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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: NNetClassifierTest1.cxx,v $
Language: C++
Date: $Date: 2007-08-18 15:16:57 $
Version: $Revision: 1.6 $
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.
=========================================================================*/
//#define USE_REVIEW_NETIO
#ifdef USE_REVIEW_NETIO
#include "itkNeuralNetworkFileReader.h"
#include "itkNeuralNetworkFileWriter.h"
#endif
#include "itkOneHiddenLayerBackPropagationNeuralNetwork.h"
#include "itkIterativeSupervisedTrainingFunction.h"
#include "itkBatchSupervisedTrainingFunction.h"
#include "itkVector.h"
#include "itkArray.h"
#include "itkListSample.h"
#include <vector>
#include <fstream>
#define ROUND(x) (floor(x+0.5))
typedef itk::Array<double> MeasurementVectorType;
typedef itk::Array<double> TargetVectorType;
typedef itk::Statistics::ListSample<TargetVectorType> TargetType;
typedef itk::Statistics::ListSample<MeasurementVectorType> SampleType;
typedef itk::Statistics::OneHiddenLayerBackPropagationNeuralNetwork<MeasurementVectorType, TargetVectorType> OneHiddenLayerBackPropagationNeuralNetworkType;
static int TestNetwork(SampleType::Pointer TestSample, TargetType::Pointer TestTargets,
OneHiddenLayerBackPropagationNeuralNetworkType::Pointer OneHiddenLayerNetwork)
{
//Network Simulation
std::cout << TestSample->Size() << std::endl;
std::cout << "Network Simulation" << std::endl;
SampleType::ConstIterator iter1 = TestSample->Begin();
TargetType::ConstIterator iter2 = TestTargets->Begin();
unsigned int error1 = 0 ;
unsigned int error2 = 0 ;
int flag;
std::ofstream outfile;
outfile.open("out1.txt",std::ios::out);
while (iter1 != TestSample->End())
{
MeasurementVectorType mv = iter1.GetMeasurementVector();
TargetVectorType tv = iter2.GetMeasurementVector();
TargetVectorType ov = OneHiddenLayerNetwork->GenerateOutput(mv);
flag = 0;
if (fabs(tv[0]-ov[0])>0.2)
{
outfile<<fabs(tv[0]-ov[0])<<std::endl;
flag = 1;
}
if (flag == 1 && ROUND(tv[0]) == 1)
{
++error1;
}
else if (flag == 1 && ROUND(tv[0]) == -1)
{
++error2;
}
std::cout << "Network Input = " << mv << std::endl;
std::cout << "Network Output = " << ov << std::endl;
std::cout << "Target = " << tv << std::endl;
++iter1;
++iter2;
}
std::cout << "Among "<<TestSample->Size()<<" measurement vectors, " << error1 + error2
<< " vectors are misclassified." << std::endl ;
std::cout<<"Network Weights and Biases after Training= "<<std::endl;
std::cout << OneHiddenLayerNetwork << std::endl;
if (double(error1 / 10) > 2 || double(error2 / 10) > 2)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
return EXIT_SUCCESS;
}
int
NNetClassifierTest1(int argc, char* argv[])
{
if (argc < 2)
{
std::cout << "ERROR: data file name argument missing." << std::endl ;
return EXIT_FAILURE;
}
int num_train=800;
int num_test=200;
char* trainFileName = argv[1]; //"train.txt"; //argv[1];
char* testFileName = argv[2]; //"test.txt"; //argv[2];
int num_input_nodes = 2;
int num_hidden_nodes = 5;
int num_output_nodes = 1;
typedef itk::Statistics::BatchSupervisedTrainingFunction<SampleType, TargetType, double> TrainingFcnType;
MeasurementVectorType mv;
TargetVectorType tv;
TargetVectorType ov;
mv.SetSize(num_input_nodes);
ov.SetSize(num_output_nodes);
tv.SetSize(num_output_nodes);
SampleType::Pointer trainsample = SampleType::New();
