static void Main(string[] args)
{
//Loading and parsing cup dataset
/* CupDataManager dm = new CupDataManager();
* DataSet wholeSet = dm.LoadData(Properties.Settings.Default.TrainingSetLocation, 10, 2, permute: true, seed: 1);
* List<double> momentums = new List<double> { 0, 0.5 };
* List<double> learningRates = new List<double> { 0.005, 0.01 };
* List<double> regularizationRates = new List<double> { 0, 0.001 };
* List<int> humberOfHiddenNeurons = new List<int> { 80 };
* //screening SGD+Momentum experiments
* GradientDescentParams passedParams = new GradientDescentParams();
* passedParams.nestrov = false;
* passedParams.resilient = false;
* passedParams.resilientUpdateAccelerationRate = 0.3;
* passedParams.resilientUpdateSlowDownRate = 0.1;
* new KFoldValidation().ScreenGD(wholeSet, 5, momentums, learningRates, regularizationRates, humberOfHiddenNeurons, passedParams,5000);*/
//screening Adam
//new KFoldValidation().ScreenAdam(wholeSet, 5, learningRates, regularizationRates, humberOfHiddenNeurons, 5000);
//ReportHowCloseWeightsAcquiredFromDifferentSeedsAre();
AA1_MLP.DataManagers.CupDataManager dm = new AA1_MLP.DataManagers.CupDataManager();
DataSet trainDS = dm.LoadData(@"C:\Users\Ronin\Documents\monks\Monks\UsedFiles\TrainValSplits\60percenttrain.txt", 10, 2, standardize: true);
DataSet testDS = dm.LoadData(@"C:\Users\Ronin\Documents\monks\Monks\UsedFiles\TrainValSplits\60percenttest.txt", 10, 2, standardize: true);
Console.WriteLine("Training Adamax");
AdamParams adampassedParams = new AdamParams();
IOptimizer adamtrainer = new Adam();
//adampassedParams.numberOfEpochs = 10000;
//adampassedParams.batchSize = 10;
//adampassedParams.trainingSet = trainDS;
//adampassedParams.validationSet = testDS;
//adampassedParams.learningRate = 0.001;
//adampassedParams.regularization = Regularizations.L2;
//adampassedParams.regularizationRate = 0.001;
//adampassedParams.NumberOfHiddenUnits = 100;
//adampassedParams.parallelize = false;
//LastTrain(testDS, adampassedParams, adamtrainer, "10kt100adam", 1);
adamtrainer = new Adamax();
adampassedParams.numberOfEpochs = 100000;
adampassedParams.batchSize = 10;
adampassedParams.trainingSet = trainDS;
adampassedParams.validationSet = testDS;
adampassedParams.learningRate = 0.001;
adampassedParams.regularization = Regularizations.L2;
adampassedParams.regularizationRate = 0.001;
adampassedParams.NumberOfHiddenUnits = 100;
//adampassedParams.PrintLoss = true;
adampassedParams.parallelize = false;
LastTrain(testDS, adampassedParams, adamtrainer, "100kadamax", 1);
/*Console.WriteLine("Training Adam");
* AdamParams adampassedParams = new AdamParams();
* IOptimizer adamtrainer = new Adamax();
*
* adampassedParams.numberOfEpochs = 100;
* adampassedParams.batchSize = 10;
* adampassedParams.trainingSet = trainDS;
* adampassedParams.validationSet = testDS;
* adampassedParams.learningRate = 0.001;
* adampassedParams.regularization = Regularizations.L2;
* adampassedParams.regularizationRate = 0.001;
* adampassedParams.NumberOfHiddenUnits = 100;
* adampassedParams.t = 1000000000;
*
* adampassedParams.parallelize = false;
* LastTrain(testDS, adampassedParams, adamtrainer, "100adam", 1);*/
/* Console.WriteLine("training SGD");
* GradientDescentParams passedParams = new GradientDescentParams();
* Gradientdescent trainer = new Gradientdescent();
* passedParams.numberOfEpochs = 100;
* passedParams.batchSize = 10;
* passedParams.trainingSet = trainDS;
* passedParams.validationSet = testDS;
* passedParams.learningRate = 0.001;
* passedParams.regularization = Regularizations.L2;
* passedParams.regularizationRate = 0.001;
* passedParams.nestrov = true;
* passedParams.resilient = false;
* passedParams.resilientUpdateAccelerationRate = 2;
* passedParams.resilientUpdateSlowDownRate = 0.5;
* passedParams.momentum = 0.5;
* passedParams.NumberOfHiddenUnits = 100;
* passedParams.parallelize = true;
* LastTrain(testDS, passedParams, trainer, "5kepochsprofiling_seq", 1);
*
* Console.WriteLine();*/
/*
* List<int> seeds = new List<int>() { 1,15,40,4,73,2};
*
* foreach (var seed in seeds)
* {
* Console.WriteLine("Seed:{0}",seed);
*
