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/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: NNetClassifierTest2.cxx,v $
Language: C++
Date: $Date: 2007-08-17 13:10:57 $
Version: $Revision: 1.5 $
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.
=========================================================================*/
#include "itkTwoHiddenLayerBackPropagationNeuralNetwork.h"
#include "itkIterativeSupervisedTrainingFunction.h"
#include "itkBatchSupervisedTrainingFunction.h"
#include "itkVector.h"
#include "itkListSample.h"
#include <vector>
#include <fstream>
#define ROUND(x) (floor(x+0.5))
int
NNetClassifierTest2(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];
char* testFileName = argv[2];
const int num_input_nodes = 2;
const int num_hidden1_nodes = 3;
const int num_hidden2_nodes = 2;
const int num_output_nodes = 1;
typedef itk::Vector<double, num_input_nodes> MeasurementVectorType;
typedef itk::Vector<double, num_output_nodes> TargetVectorType;
typedef itk::Statistics::ListSample<MeasurementVectorType> SampleType;
typedef itk::Statistics::ListSample<TargetVectorType> TargetType;
typedef itk::Statistics::IterativeSupervisedTrainingFunction<SampleType, TargetType, double> TrainingFcnType;
MeasurementVectorType mv;
TargetVectorType tv;
TargetVectorType ov;
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);
}
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);
}
infile2.close();
typedef itk::Statistics::TwoHiddenLayerBackPropagationNeuralNetwork<MeasurementVectorType, TargetVectorType> TwoHiddenLayerBackPropagationNeuralNetworkType;
TwoHiddenLayerBackPropagationNeuralNetworkType::Pointer net1 = TwoHiddenLayerBackPropagationNeuralNetworkType::New();
net1->SetNumOfInputNodes(num_input_nodes);
net1->SetNumOfFirstHiddenNodes(num_hidden1_nodes);
net1->SetNumOfSecondHiddenNodes(num_hidden2_nodes);
net1->SetNumOfOutputNodes(num_output_nodes);
net1->Initialize();
net1->InitializeWeights();
net1->SetLearningRate(0.05);
TrainingFcnType::Pointer trainingfcn = TrainingFcnType::New();
trainingfcn->SetIterations(5000);
trainingfcn->SetThreshold(0.0001);
trainingfcn->Train(net1, trainsample, traintargets);
//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())
{
mv = iter1.GetMeasurementVector();
tv = iter2.GetMeasurementVector();
ov.Set_vnl_vector(net1->GenerateOutput(mv));
flag=0;
if (fabs(tv[0]-ov[0])>0.3)
{
flag = 1;
}
if (flag == 1 && ROUND(tv[0]) == 1)
{
++error1;
}
else if (flag == 1 && ROUND(tv[0]) == -1)
{
++error2;
}
outfile<<mv[0]<<" "<<mv[1]<<" "<<tv[0]<<" "<<ov[0]<<std::endl;
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 "<<num_test<<" measurement vectors, " << error1 + error2
<< " vectors are misclassified." << std::endl ;
std::cout<<"Network Weights = "<<std::endl;
std::cout << net1 << std::endl;
if (double(error1 / 10) > 2 || double(error2 / 10) > 2)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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