SampleType::Pointer testsample = SampleType::New();
TargetType::Pointer traintargets = TargetType::New();
TargetType::Pointer testtargets = TargetType::New();
trainsample->SetMeasurementVectorSize( num_input_nodes);
traintargets->SetMeasurementVectorSize( num_output_nodes);
testsample->SetMeasurementVectorSize( num_input_nodes);
testtargets->SetMeasurementVectorSize( num_output_nodes);
std::ifstream infile1;
infile1.open(trainFileName, std::ios::in);
for (int a = 0; a < num_train; a++)
{
for (int i = 0; i < num_input_nodes; i++)
{
infile1 >> mv[i];
}
infile1 >> tv[0];
trainsample->PushBack(mv);
traintargets->PushBack(tv);
std::cout << "Input =" << mv << std::endl;
std::cout << "target =" << tv << std::endl;
}
infile1.close();
std::ifstream infile2;
infile2.open(testFileName, std::ios::in);
for (int a = 0; a < num_test; a++)
{
for (int i = 0; i < num_input_nodes; i++)
{
infile2 >> mv[i];
}
infile2 >> tv[0];
testsample->PushBack(mv);
testtargets->PushBack(tv);
std::cout << "Input =" << mv << std::endl;
std::cout << "target =" << tv << std::endl;
}
infile2.close();
OneHiddenLayerBackPropagationNeuralNetworkType::Pointer OneHiddenLayerNet = OneHiddenLayerBackPropagationNeuralNetworkType::New();
OneHiddenLayerNet->SetNumOfInputNodes(num_input_nodes);
OneHiddenLayerNet->SetNumOfFirstHiddenNodes(num_hidden_nodes);
OneHiddenLayerNet->SetNumOfOutputNodes(num_output_nodes);
OneHiddenLayerNet->Initialize();
OneHiddenLayerNet->InitializeWeights();
OneHiddenLayerNet->SetLearningRate(0.001);
TrainingFcnType::Pointer trainingfcn = TrainingFcnType::New();
trainingfcn->SetIterations(200);
trainingfcn->Train(OneHiddenLayerNet, trainsample, traintargets);
int return_value1=TestNetwork(testsample,testtargets,OneHiddenLayerNet);
int return_value2=EXIT_SUCCESS;
#ifdef USE_REVIEW_NETIO
{//Test Reading and writing.
typedef itk::Statistics::OneHiddenLayerBackPropagationNeuralNetwork<MeasurementVectorType, TargetVectorType> OneHiddenLayerBackPropagationNeuralNetworkType;
std::string TestOneHiddenLayerNetFileName("/tmp/OneLayer.net");
{
typedef itk::NeuralNetworkFileWriter<OneHiddenLayerBackPropagationNeuralNetworkType> OHLWriterType;
OHLWriterType::Pointer writerOneHiddenLayerBackPropagation=OHLWriterType::New();
writerOneHiddenLayerBackPropagation->SetWriteWeightValuesType(OHLWriterType::ASCII);
writerOneHiddenLayerBackPropagation->SetFileName(TestOneHiddenLayerNetFileName);
writerOneHiddenLayerBackPropagation->SetInput(OneHiddenLayerNet);
writerOneHiddenLayerBackPropagation->Update();
}
{
typedef itk::NeuralNetworkFileReader<OneHiddenLayerBackPropagationNeuralNetworkType> OHLReaderType;
OHLReaderType::Pointer readerOneHiddenLayerBackPropagation=OHLReaderType::New();
readerOneHiddenLayerBackPropagation->SetFileName(TestOneHiddenLayerNetFileName);
readerOneHiddenLayerBackPropagation->SetReadWeightValuesType( OHLReaderType::ASCII );
readerOneHiddenLayerBackPropagation->Update();
//The following line gives a compiler error
OneHiddenLayerBackPropagationNeuralNetworkType::Pointer OneHiddenLayerNet_ReadIn = readerOneHiddenLayerBackPropagation->GetOutput();
return_value2=TestNetwork(testsample,testtargets,OneHiddenLayerNet_ReadIn);
}
}
#endif
if(return_value1 == EXIT_FAILURE || return_value2 == EXIT_FAILURE)
{
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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