public Backpropagation(IContainsFlat network, IMLDataSet training, double learnRate, double momentum)
: base(network, training)
{
ValidateNetwork.ValidateMethodToData(network, training);
TrainFlatNetworkBackPropagation propagation = new TrainFlatNetworkBackPropagation(network.Flat, this.Training, learnRate, momentum);
base.FlatTraining = propagation;
}
/// <param name="network">The network that is to be trained</param>
/// <param name="training">The training set</param>
/// <param name="learnRate"></param>
/// <param name="momentum"></param>
public Backpropagation(IContainsFlat network,
IMLDataSet training, double learnRate,
double momentum) : base(network, training)
{
ValidateNetwork.ValidateMethodToData(network, training);
var backFlat = new TrainFlatNetworkBackPropagation(
network.Flat, Training, learnRate, momentum);
FlatTraining = backFlat;
}
public static long BenchmarkEncogFlat(double[][] input, double[][] output)
{
var network = new FlatNetwork(input[0].Length, HIDDEN_COUNT, 0,
output[0].Length, false);
network.Randomize();
var trainingSet = new BasicMLDataSet(input, output);
var train = new TrainFlatNetworkBackPropagation(
network, trainingSet, 0.7, 0.7);
var a = new double[2];
var b = new double[1];
var sw = new Stopwatch();
sw.Start();
// run epoch of learning procedure
for (int i = 0; i < ITERATIONS; i++)
{
train.Iteration();
}
sw.Stop();
return sw.ElapsedMilliseconds;
}