|
public TrainFlatNetworkQPROP ( |
||
| network | The network to train. | |
| training | IMLDataSet | The training data. |
| theLearningRate | double | 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. |
| return | Encog.ML.Data | |
public TrainFlatNetworkQPROP(FlatNetwork network,
IMLDataSet training, double theLearningRate) : base(network, training)
{
LearningRate = theLearningRate;
LastDelta = new double[Network.Weights.Length];
Decay = 0.0001d;
OutputEpsilon = 0.35;
}