementVectorSize());
//std::cout<<"Target dim ="<<targets->GetMeasurementVectorSize()<<std::endl;
//typename Superclass::OutputVectorType outputvector;
typename Superclass::VectorType inputvector;
typename Superclass::OutputVectorType targetvector;
//typename Superclass::OutputVectorType errorvector;
SizeValueType num_iterations = this->GetIterations();
m_Stop = false;
SizeValueType count = 0;
while (!m_Stop)
{
for (SizeValueType i = 0; i < this->m_InputSamples.size(); i++)
{
inputvector = this->m_InputSamples[i];
targetvector = this->m_Targets[i];
outputvector=net->GenerateOutput(inputvector);
for(unsigned int k=0; k<targetvector.Size(); k++)
{
errorvector[k] = targetvector[k] - outputvector[k];
}
net->BackwardPropagate(this->m_PerformanceFunction
->EvaluateDerivative(errorvector));
}
net->UpdateWeights(this->m_LearningRate);
count++;
if (count > num_iterations)
{
m_Stop = true;
}
}
#ifdef __OLD_CODE__
if (this->m_PerformanceFunction->Evaluate(errorvector) < m_Threshold
&& count < num_iterations)
{
std::cout << "Goal Met " << std::endl;
}
else
{
std::cout << "Goal Not Met Max Iterations Reached " << std::endl;
}
std::cout << net << std::endl;
#endif
}
/** Print the object */
template<typename TSample, typename TTargetVector, typename ScalarType>
void
BatchSupervisedTrainingFunction<TSample,TTargetVector,ScalarType>
::PrintSelf( std::ostream& os, Indent indent ) const
{
os << indent << "BatchSupervisedTrainingFunction(" << this << ")" << std::endl;
os << indent << "m_Threshold = " << m_Threshold << std::endl;
os << indent << "m_Stop = " << m_Stop << std::endl;
Superclass::PrintSelf( os, indent );
}
} // end namespace Statistics
} // end namespace itk
#endif
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