public IBidirectionalRecurrentTrainingManager CreateBidirectionalManager(
INeuralNetworkBidirectionalBatchTrainer trainer,
string dataFile,
ISequentialTrainingDataProvider testData,
int memorySize,
int?autoAdjustOnNoChangeCount = null
)
{
return(new BidirectionalManager(_lap, trainer, dataFile, testData, memorySize, autoAdjustOnNoChangeCount));
}
protected Tuple <float[], float[]> _Load(INeuralNetworkBidirectionalBatchTrainer network, string file, int hiddenLayerSize)
{
var forwardMemory = Enumerable.Range(0, hiddenLayerSize).Select(i => 0f).ToArray();
var backwardMemory = Enumerable.Range(0, hiddenLayerSize).Select(i => 0f).ToArray();
if (File.Exists(file))
{
using (var stream = new FileStream(file, FileMode.Open, FileAccess.Read)) {
var networkInfo = Serializer.Deserialize <BidirectionalNetwork>(stream);
network.NetworkInfo = networkInfo;
Array.Copy(networkInfo.ForwardMemory.Data, forwardMemory, Math.Min(networkInfo.ForwardMemory.Data.Length, forwardMemory.Length));
Array.Copy(networkInfo.BackwardMemory.Data, backwardMemory, Math.Min(networkInfo.BackwardMemory.Data.Length, backwardMemory.Length));
}
}
return(Tuple.Create(forwardMemory, backwardMemory));
}
public BidirectionalManager(ILinearAlgebraProvider lap,
INeuralNetworkBidirectionalBatchTrainer trainer,
string dataFile,
ISequentialTrainingDataProvider testData,
int memorySize,
int?autoAdjustOnNoChangeCount = 5) : base(testData, autoAdjustOnNoChangeCount)
{
_lap = lap;
_trainer = trainer;
_dataFile = dataFile;
var memory = _Load(_trainer, dataFile, memorySize);
_forwardMemory = memory.Item1;
_backwardMemory = memory.Item2;
}
protected bool _CalculateTestScore(ITrainingContext context, float[] forwardMemory, float[] backwardMemory, ISequentialTrainingDataProvider data, INeuralNetworkBidirectionalBatchTrainer network, IRecurrentTrainingContext recurrentContext, ref double bestScore, ref BidirectionalNetwork output)
{
bool flag = false;
var score = _GetScore(data, network, forwardMemory, backwardMemory, recurrentContext);
var errorMetric = recurrentContext.TrainingContext.ErrorMetric;
if ((errorMetric.HigherIsBetter && score > bestScore) || (!errorMetric.HigherIsBetter && score < bestScore))
{
bestScore = score;
output = network.NetworkInfo;
output.ForwardMemory = new FloatArray {
Data = forwardMemory
};
output.BackwardMemory = new FloatArray {
Data = backwardMemory
};
flag = true;
}
context.WriteScore(score, errorMetric.DisplayAsPercentage, flag);
return(flag);
}
protected double _GetScore(ISequentialTrainingDataProvider data, INeuralNetworkBidirectionalBatchTrainer network, float[] forwardMemory, float[] backwardMemory, IRecurrentTrainingContext context)
{
return(Math.Abs(network.Execute(data, forwardMemory, backwardMemory, context).SelectMany(d => d).Select(d => context.TrainingContext.ErrorMetric.Compute(d.Output, d.ExpectedOutput)).Average()));
}
