/// <summary>
/// Uses the network with one input pattern to genereate output.
/// </summary>
/// <param name="pat">input pattern. Size (PatternSize X 1)</param>
/// <returns>the output matrix of the network</returns>
internal Matrix <float> use(ArrayInputData pat)
{
// convert
pat.Key = pat.Key.InsertRow(pat.Key.RowCount, new DenseVector(1, 1.0f));
// pass
forwardPass(pat.Key);
// return result
return(net_out);
}
/// <summary>
/// Trains the network using common backprop.
/// </summary>
/// <param name="traindata">all training data used in this training session. Matrix must have the size (PatternSize X PatternCount)</param>
/// <param name="epochs">Number of epochs to train the network.</param>
internal void backpropTraining(ArrayInputData traindata, int epochs)
{
// training pattern init
Matrix <float> pat = new DenseMatrix(traindata.Key.RowCount + 1, traindata.Key.ColumnCount, 1.0f);
pat.SetSubMatrix(0, traindata.Key.RowCount, 0, traindata.Key.ColumnCount, traindata.Key);
// target values init
Matrix <float> targets = traindata.Value;
// dw, dv
Matrix <float> dw = new DenseMatrix(HiddenNodes, PatternSize + 1);
Matrix <float> dv = new DenseMatrix(TargetSize, HiddenNodes + 1);
// training
for (int i = 0; i < epochs; i++)
{
// backprop
forwardPass(pat);
backwardPass(pat, targets);
weightUpdate(pat, dw, dv, targets);
}
}
