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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.
*
*=========================================================================*/
#include "itkOneHiddenLayerBackPropagationNeuralNetwork.h"
#include "itkSymmetricSigmoidTransferFunction.h"
#include "itkBatchSupervisedTrainingFunction.h"
#include "itkListSample.h"
#include "itkMath.h"
#include <fstream>
#define ROUND(x) (floor(x+0.5))
int
QPropXORTest1(int argc, char* argv[])
{
if (argc < 2)
{
std::cout << "Usage: " << argv[0]
<< " InputFile(.txt)" << std::endl;
return EXIT_FAILURE;
}
char* dataFileName =argv[1]; //"qpropxortest.txt";
int num_input_nodes = 2;
int num_hidden_nodes = 2;
int num_output_nodes = 1;
srand(time(ITK_NULLPTR));
typedef itk::Array<double> MeasurementVectorType;
typedef itk::Array<double> TargetVectorType;
typedef itk::Statistics::ListSample<MeasurementVectorType> SampleType;
typedef itk::Statistics::ListSample<TargetVectorType> TargetType;
typedef itk::Statistics::BatchSupervisedTrainingFunction<SampleType, TargetType, double> TrainingFcnType;
MeasurementVectorType mv(num_input_nodes);
TargetVectorType tv(num_output_nodes);
SampleType::Pointer sample = SampleType::New();
TargetType::Pointer targets = TargetType::New();
sample->SetMeasurementVectorSize( num_input_nodes);
targets->SetMeasurementVectorSize( num_output_nodes);
std::ifstream infile1;
infile1.open(dataFileName, std::ios::in);
if (infile1.fail())
{
std::cout << argv[0] << " Cannot open file for reading: "
<< dataFileName << std::endl;
return EXIT_FAILURE;
}
infile1 >> mv[0] >> mv[1] >> tv[0];
while (!infile1.eof())
{
std::cout << "Input =" << mv << std::endl;
std::cout << "target =" << tv << std::endl;
sample->PushBack(mv);
targets->PushBack(tv);
infile1 >> mv[0] >> mv[1] >> tv[0];
}
infile1.close();
std::cout << sample->Size() << std::endl;
typedef itk::Statistics::OneHiddenLayerBackPropagationNeuralNetwork
<MeasurementVectorType, TargetVectorType> NetworkType;
NetworkType::Pointer net1 = NetworkType::New();
net1->SetNumOfInputNodes(num_input_nodes);
net1->SetNumOfFirstHiddenNodes(num_hidden_nodes);
net1->SetNumOfOutputNodes(num_output_nodes);
typedef itk::Statistics::SymmetricSigmoidTransferFunction<double> tfType;
tfType::Pointer transferfunction1=tfType::New();
net1->SetFirstHiddenTransferFunction(transferfunction1);
net1->SetOutputTransferFunction(transferfunction1);
typedef itk::Statistics::QuickPropLearningRule<NetworkType::LayerInterfaceType, TargetVectorType> QuickPropLearningRuleType;
QuickPropLearningRuleType::Pointer learningfunction=QuickPropLearningRuleType::New();
net1->SetLearningFunction(learningfunction);
net1->SetFirstHiddenLayerBias(1.0);
net1->SetOutputLayerBias(1.0);
net1->Initialize();
TrainingFcnType::Pointer trainingfcn = TrainingFcnType::New();
trainingfcn->SetIterations(50);
trainingfcn->SetThreshold(0.001);
//Network Simulation
std::cout << sample->Size() << std::endl;
std::cout << "Network Simulation" << std::endl;
TargetVectorType ov(num_output_nodes);
SampleType::ConstIterator iter1 = sample->Begin();
TargetType::ConstIterator iter2 = targets->Begin();
unsigned int error1 = 0;
unsigned int error2 = 0;
int flag;
int train_flag=1;
long num_iterations =0;
long max_iterations=1000;
std::ofstream outfile;
outfile.open("out1.txt",std::ios::out);
if (outfile.fail())
{
std::cout << argv[0] << " Cannot open file for wriing: "
<< "out1.txt" << std::endl;
return EXIT_FAILURE;
}
while (train_flag==1)
{
//train the network
net1->InitializeWeights();
trainingfcn->Train(net1, sample, targets);
num_iterations += 50;
iter1 = sample->Begin();
iter2 = targets->Begin();
error1=0;
error2=0;
//Simulate the network
while (iter1 != sample->End())
{
mv = iter1.GetMeasurementVector();
tv = iter2.GetMeasurementVector();
ov=net1->GenerateOutput(mv);
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;
flag = 0;
std::cout<<std::fabs(tv[0]-ov[0])<<std::endl;
if (std::fabs(tv[0]-ov[0])>0.2)
{
flag = 1;
}
if (flag == 1 && itk::Math::AlmostEquals(tv[0], 0.5) )
{
++error1;
}
else if (flag == 1 && itk::Math::AlmostEquals(tv[0], -0.5) )
{
++error2;
}
++iter1;
++iter2;
}
//check for convergence
if((error1+error2) == 0 || num_iterations > max_iterations)
{//Done training
train_flag=0;
}
}//outer while
std::cout<<"Number of Epochs = "<<num_iterations<<std::endl;
std::cout << "Among 4 measurement vectors, " << error1 + error2
<< " vectors are misclassified." << std::endl;
std::cout<<"Network Weights and Biases after Training= "<<std::endl;
std::cout << net1 << std::endl;
if ((error1 + error2) > 2)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
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