private static void LastTrain(DataSet testDS, INeuralTrainerParams passedParams, IOptimizer trainer, string prefix, int seed)
{
string path = prefix + passedParams.NumberOfHiddenUnits + "_lr" + passedParams.learningRate + "_reg" + passedParams.regularizationRate;
//building the architecture
Network n = new Network(new List <Layer>()
{
new Layer(new ActivationIdentity(), true, 10),
new Layer(new ActivationTanh(), true, passedParams.NumberOfHiddenUnits),
// new Layer(new ActivationLeakyRelu(),true,40),
new Layer(new ActivationIdentity(), false, 2),
}, false, AA1_MLP.Enums.WeightsInitialization.Xavier, seed);
passedParams.network = n;
var watch = System.Diagnostics.Stopwatch.StartNew();
List <double[]> learningCurve = trainer.Train(passedParams);
watch.Stop();
var elapsedMs = watch.ElapsedMilliseconds;
Console.WriteLine("elapsed Time:{0} ms", elapsedMs);
double MEE = 0;
double MSE = 0;
var log = ModelManager.TesterCUPRegression(testDS, n, out MEE, out MSE);
File.WriteAllText(path + ".txt", string.Join("\n", learningCurve.Select(s => string.Join(",", s))));
File.AppendAllText(path + ".txt", "\nMEE:" + MEE + "MSE:" + MSE);
File.WriteAllText(path + "predVsActual.txt", string.Join("\n", log.Select(s => string.Join(",", s))));
ModelManager.SaveNetowrk(n, path + ".n");
}
public List <double[]> RunExperiment(IOptimizer optimizer, INeuralTrainerParams passedParams, out double MEE, out double MSE)
{
//building the architecture
Network n = new Network(new List <Layer>()
{
new Layer(new ActivationIdentity(), true, 10),
new Layer(new ActivationTanh(), true, passedParams.NumberOfHiddenUnits),
// new Layer(new ActivationLeakyRelu(),true,40),
new Layer(new ActivationIdentity(), false, 2),
}, false, AA1_MLP.Enums.WeightsInitialization.Xavier);
passedParams.network = n;
List <double[]> learningCurve = optimizer.Train(passedParams);
MEE = 0;
MSE = 0;
var log = ModelManager.TesterCUPRegression(passedParams.validationSet, n, out MEE, out MSE);
return(learningCurve);
}
private void RunKFoldWithSetOfParams(AA1_MLP.Entities.DataSet wholeSet, int k, INeuralTrainerParams passedParams, IOptimizer trainer, string reportsPath)
{
string kRunFolderName = string.Format("hdn{0}_k{1}_lr{2}_reg{3}", passedParams.NumberOfHiddenUnits, k, passedParams.learningRate, passedParams.regularizationRate);
string KRunfolderPath = Path.Combine(reportsPath, kRunFolderName);
if (passedParams is GradientDescentParams)
{
KRunfolderPath = string.Format("{0}_mo{1}", KRunfolderPath, ((GradientDescentParams)passedParams).momentum);
}
if (Directory.Exists(KRunfolderPath))
{
Directory.Delete(KRunfolderPath);
}
Directory.CreateDirectory(KRunfolderPath);
double avgMSE = 0;
double MEE = 0, MSE = 0;
int sizeOfDataFold = wholeSet.Labels.RowCount / k;
//the training set split
TrainDataset = new DataSet(
inputs: wholeSet.Inputs.SubMatrix(sizeOfDataFold,
wholeSet.Inputs.RowCount - sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount),
labels: wholeSet.Labels.SubMatrix(sizeOfDataFold,
wholeSet.Labels.RowCount - sizeOfDataFold, 0, wholeSet.Labels.ColumnCount));
//the validation set
ValidationSplit = new DataSet(
inputs: wholeSet.Inputs.SubMatrix(0, sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount),