* /*AdamParams passedParams = new AdamParams();
* IOptimizer trainer = new Adam();
* Console.WriteLine("training SGD");
* GradientDescentParams passedParams = new GradientDescentParams();
* Gradientdescent trainer = new Gradientdescent();
* passedParams.numberOfEpochs = 20000;
* passedParams.batchSize = 10;
* passedParams.trainingSet = trainDS;
* passedParams.validationSet = testDS;
* passedParams.learningRate = 0.001;
* passedParams.regularization = Regularizations.L2;
* passedParams.regularizationRate = 0.001;
* passedParams.nestrov = true;
* passedParams.resilient = false;
* passedParams.resilientUpdateAccelerationRate = 2;
* passedParams.resilientUpdateSlowDownRate = 0.5;
*
* passedParams.momentum = 0.5;
* passedParams.NumberOfHiddenUnits = 100;
*
* LastTrain(testDS, passedParams, trainer, "20kseed_"+seed+"_", seed);
* }*/
/* Console.WriteLine("Training Adam");
* AdamParams adampassedParams = new AdamParams();
* IOptimizer adamtrainer = new Adam();
*
* adampassedParams.numberOfEpochs = 30000;
* adampassedParams.batchSize = 50;
* adampassedParams.trainingSet = trainDS;
* adampassedParams.validationSet = testDS;
* adampassedParams.learningRate = 0.001;
* adampassedParams.regularization = Regularizations.L2;
* adampassedParams.regularizationRate = 0.001;
* adampassedParams.NumberOfHiddenUnits = 100;
*
* LastTrain(testDS, adampassedParams, adamtrainer, "BS50_30kepochs_100_final_adam_hdn", 1);
*/
//Loading and parsing cup dataset
// CupDataManager dm = new CupDataManager();
//Loading the test dataset
//DataSet TestSet = dm.LoadData(Properties.Settings.Default.TestSetLocation, 10, reportOsutput: false);
//Loading the trained model
//var n = AA1_MLP.Utilities.ModelManager.LoadNetwork("Final_hidn18_reg0.01_mo0.5_lr9E-06_model.AA1");
//double MEE = 0;
//applying the model on the test data
//var predictions = ModelManager.GeneratorCUP(TestSet, n);
//writing the results
// File.WriteAllText("OMG_LOC-OSM2-TS.txt", string.Join("\n", predictions.Select(s => string.Join(",", s))));
}
static void Main(string[] args)
{
//Properties.Settings.Default.trainingDataLocation;
// string testSetLocation = Properties.Settings.Default.testDataLocation;
AA1_MLP.DataManagers.CupDataManager dm = new AA1_MLP.DataManagers.CupDataManager();
var trainDS = LoadData("D:\\dropbox\\Dropbox\\Master Course\\SEM-3\\ML\\CM_CUP_Datasets\\Standardized60percenttrain.txt");
var testDS = dm.LoadData("D:\\dropbox\\Dropbox\\Master Course\\SEM-3\\ML\\CM_CUP_Datasets\\Standardized60percenttest.txt", 10, 2);
// StandardizeData(trainDS);
// StandardizeData(testDS);
BasicNetwork network = new BasicNetwork();
network.AddLayer(new BasicLayer(10));
network.AddLayer(new BasicLayer(new ActivationTANH(), true, 100));
network.AddLayer(new BasicLayer(new ActivationLinear(), true, 2));
network.Structure.FinalizeStructure();
network.Reset();
var train = new Encog.Neural.Networks.Training.Propagation.SGD.StochasticGradientDescent(network, trainDS);
var update = new Encog.Neural.Networks.Training.Propagation.SGD.Update.NesterovUpdate();
update.Init(train);
train.LearningRate = 0.001;
train.Momentum = 0.5;
train.L2 = 0.001;
train.BatchSize = 10;
var watch = System.Diagnostics.Stopwatch.StartNew();
int epoch = 0;
using (System.IO.StreamWriter file =
new System.IO.StreamWriter(@"C:\Users\ahmad\Documents\monks\Monks\AA1_Monks\AA1_CUP\bin\Release\encog.txt"))
{
do
{
train.Iteration();
double valMEE = 0.0, valMSE = 0.0;
TesterCUPRegression(testDS, network, out valMEE, out valMSE);
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error + " valMSE:" + valMSE);
file.WriteLine("{0},{1}", train.Error, valMSE);
epoch++;
} while ((epoch < 5000) && (train.Error > 0));
}
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
Console.WriteLine("elapsed Time:{0} ms", elapsedMs);
var serializer = new BinaryFormatter();
using (var s = new FileStream(@"C:\Users\ahmad\Documents\monks\Monks\AA1_Monks\AA1_CUP\bin\Release\encog.n", FileMode.Create))
{
serializer.Serialize(s, network);
}
double MEE = 0.0, MSE = 0.0;
TesterCUPRegression(testDS, network, out MEE, out MSE);
Console.WriteLine("MEE{0},MSE{1}", MEE, MSE);
}
