public static void Run()
{
var training = EncogUtility.LoadEGB2Memory(Config.TrainingFile);
var pattern = new FeedForwardPattern
{
InputNeurons = training.InputSize,
OutputNeurons = training.IdealSize,
ActivationFunction = new ActivationTANH()
};
var prune = new PruneIncremental(training, pattern, 100, 1, 10,
new ConsoleStatusReportable());
prune.AddHiddenLayer(5, 50);
prune.AddHiddenLayer(0, 50);
Console.WriteLine("Starting prune process");
prune.Process();
EncogDirectoryPersistence.SaveObject(Config.NetworkFile, prune.BestNetwork);
EncogUtility.SaveEGB(Config.TrainingFile, prune.Training);
}
public static void Incremental(FileInfo dataDir)
{
FileInfo file = FileUtil.CombinePath(dataDir, Config.TRAINING_FILE);
if (!file.Exists)
{
Console.WriteLine(@"Can't read file: " + file);
return;
}
IMLDataSet training = EncogUtility.LoadEGB2Memory(file);
var pattern = new FeedForwardPattern
{
InputNeurons = training.InputSize,
OutputNeurons = training.IdealSize,
ActivationFunction = new ActivationTANH()
};
var prune = new PruneIncremental(training, pattern, 100, 1, 10,
new ConsoleStatusReportable());
prune.AddHiddenLayer(5, 50);
prune.AddHiddenLayer(0, 50);
prune.Process();
EncogDirectoryPersistence.SaveObject(file, prune.BestNetwork);
}
public static void Run()
{
Log("Loading training data");
var encog = new EncogPersistedCollection("market-training.dat", FileMode.Open);
var trainingSet = (BasicMLDataSet)encog.Find("market-training");
Log("Figuring out best system");
var pattern = new FeedForwardPattern
{
InputNeurons = trainingSet.InputSize,
OutputNeurons = trainingSet.IdealSize,
ActivationFunction = new ActivationTANH()
};
var prune = new PruneIncremental(trainingSet, pattern, 100, new ConsoleStatusReportable());
prune.AddHiddenLayer(1, 50);
prune.AddHiddenLayer(0, 50);
prune.Process();
Log("Done!!!!");
}
public void Prune()
{
INeuralDataSet trainingSet = new BasicNeuralDataSet(networkInput, networkIdealOutput);
FeedForwardPattern pattern = new FeedForwardPattern();
pattern.InputNeurons = INPUT_NEURONS;
pattern.OutputNeurons = OUTPUT_NEURONS;
if (ACTIVIATION_FUNCTION == 1)
pattern.ActivationFunction = new ActivationSigmoid();
else if (ACTIVIATION_FUNCTION == 2)
pattern.ActivationFunction = new ActivationTANH();
else
throw new System.Exception("Only 2 activation functions have been impletemented.");
PruneIncremental prune = new PruneIncremental(trainingSet, pattern, 200, new ConsoleStatusReportable());
prune.AddHiddenLayer(10, 40);
prune.AddHiddenLayer(0, 30);
prune.Process();
network = prune.BestNetwork;
Console.WriteLine("Prune process complete.");
}
private void Step4_Pruning()
{
txtStatus.Text = "Prune the network";
var trainingSet = EncogUtility.LoadCSV2Memory(Config.NormalizedTrainingFile.ToString(),
22, 1, true, CSVFormat.English, false);
var pattern = new FeedForwardPattern()
{
InputNeurons = 22,
OutputNeurons = 1,
ActivationFunction = new ActivationTANH()
};
prune = new PruneIncremental(trainingSet, pattern, 100, 1, 10, this);
prune.AddHiddenLayer(1, 10);
prune.AddHiddenLayer(0, 10);
Logs.Clear();
pruneWorker.RunWorkerAsync();
}