int main (int argc, char* argv[]){
RegressionDataset dataset;
dataset.setName("IAM-sequenced10px");
fillImageVector(argv[1], 0, dataset, 10);
cout << dataset.getMean();
cout << dataset.getStandardDeviation();
dataset.save("../xml/IAM-10.xml");
return EXIT_SUCCESS;
}
int main(int argc, char* argv[]) {
vector<string> arguments;
arguments.push_back("population size");
arguments.push_back("number of hidden units");
arguments.push_back("number of iterations");
arguments.push_back("learning dataset");
arguments.push_back("validation dataset");
arguments.push_back("simple load mode");
cout << helper("Pbdnn cluster", "Train a population of neural networks on a regression task.", arguments) << endl;
if (argc != arguments.size() + 1) {
cerr << "Not enough arguments, " << argc - 1 << " given and " << arguments.size() << " required" << endl;
return EXIT_FAILURE;
}
int simpleMode = atoi(argv[6]);
RegressionDataset dataset;
RegressionDataset dataset2;
if(simpleMode!=0) {
dataset.simpleLoad(argv[4]);
dataset2.simpleLoad(argv[5]);
}
else {
dataset.load(argv[4]);
dataset2.load(argv[5]);
}
cout << "Learning dataset loaded, total elements : " << dataset.getNumSamples() << endl;
cout << "Validation dataset loaded, total elements : " << dataset2.getNumSamples() << endl;
int populationSize = atoi(argv[1]);
int numberOfHiddenUnits = atoi(argv[2]);
int iterations = atoi(argv[3]);
vector<Vec3b> colors = createColorRepartition(populationSize);
AEMeasurer mae;
PBDNN pop = PBDNN(populationSize, dataset.getFeatureVectorLength(), numberOfHiddenUnits, dataset.getMean(), dataset.getStandardDeviation());
DiversityMeasurer diversity(pop, dataset2, mae,0.01);
// 07/02/13 : Not sure if useful or not so stop doing it
/*do {
pop = PBDNN(populationSize, dataset.getFeatureVectorLength(), numberOfHiddenUnits, dataset.getMean(), dataset.getStandardDeviation());
diversity.measurePerformance();
} while (diversity.getDisagreementScalar() < 0.17);*/
Mask mask;
LearningParams params;
params.setActualIteration(0);
params.setMaxIterations(iterations);
params.setLearningRate(0.001);
params.setMaxTrainedPercentage(0.1);
params.setSavedDuringProcess(true);
params.setValidateEveryNIteration(100);
ofstream log("training.log");
PopulationClusterBP pbp(pop, dataset, params, dataset2, mask, mask, log);
// 07/02/13 : Not sure if useful or not so stop doing it
/*cout << "Starting diversity" << endl << diversity.getDisagreementMatrix() << endl;
cout << "Starting overall diversity : " << diversity.getDisagreementScalar() << endl;*/
cout << "Training" << endl;
double t = (double) getTickCount();
pbp.train();
t = ((double) getTickCount() - t) / getTickFrequency();
cout << "Time :" << t << endl;
cout << endl << "Saving network" << endl;
ofstream outStream("IAMpop.pop");
outStream << pop;
if(simpleMode == 0) {
cout << "Recording Data" << endl;
vector<NeuralNetworkPtr> population = pop.getPopulation();
vector<vector<int> > assignedTo = diversity.findBestNetwork();
vector<vector<FeatureVector> > recomposed = diversity.buildBestOutput();
vector<int> pngParams = vector<int>();
pngParams.push_back(CV_IMWRITE_PNG_COMPRESSION);
pngParams.push_back(3);
for (uint i = 0; i < population.size(); i++) {
ostringstream dir;
dir << "network" << i;
if (mkdir(dir.str().c_str(), S_IRWXU) == 0) {
for (uint j = 0; j < dataset2.getNumSequences(); j++) {
ostringstream name;
name << "network" << i << "\/neuralNet" << i << "sample" << j << ".png";
vector<FeatureVector> features;
for (uint k = 0; k < dataset2[j].size(); k++) {
population[i]->forward(dataset2[j][k]);
features.push_back(population[i]->getOutputSignal());
}
vector<int> color = vector<int>(features.size(), i);
Mat image = buildColorMapImage(features, 3, color, colors);
imwrite(name.str(), image, pngParams);
}
}
else {
throw invalid_argument("pbdnnCluster : could not create directory");
}
}
ostringstream dirR;
dirR << "recomposed";
if (mkdir(dirR.str().c_str(), S_IRWXU) == 0) {
for (uint j = 0; j < recomposed.size(); j++) {
ostringstream name;
name << "recomposed\/recomposedSample" << j << ".png";
vector<FeatureVector> features;
Mat image = buildColorMapImage(recomposed[j], 3, assignedTo[j], colors);
imwrite(name.str(), image, pngParams);
}
}
}
return EXIT_SUCCESS;
}