/// <inheritDoc/>
public override void CreateTrainer(OpenCLTrainingProfile profile, Boolean singleThreaded)
{
Propagation.Propagation train = new Backpropagation(Network, Training,
profile, LearningRate, Momentum);
if (singleThreaded)
train.NumThreads = 1;
foreach (IStrategy strategy in Strategies)
{
train.AddStrategy(strategy);
}
Train = train;
}
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 double TrainNetwork(String what, BasicNetwork network, IMLDataSet trainingSet)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(
network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, trainingSet, 0.00001, 0.0);
var stop = new StopTrainingStrategy();
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop())
{
trainMain.Iteration();
app.WriteLine("Training " + what + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
return trainMain.Error;
}
public static double TrainNetworks(BasicNetwork network, IMLDataSet minis)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(minis);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain = new Backpropagation(network, minis, 0.0001, 0.01);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.Iteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error + @" Genetic Iteration:" + trainAlt.IterationNumber);
}
sw.Stop();
return trainMain.Error;
}
public static double TrainNetworks(BasicNetwork network, IMLDataSet minis)
{
Backpropagation trainMain = new Backpropagation(network, minis,0.0001,0.6);
//set the number of threads below.
trainMain.ThreadCount = 0;
// train the neural network
ICalculateScore score = new TrainingSetScore(minis);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
// IMLTrain trainMain = new Backpropagation(network, minis, 0.0001, 0.01);
StopTrainingStrategy stop = new StopTrainingStrategy(0.0001, 200);
trainMain.AddStrategy(new Greedy());
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
//prune strategy not in GIT!...Removing it.
//PruneStrategy strategypruning = new PruneStrategy(0.91d, 0.001d, 10, network,minis, 0, 20);
//trainMain.AddStrategy(strategypruning);
EncogUtility.TrainConsole(trainMain,network,minis, 15.2);
var sw = new Stopwatch();
sw.Start();
while (!stop.ShouldStop())
{
trainMain.Iteration();
Console.WriteLine(@"Iteration #:" + trainMain.IterationNumber + @" Error:" + trainMain.Error + @" Genetic Iteration:" + trainAlt.IterationNumber);
}
sw.Stop();
Console.WriteLine(@"Total elapsed time in seconds:" + TimeSpan.FromMilliseconds(sw.ElapsedMilliseconds).Seconds);
return trainMain.Error;
}