public RnnTrainRec(Database db, ObjectId mixtureId, Chromosome chromosome, double trainingSeconds,
Vec initialWeights, MRnnSpec rnnSpec, IEnumerable <double> costHistory)
: base(db, mixtureId, chromosome, trainingSeconds)
{
InitialWeights = initialWeights;
RnnSpec = rnnSpec;
CostHistory = costHistory.ToArray();
Database.Store(this);
}
public static RnnTrainRec TrainRnn(Mat input, Vec output, Chromosome chrom, MakeRnnTrainRecFunc makeResult, Func <bool> canceled = null)
{
Func <int, LayerSpec> logisticSigmoidRecurrent = nodeCount =>
new LayerSpec(nodeCount, true, ActivationType.LogisticSigmoid);
var layers = new List <LayerSpec> {
logisticSigmoidRecurrent(chrom.RnnLayer1NodeCount),
logisticSigmoidRecurrent(chrom.RnnLayer2NodeCount),
new LayerSpec(1, false, ActivationType.Linear)
};
var epochMax = chrom.RnnTrainingEpochs;
var numInputs = input.RowCount;
var rnnWeightCount = RNN.GetWeightCount(layers, numInputs);
var initialWeights = RNN.MakeRandomWeights(rnnWeightCount);
var trainResult = RNN.TrainSCG(layers, initialWeights, epochMax, input, output, canceled);
return(makeResult(initialWeights, MRnnSpec.FromRnnSpec(trainResult.RNNSpec), trainResult.CostHistory));
}
