public override double Train(Data info, float lr, float mom)
{
IMLDataSet data = new BasicMLDataSet(info.InputData, info.OutputData);
//Train network on data set, parameters (Network, dataset, learning rate, momentum).
ICalculateScore score = new TrainingSetScore(data);
IMLTrain trainAlt = new NeuralSimulatedAnnealing(EncogNetwork, score, 10, 2, 1000);
IMLTrain learner;
learner = new LevenbergMarquardtTraining(EncogNetwork, data);
var stop = new StopTrainingStrategy();
learner.AddStrategy(new Greedy());
learner.AddStrategy(new HybridStrategy(trainAlt));
//Train network on data set.
double lastError = double.PositiveInfinity;
do
{
if (learner.Error != 0)
{
lastError = learner.Error;
}
learner.Iteration();
} while (lastError - learner.Error > 0.0000001);
return(learner.Error);
}
public void Train(BasicNetwork network, IMLDataSet training)
{
IMLTrain trainMain = new LevenbergMarquardtTraining(network, training);
// train the neural network
var stop = new StopTrainingStrategy();
var score = new TrainingSetScore(trainMain.Training);
var trainAlt = new NeuralSimulatedAnnealing(network, score, 10, 2, 100);
trainMain.AddStrategy(new HybridStrategy(trainAlt));
trainMain.AddStrategy(stop);
var epoch = 0;
while (!stop.ShouldStop() && trainMain.IterationNumber < 1500)
{
trainMain.Iteration();
Console.WriteLine("Training " + ", Epoch #" + epoch + " Error:" + trainMain.Error);
epoch++;
}
}