public QuickPropagation(IContainsFlat network, IMLDataSet training, double learnRate)
: base(network, training)
{
ValidateNetwork.ValidateMethodToData(network, training);
TrainFlatNetworkQPROP kqprop = new TrainFlatNetworkQPROP(network.Flat, this.Training, learnRate);
base.FlatTraining = kqprop;
}
/**
*
*
* @param network
*
* @param training
*
* @param theLearningRate
*
*/
/// <summary>
/// Construct a QPROP trainer for flat networks.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="training">The training data.</param>
/// <param name="learnRate">The learning rate. 2 is a good suggestion as
/// a learning rate to start with. If it fails to converge,
/// then drop it. Just like backprop, except QPROP can
/// take higher learning rates.</param>
public QuickPropagation(IContainsFlat network,
IMLDataSet training, double learnRate) : base(network, training)
{
ValidateNetwork.ValidateMethodToData(network, training);
var backFlat = new TrainFlatNetworkQPROP(
network.Flat, Training, learnRate);
FlatTraining = backFlat;
}
