static void Main(string[] args)
{
double[] p = new double[10];
IMLData data = new BasicMLData(p);
var network = CreateNetwork();
var trainingSet = GetTrainingSet();
//var train = new ResilientPropagation(network, trainingSet);
//var train = new Backpropagation(network, trainingSet, 0.7, 0.2);
//var train = new ManhattanPropagation(network, trainingSet, 0.001);
//var train = new ScaledConjugateGradient(network, trainingSet);
//var train = new LevenbergMarquardtTraining(network, trainingSet);
var train = new QuickPropagation(network, trainingSet, 2.0);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine($"Iteration No: {epoch++}, Error: {train.Error}");
}while (train.Error > 0.01);
foreach (var item in trainingSet)
{
var output = network.Compute(item.Input);
Console.WriteLine($"Input: {item.Input[0]}, {item.Input[1]} Ideal: {item.Ideal[0]} Actual: {output[0]}");
}
Console.ReadLine();
}
private static Propagation GetTraining(BasicNetwork network, BasicMLDataSet trainingSet)
{
//var train = new ResilientPropagation(network, trainingSet);
//var train = new Backpropagation(network, trainingSet, 0.7, 0.2);
//var train = new ManhattanPropagation(network, trainingSet, 0.001);
//var train = new ScaledConjugateGradient(network, trainingSet);
//var train = new LevenbergMarquardtTraining(network, trainingSet);
var training = new QuickPropagation(network, trainingSet, 2.0);
return(training);
}
public static BasicNetwork TrainNetwork(string trainSetLocation, string path2SaveModel, string trainingHistoryLocation, int numberOfEpochs, double errorThreshold, NetworkTrainer trainer)
{
IMLDataSet trainingDataSet = null;
List <NeuralLayerDescriptor> descriptor = NetworkTrainingInitializer(trainSetLocation, out trainingDataSet);
var network = NetworkCreator.CreateNetwork(descriptor);
ITrain train = null;
switch (trainer)
{
case NetworkTrainer.BackProp: train = new Backpropagation(network, trainingDataSet);
break;
case NetworkTrainer.ResilientBackProp: train = new ResilientPropagation(network, trainingDataSet);
break;
case NetworkTrainer.Quick:
train = new QuickPropagation(network, trainingDataSet);
break;
case NetworkTrainer.ScaledConjugateGradient: train = new ScaledConjugateGradient(network, trainingDataSet);
break;
}
int epoch = 0;
using (System.IO.StreamWriter file =
new System.IO.StreamWriter(trainingHistoryLocation))
{
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
file.WriteLine(train.Error);
epoch++;
} while ((epoch < numberOfEpochs) && (train.Error > errorThreshold));
}
var serializer = new BinaryFormatter();
using (var s = new FileStream(path2SaveModel, FileMode.Create))
{
serializer.Serialize(s, network);
}
return(network);
}
