//Constructor
/// <summary>
/// Instantiates the RPropTrainer
/// </summary>
/// <param name="net">
/// The feed forward network to be trained
/// </param>
/// <param name="inputVectorCollection">
/// Collection of the training input vectors
/// </param>
/// <param name="outputVectorCollection">
/// Collection of the desired outputs
/// </param>
/// Trainer parameters
/// <param name="settings">
/// </param>
public RPropTrainer(FeedForwardNetwork net,
List <double[]> inputVectorCollection,
List <double[]> outputVectorCollection,
RPropTrainerSettings settings = null
)
{
if (!net.Finalized)
{
throw new Exception("Can´t create trainer. Network structure was not finalized.");
}
_settings = settings;
if (_settings == null)
{
//Default parameters
_settings = new RPropTrainerSettings();
}
_net = net;
_inputVectorCollection = inputVectorCollection;
_outputVectorCollection = outputVectorCollection;
_weigthsGradsAcc = new double[_net.NumOfWeights];
_weigthsGradsAcc.Populate(0);
_weigthsPrevGradsAcc = new double[_net.NumOfWeights];
_weigthsPrevGradsAcc.Populate(0);
_weigthsPrevDeltas = new double[_net.NumOfWeights];
_weigthsPrevDeltas.Populate(_settings.IniDelta);
_weigthsPrevChanges = new double[_net.NumOfWeights];
_weigthsPrevChanges.Populate(0);
_prevMSE = 0;
MSE = 0;
Epoch = 0;
//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;
}
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;
}
