/// <summary>
/// Deep copy constructor
/// </summary>
/// <param name="source">Source instance</param>
public RPropTrainerSettings(RPropTrainerSettings source)
{
NumOfAttempts = source.NumOfAttempts;
NumOfAttemptEpochs = source.NumOfAttemptEpochs;
ZeroTolerance = source.ZeroTolerance;
PositiveEta = source.PositiveEta;
NegativeEta = source.NegativeEta;
IniDelta = source.IniDelta;
MinDelta = source.MinDelta;
MaxDelta = source.MaxDelta;
return;
}
//Constructor
/// <summary>
/// Instantiates the RPropTrainer
/// </summary>
/// <param name="net">The FF network to be trained.</param>
/// <param name="inputVectorCollection">The input vectors (input).</param>
/// <param name="outputVectorCollection">The output vectors (ideal).</param>
/// <param name="cfg">The configuration of the trainer.</param>
/// <param name="rand">The random object to be used.</param>
public RPropTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
RPropTrainerSettings cfg,
Random rand
)
{
if (!net.Finalized)
{
throw new InvalidOperationException($"Can´t create trainer. Network structure was not finalized.");
}
_cfg = cfg;
MaxAttempt = _cfg.NumOfAttempts;
MaxAttemptEpoch = _cfg.NumOfAttemptEpochs;
_net = net;
_rand = rand;
_inputVectorCollection = inputVectorCollection;
_outputVectorCollection = outputVectorCollection;
_weigthsGradsAcc = new double[_net.NumOfWeights];
_weigthsPrevGradsAcc = new double[_net.NumOfWeights];
_weigthsPrevDeltas = new double[_net.NumOfWeights];
_weigthsPrevChanges = new double[_net.NumOfWeights];
//Parallel gradient workers (batch ranges) preparation
int numOfWorkers = Math.Max(1, Math.Min(Environment.ProcessorCount - 1, _inputVectorCollection.Count));
_gradientWorkerDataCollection = new GradientWorkerData[numOfWorkers];
int workerBatchSize = _inputVectorCollection.Count / numOfWorkers;
for (int workerIdx = 0, fromRow = 0; workerIdx < numOfWorkers; workerIdx++, fromRow += workerBatchSize)
{
GradientWorkerData gwd = new GradientWorkerData
(
fromRow: fromRow,
toRow: (workerIdx == numOfWorkers - 1 ? _inputVectorCollection.Count - 1 : (fromRow + workerBatchSize) - 1),
numOfWeights: _net.NumOfWeights
);
_gradientWorkerDataCollection[workerIdx] = gwd;
}
InfoMessage = string.Empty;
//Start training attempt
Attempt = 0;
NextAttempt();
return;
}
