//
public RProp(BackPropNet net, string patterns, string targets)
: base(net, patterns, targets)
{
try
{
// allocate mem for oldTotalDelta
oldTotalDelta = new double[net.Depth-1][];
totalDelta = new double[net.Depth-1][];
biasLearnRates = new double[net.Depth-1][];
oldTotalErrGrad = new double[net.Depth-1][,];
totalErrGrad = new double[net.Depth-1][,];
weightLearnRates = new double[net.Depth-1][,];
//
for(uint i=0; i< net.Depth-1; i++)
{
oldTotalDelta[i] = new double[net.GetLayer(i+1).GetSize()];
totalDelta[i] = new double[net.GetLayer(i+1).GetSize()];
biasLearnRates[i] = new double[net.GetLayer(i+1).GetSize()];
oldTotalErrGrad[i] = new double[errGrad[i].GetLength(0), errGrad[i].GetLength(1)];
totalErrGrad[i] = new double[errGrad[i].GetLength(0), errGrad[i].GetLength(1)];
weightLearnRates[i]= new double[errGrad[i].GetLength(0), errGrad[i].GetLength(1)];
}
//
// initialize all learning rates to 0.1
//
InitializeLearnRates();
}
catch(Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
}
protected uint vPeriod = 5; // validation period.
#endregion Fields
#region Constructors
//
//
public TrainingAlgorithm(BackPropNet net, string tPatterns, string tTargets)
{
uint depth = net.Depth;
//
try
{
// allocate mem for matrices of error gradients
errGrad = new double[depth-1][,]; // allocate mem for depth-1 vectors
//
for(uint i=0; i<depth-1; i++)
{
uint temp1 = net.GetLayer(i).GetSize();
uint temp2 = net.GetLayer(i+1).GetSize();
//
errGrad[i] = new double[temp1, temp2];
}
//
//
this.trPatterns = new DataSet(tPatterns);
this.trTargets = new DataSet(tTargets);
this.network = net;
//
if(trPatterns.GetNumberOfVectors() != trTargets.GetNumberOfVectors())
throw new Exception("Datasets have different number of vectors.");
if(trPatterns.GetDataValidation() != true || trTargets.GetDataValidation() != true)
throw new Exception("Invalid Data");
if(trPatterns.GetLengthOfVectors() != net.GetLayer(0).GetSize() ||
trTargets.GetLengthOfVectors() != net.GetLayer(net.Depth-1).GetSize())
throw new Exception("Incosistent Data/Inputs or Data/Outputs");
}
catch(Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
}
//
public SimpleBPM(BackPropNet net, string patterns, string targets)
: base(net, patterns, targets)
{
//
try
{
// allocate memory for previous biases
pBiasUpdates = new double[net.Depth-1][];
//
for(uint i=0; i<net.Depth-1; i++)
pBiasUpdates[i] = new double[net.GetLayer(i+1).GetSize()];
//
// allocate memory for previous weights
pWeightUpdates = (double[][,])this.errGrad.Clone(); // it should be tested
//
}
catch(System.Exception ex)
{
Console.WriteLine(ex.Message);
throw;
}
}
