| public LearnBPROP ( double learningRate, double momentum ) : void | ||
| learningRate | double | The learning rate. |
| momentum | double | The momentum. |
| return | void | |
/// <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
}
