/// <summary>
/// Construct a resilient training object, allow the training parameters to
/// be specified. Usually the default parameters are acceptable for the
/// resilient training algorithm. Therefore you should usually use the other
/// constructor, that makes use of the default values.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training set to use.</param>
/// <param name="initialUpdate"></param>
/// <param name="maxStep">The maximum that a delta can reach.</param>
public ResilientPropagation(IContainsFlat network,
IMLDataSet training, double initialUpdate,
double maxStep) : base(network, training)
{
var rpropFlat = new TrainFlatNetworkResilient(
network.Flat, Training,
RPROPConst.DefaultZeroTolerance, initialUpdate, maxStep);
FlatTraining = rpropFlat;
}
/// <summary>
/// Construct a resilient training object, allow the training parameters to
/// be specified. Usually the default parameters are acceptable for the
/// resilient training algorithm. Therefore you should usually use the other
/// constructor, that makes use of the default values.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="training">The training set to use.</param>
/// <param name="profile">Optional EncogCL profile to execute on.</param>
/// <param name="initialUpdate">The initial update values, this is the amount that the deltas
/// are all initially set to.</param>
/// <param name="maxStep">The maximum that a delta can reach.</param>
public ResilientPropagation(BasicNetwork network,
INeuralDataSet training, OpenCLTrainingProfile profile,
double initialUpdate, double maxStep)
: base(network, training)
{
if (profile == null)
{
TrainFlatNetworkResilient rpropFlat = new TrainFlatNetworkResilient(
network.Structure.Flat, this.Training);
this.FlatTraining = rpropFlat;
}
#if !SILVERLIGHT
else
{
TrainFlatNetworkOpenCL rpropFlat = new TrainFlatNetworkOpenCL(
network.Structure.Flat, this.Training,
profile);
rpropFlat.LearnRPROP(initialUpdate, maxStep);
this.FlatTraining = rpropFlat;
}
#endif
}