ViElement ViNeuralNetwork::exportData()
{
ViElement element("neuralnetwork");
ViElement neuronLayers("neuronlayers");
neuronLayers.addAttribute("count", mLayers.size());
for(int i = 0; i < mLayers.size(); ++i)
{
neuronLayers.addChild(mLayers[i]->exportData());
}
element.addChild(neuronLayers);
ViElement synapseLayers("synapselayers");
synapseLayers.addAttribute("count", mLayers.size() - 1);
for(int i = 1; i < mLayers.size(); ++i)
{
ViElement synapseLayer("synapselayer");
synapseLayer.addAttribute("count", mLayers[i]->inputSize());
for(int j = 0; j < mLayers[i]->size(); ++j)
{
for(int k = 0; k < mLayers[i]->at(j)->inputSize(); ++k)
{
synapseLayer.addChild(mLayers[i]->at(j)->inputAt(k)->exportData());
}
}
synapseLayers.addChild(synapseLayer);
}
element.addChild(synapseLayers);
return element;
}
int main(int argc, char const *argv[])
{
std::srand ( unsigned ( std::time(0) ) );
DataRetrieval dr(argv[1]);
particles = dr.getParticles();
std::random_shuffle ( particles.begin(), particles.end() );
numHiddenLayers =atoi(argv[2]);
numHiddenNodes = atoi(argv[3]);
int generations = atoi(argv[4]);
double trainPercent =0.8;
cout << "==========================================================================================" << endl;
cout << "Starting Neural Network" << endl;
cout << "Number of Inputs: " << numInputs << endl;
cout << "Number of Hidden Layers: " << numHiddenLayers << endl;
cout << "Number of Hidden Nodes: " << numHiddenNodes << endl;
cout << "Number of Outputs: " << numOutputs << endl;
cout << "Number of Generations: " << generations << endl;
cout << "-------------------------------------------------------------------------------------------" << endl;
int numTrainedParticles = particles.size()*trainPercent;
cout << "Number of Trained Particles: " << numTrainedParticles << endl;
NeuronLayers neuronLayers(numInputs, numHiddenLayers, numHiddenNodes, numOutputs);
for(int i=0;i<generations;i++)
{
//std::random_shuffle ( particles.begin(), particles.begin()+numTrainedParticles );
clog << "Generation No: "<< i<< " out of " << generations;
for(int u=0;u<numTrainedParticles;u++)
{
ParticleInformation particle = particles[u];
fillInputs(particle, neuronLayers);
fillTargets(particle, neuronLayers);
neuronLayers.forwardPass();
neuronLayers.backwardPass();
}
float error = fillNeuralNetworkPIDs(neuronLayers, 0, numTrainedParticles);
float efficiency = makeNeuralNetworkHistogram(0,numTrainedParticles, false);
clog << "\t"<< efficiency << " " << error << endl;
//clog << endl;
}
//Trained Results
cout << "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" << endl;
cout << "Trained Results" << endl;
float error = fillNeuralNetworkPIDs(neuronLayers, 0, numTrainedParticles);
float efficiency = makeNeuralNetworkHistogram( 0,numTrainedParticles, true);
cout << "Error: " << error << endl;
cout << "Pion Efficiency: " << efficiency << endl;
cout << "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" << endl;
//Untrained Results
cout << "Untrained Results" << endl;
error = fillNeuralNetworkPIDs(neuronLayers, numTrainedParticles, particles.size());
efficiency =makeNeuralNetworkHistogram( numTrainedParticles,particles.size(), true);
cout << "Error: " << error << endl;
cout << "Pion Efficiency: " << efficiency << endl;
cout << "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" << endl;
//Combined Results
cout << "Combined Results" << endl;
error = fillNeuralNetworkPIDs(neuronLayers, 0, particles.size());
efficiency = makeNeuralNetworkHistogram( 0,particles.size(), true);
cout << "Error: " << error << endl;
cout << "Pion Efficiency: " << efficiency << endl;
cout << "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" << endl;
return 0;
}