/// <summary>
/// The network that is to be trained.
/// </summary>
/// <param name="network">The training set.</param>
/// <param name="training">The OpenCL profile to use, null for CPU.</param>
/// <param name="profile">The OpenCL profile, or null for none.</param>
/// <param name="learnRate">The rate at which the weight matrix will be adjusted based on
/// learning.</param>
/// <param name="momentum">The influence that previous iteration's training deltas will
/// have on the current iteration.</param>
public Backpropagation(BasicNetwork network,
INeuralDataSet training, OpenCLTrainingProfile profile, double learnRate,
double momentum)
: base(network, training)
{
if (profile == null)
{
TrainFlatNetworkBackPropagation backFlat = new TrainFlatNetworkBackPropagation(
network.Structure.Flat,
this.Training,
learnRate,
momentum);
this.FlatTraining = backFlat;
}
#if !SILVERLIGHT
else
{
TrainFlatNetworkOpenCL rpropFlat = new TrainFlatNetworkOpenCL(
network.Structure.Flat, this.Training,
profile);
rpropFlat.LearnBPROP(learnRate, momentum);
this.FlatTraining = rpropFlat;
}
#endif
}
/// <param name="network">The network that is to be trained</param>
/// <param name="training">The training set</param>
/// <param name="learnRate"></param>
/// <param name="momentum"></param>
public Backpropagation(IContainsFlat network,
IMLDataSet training, double learnRate,
double momentum) : base(network, training)
{
ValidateNetwork.ValidateMethodToData(network, training);
var backFlat = new TrainFlatNetworkBackPropagation(
network.Flat, Training, learnRate, momentum);
FlatTraining = backFlat;
}
/// <summary>
/// Pause the training.
/// </summary>
/// <returns>A training continuation object to continue with.</returns>
public override TrainingContinuation Pause()
{
TrainingContinuation result = new TrainingContinuation();
TrainFlatNetworkBackPropagation backFlat = (TrainFlatNetworkBackPropagation)FlatTraining;
double[] d = backFlat.LastDelta;
result[Backpropagation.LAST_DELTA] = d;
return(result);
}
