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 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();
}
