public List<Tuple<int, double, double>> Train(int iterationCount, double learnRate, double momentum)
{
List<Tuple<int, double, double>> error = new List<Tuple<int, double, double>>();
var training = new Backpropagation(network, trainingData, learnRate, momentum);
training.BatchSize = 1;
for (int i = 0; i < iterationCount; ++i)
{
training.Iteration(1);
network.CalculateError(validationData);
double val_error = network.CalculateError(validationData);
error.Add(new Tuple<int, double, double>(i, training.Error, val_error));
//if(i%100 == 0)
//Console.WriteLine("{0}: [{1}; {2}]", i, training.Error, val_error);
}
training.FinishTraining();
return error.Skip(100).ToList();
}
public virtual void TrainNetwork()
{
var train = new Backpropagation(Network.Model, TrainingSet, Parameters.LearingCoefficient, Parameters.InertiaCoefficient)
{
BatchSize = 1
};
var iteration = 1;
var errors = new List<double[]>();
do
{
train.Iteration();
var validationError = Network.Model.CalculateError(ValidationSet);
errors.Add(new[] { iteration, train.Error, validationError });
Console.WriteLine(
@"Iteration #" + iteration++ +
@" Training error:" + String.Format("{0:N10}", train.Error) +
@", Validation error:" + String.Format("{0:N10}", validationError));
} while ((iteration < Parameters.IterationsCount) && (train.Error > Parameters.AcceptedError));
train.FinishTraining();
ErrorSet = errors.ToArray();
}
public double[][] Train(int iterationsCount, double learningRate, double momentum)
{
var train = new Backpropagation(network, trainingData, learningRate, momentum);
train.BatchSize = 1;
double[][] errors = new double[2][];
errors[0] = new double[iterationsCount];
errors[1] = new double[iterationsCount];
for (int i = 0; i < iterationsCount; i++)
{
train.Iteration(1);
errors[0][i] = train.Error;
errors[1][i] = network.CalculateError(validationData);
}
train.FinishTraining();
return errors;
}