/// <summary>
/// Construct a Manhattan propagation training object.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="profile">The learning rate.</param>
/// <param name="learnRate">The OpenCL profile to use, null for CPU.</param>
public ManhattanPropagation(BasicNetwork network,
INeuralDataSet training, OpenCLTrainingProfile profile, double learnRate)
: base(network, training)
{
if (profile == null)
{
FlatTraining = new TrainFlatNetworkManhattan(
network.Structure.Flat,
this.Training,
learnRate);
}
#if !SILVERLIGHT
else
{
TrainFlatNetworkOpenCL rpropFlat = new TrainFlatNetworkOpenCL(
network.Structure.Flat, this.Training,
profile);
rpropFlat.LearnManhattan(learnRate);
this.FlatTraining = rpropFlat;
}
#endif
}
/// <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
}
/// <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
}
