private void trainNetworkBackprop()
{
// IMLTrain train = new Backpropagation(this.network, this.input,this.ideal, 0.000001, 0.1);
IMLDataSet aset = new BasicMLDataSet(input, ideal);
int epoch = 1;
// train the neural network
ICalculateScore score = new TrainingSetScore(aset);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, aset, 0.001, 0.0);
StopTrainingStrategy stop = new StopTrainingStrategy();
var pop = new NEATPopulation(INPUT_SIZE, OUTPUT_SIZE, 1000);
// train the neural network
var step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
var train = new NEATTraining(score, pop);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
trainMain.AddStrategy(new HybridStrategy(train));
network.ClearContext();
while (!stop.ShouldStop())
{
trainMain.Iteration();
train.Iteration();
Console.WriteLine(@"Training " + @"Epoch #" + epoch + @" Error:" + trainMain.Error + @" Genetic iteration:" + trainAlt.IterationNumber + @"neat iteration:" + train.IterationNumber);
epoch++;
}
}
private NEATPopulation Generate()
{
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
ActivationStep step = new ActivationStep();
step.Center = 0.5;
NEATTraining train = new NEATTraining(
score, 2, 1, 10);
return((NEATPopulation)train.Population);
}
/// <summary>
/// Builds and trains a neat network.
/// </summary>
/// <param name="aset">The IMLDataset.</param>
/// <param name="inputcounts">The inputcounts.</param>
/// <param name="outputcounts">The outputcounts.</param>
/// <param name="populationsize">The populationsize.</param>
/// <param name="ToErrorTraining">To error rate you want to train too.</param>
/// <returns>a trained netnetwork.</returns>
public static NEATNetwork BuildTrainNeatNetwork(IMLDataSet aset, int inputcounts, int outputcounts, int populationsize, double ToErrorTraining)
{
NEATPopulation pop = new NEATPopulation(inputcounts, outputcounts, populationsize);
ICalculateScore score = new TrainingSetScore(aset);
// train the neural network
ActivationStep step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
NEATTraining train = new NEATTraining(score, pop);
EncogUtility.TrainToError(train, ToErrorTraining);
NEATNetwork network = (NEATNetwork)train.Method;
return(network);
}
private void Validate(NEATPopulation pop)
{
Assert.AreEqual(0.3, pop.OldAgePenalty);
Assert.AreEqual(50, pop.OldAgeThreshold);
Assert.AreEqual(10, pop.PopulationSize);
Assert.AreEqual(0.2, pop.SurvivalRate);
Assert.AreEqual(10, pop.YoungBonusAgeThreshold);
Assert.AreEqual(0.3, pop.YoungScoreBonus);
// see if the population can actually be used to train
IMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
ICalculateScore score = new TrainingSetScore(trainingSet);
NEATTraining train = new NEATTraining(score, pop);
train.Iteration();
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var pop = new NEATPopulation(2, 1, 1000);
ICalculateScore score = new TrainingSetScore(trainingSet);
// train the neural network
var step = new ActivationStep();
step.Center = 0.5;
pop.OutputActivationFunction = step;
var train = new NEATTraining(score, pop);
EncogUtility.TrainToError(train, 0.01);
var network = (NEATNetwork)train.Method;
network.ClearContext();
// test the neural network
Console.WriteLine(@"Neural Network Results:");
EncogUtility.Evaluate(network, trainingSet);
}
