public void TestLMA()
{
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
BasicNetwork network = NetworkUtil.CreateXORNetworkUntrained();
IMLTrain rprop = new LevenbergMarquardtTraining(network, trainingData);
NetworkUtil.TestTraining(rprop, 0.03);
}
public void Execute(IExampleInterface app)
{
IMLDataSet trainingData = GenerateTraining(InputOutputCount, Compl);
IMLMethod method = EncogUtility.SimpleFeedForward(InputOutputCount,
HiddenCount, 0, InputOutputCount, false);
var train = new LevenbergMarquardtTraining((BasicNetwork) method, trainingData);
EncogUtility.TrainToError(train, 0.01);
EncogFramework.Instance.Shutdown();
}
/// <summary>
/// Create a LMA trainer.
/// </summary>
///
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
if (!(method is BasicNetwork))
{
throw new EncogError(
"LMA training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
var result = new LevenbergMarquardtTraining(
(BasicNetwork) method, training);
return result;
}
/// <summary>
/// Create a LMA trainer.
/// </summary>
///
/// <param name="method">The method to use.</param>
/// <param name="training">The training data to use.</param>
/// <param name="argsStr">The arguments to use.</param>
/// <returns>The newly created trainer.</returns>
public IMLTrain Create(IMLMethod method,
IMLDataSet training, String argsStr)
{
if (!(method is BasicNetwork))
{
throw new EncogError(
"LMA training cannot be used on a method of type: "
+ method.GetType().FullName);
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(argsStr);
var holder = new ParamsHolder(args);
bool useReg = holder.GetBoolean(
MLTrainFactory.PropertyBayesianRegularization, false, false);
var result = new LevenbergMarquardtTraining(
(BasicNetwork) method, training) {UseBayesianRegularization = useReg};
return result;
}
public IMLTrain Create(IMLMethod method, IMLDataSet training, string argsStr)
{
bool flag;
LevenbergMarquardtTraining training3;
if (method is BasicNetwork)
{
flag = new ParamsHolder(ArchitectureParse.ParseParams(argsStr)).GetBoolean("BAYES_REG", false, false);
training3 = new LevenbergMarquardtTraining((BasicNetwork) method, training);
if (3 == 0)
{
LevenbergMarquardtTraining training2;
return training2;
}
}
else if (0 == 0)
{
throw new EncogError("LMA training cannot be used on a method of type: " + method.GetType().FullName);
}
training3.UseBayesianRegularization = flag;
if (0 == 0)
{
}
return training3;
}
public void Train(BasicNetwork network, IMLDataSet training)
{
IMLTrain trainMain = new LevenbergMarquardtTraining(network, training);
// train the neural network
var stop = new StopTrainingStrategy();
ICalculateScore score = new TrainingSetScore(trainMain.Training);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
int epoch = 0;
while (!stop.ShouldStop() && trainMain.IterationNumber < 1500)
{
trainMain.Iteration();
Console.WriteLine("Training " + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
}
private double TrainNetwork(String what, BasicNetwork network, IMLDataSet trainingSet, string Method)
{
// train the neural network
ICalculateScore score = new TrainingSetScore(trainingSet);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
IMLTrain trainMain;
if (Method.Equals("Leven"))
{
Console.WriteLine("Using LevenbergMarquardtTraining");
trainMain = new LevenbergMarquardtTraining(network, trainingSet);
}
else
trainMain = new Backpropagation(network, trainingSet);
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;
}