public static double TrainNetwork(String what,
FreeformNetwork network, IMLDataSet trainingSet) {
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
IMLTrain trainMain = new FreeformBackPropagation(network, trainingSet,0.00001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
EncogUtility.TrainToError(trainMain, 0.01);
return trainMain.Error;
}
public void Execute(IExampleInterface app)
{
// create a neural network, without using a factory
var network = new FreeformNetwork();
IFreeformLayer inputLayer = network.CreateInputLayer(2);
IFreeformLayer hiddenLayer1 = network.CreateLayer(3);
IFreeformLayer outputLayer = network.CreateOutputLayer(1);
network.ConnectLayers(inputLayer, hiddenLayer1, new ActivationSigmoid(), 1.0, false);
network.ConnectLayers(hiddenLayer1, outputLayer, new ActivationSigmoid(), 1.0, false);
network.Reset();
// create training data
var trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var train = new FreeformBackPropagation(network, trainingSet, 0.7, 0.2);
train.BatchSize = 1;
EncogUtility.TrainToError(train, 0.01);
EncogUtility.Evaluate(network, trainingSet);
EncogFramework.Instance.Shutdown();
}
