public void PreTrain(INNInputProvider PProvider, int PWhichWeightSet)
{
double[] input = PProvider.NextInputCase();
double[] posvis;
double[] poshid;
double[] negvis;
double[] neghid;
GetPreTrainData(PWhichWeightSet, input, out posvis, out poshid, out negvis, out neghid);
PerformPreTraining(PWhichWeightSet, posvis, poshid, negvis, neghid);
}
public void CalculateDiagonalHessian(INNInputProvider PProvider, int PHessianBatchLength)
{
double[][] input = new double[PHessianBatchLength][];
double[][] desired = new double[PHessianBatchLength][];
for (int i = 0; i < PHessianBatchLength; i++)
{
input[i] = PProvider.NextInputCase();
desired[i] = PProvider.DesiredOutput();
}
double[][][] output = GetOutputFromInputsBatch(input);
diagonalhessian = GetDerivativesFromOutputBatch(TDerivative.Second, output, input, desired);
PProvider.Rewind(PHessianBatchLength);
}
public override void Train(INNInputProvider PProvider)
{
double[] input = PProvider.NextInputCase();
double[][] output = GetOutputFromInputs(input);
double[][][] derivatives = GetDerivativesFromOutput(TDerivative.First, output, input, PProvider.DesiredOutput());
for (int i = 0; i < numlayers; i++)
{
for (int j = 0; j < layers[i].NumNeurons; j++)
{
int numweightsback = weights[i][j].GetLength(0);
for (int k = 0; k < numweightsback; k++)
{
derivatives[i][j][k] *= (-learnrates[i]) / (diagonalhessian[i][j][k] + blowuppreventer);
}
derivatives[i][j][numweightsback] *= (-learnrates[i]) / (diagonalhessian[i][j][numweightsback] + blowuppreventer);
}
}
ModifyWeights(derivatives);
}
public override void Run(INNInputProvider PProvider)
{
ProcessNetwork(PProvider.NextInputCase());
}