labels: wholeSet.Labels.SubMatrix(0, sizeOfDataFold, 0, wholeSet.Labels.ColumnCount));
Console.WriteLine("Run number:{0}", 0);
passedParams.trainingSet = TrainDataset;
passedParams.validationSet = ValidationSplit;
var lc = RunExperiment(trainer, passedParams, out MEE, out MSE);
File.WriteAllText(Path.Combine(KRunfolderPath, "0_learningCurve.txt"), string.Join("\n", lc.Select(s => string.Join(",", s))));
avgMSE += MSE;
for (int idxdataFold = 1; idxdataFold < k - 1; idxdataFold++)
{
Console.WriteLine("Run number:{0}", idxdataFold);
//the training set split
TrainDataset = new DataSet(
inputs: wholeSet.Inputs.SubMatrix(0, idxdataFold * sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount
).Stack(wholeSet.Inputs.SubMatrix(idxdataFold * sizeOfDataFold + sizeOfDataFold,
wholeSet.Inputs.RowCount - idxdataFold * sizeOfDataFold - sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount)),
labels: wholeSet.Labels.SubMatrix(0, idxdataFold * sizeOfDataFold, 0, wholeSet.Labels.ColumnCount
).Stack(wholeSet.Labels.SubMatrix(idxdataFold * sizeOfDataFold + sizeOfDataFold,
wholeSet.Labels.RowCount - idxdataFold * sizeOfDataFold - sizeOfDataFold, 0, wholeSet.Labels.ColumnCount)));
//the validation set
ValidationSplit = new DataSet(
inputs: wholeSet.Inputs.SubMatrix(idxdataFold * sizeOfDataFold, sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount),
labels: wholeSet.Labels.SubMatrix(idxdataFold * sizeOfDataFold, sizeOfDataFold, 0, wholeSet.Labels.ColumnCount));
passedParams.trainingSet = TrainDataset;
passedParams.validationSet = ValidationSplit;
lc = RunExperiment(trainer, passedParams, out MEE, out MSE);
File.WriteAllText(Path.Combine(KRunfolderPath, idxdataFold + "_learningCurve.txt"), string.Join("\n", lc.Select(s => string.Join(",", s))));
avgMSE += MSE;
}
//the training set split
TrainDataset = new DataSet(
inputs: wholeSet.Inputs.SubMatrix(0, (k - 1) * sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount
).Stack(wholeSet.Inputs.SubMatrix((k - 1) * sizeOfDataFold + sizeOfDataFold,
wholeSet.Inputs.RowCount - (k - 1) * sizeOfDataFold - sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount)),
labels: wholeSet.Labels.SubMatrix(0, (k - 1) * sizeOfDataFold, 0, wholeSet.Labels.ColumnCount
).Stack(wholeSet.Labels.SubMatrix((k - 1) * sizeOfDataFold + sizeOfDataFold,
wholeSet.Labels.RowCount - (k - 1) * sizeOfDataFold - sizeOfDataFold, 0, wholeSet.Labels.ColumnCount)));
//the validation set
ValidationSplit = new DataSet(
inputs: wholeSet.Inputs.SubMatrix((k - 1) * sizeOfDataFold, sizeOfDataFold, 0, wholeSet.Inputs.ColumnCount),
labels: wholeSet.Labels.SubMatrix((k - 1) * sizeOfDataFold, sizeOfDataFold, 0, wholeSet.Labels.ColumnCount));
Console.WriteLine("Run number:{0}", k - 1);
passedParams.trainingSet = TrainDataset;
passedParams.validationSet = ValidationSplit;
lc = RunExperiment(trainer, passedParams, out MEE, out MSE);
File.WriteAllText(Path.Combine(KRunfolderPath, (k - 1) + "_learningCurve.txt"), string.Join("\n", lc.Select(s => string.Join(",", s))));
avgMSE += MSE;
avgMSE /= k;
Console.WriteLine("Average MSE:{0}", avgMSE);
File.AppendAllLines(Path.Combine(reportsPath, "avgMSEs"), new string[] { string.Format("{0},{1}", kRunFolderName, avgMSE) });
}
