/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
var network = new FlatNetwork(2, 4, 0, 1, false);
network.Randomize();
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
var train = new TrainFlatNetworkResilient(network, trainingSet);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error:" + train.Error);
epoch++;
} while (train.Error > 0.01);
var output = new double[1];
// test the neural network
Console.WriteLine(@"Neural Network Results:");
foreach (IMLDataPair pair in trainingSet)
{
double[] input = pair.Input.Data;
network.Compute(input, output);
Console.WriteLine(input[0] + @"," + input[1] + @":" + output[0]);
}
}
/// <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="initialUpdate"></param>
/// <param name="maxStep">The maximum that a delta can reach.</param>
public ResilientPropagation(IContainsFlat network,
IMLDataSet training, double initialUpdate,
double maxStep) : base(network, training)
{
var rpropFlat = new TrainFlatNetworkResilient(
network.Flat, Training,
RPROPConst.DefaultZeroTolerance, initialUpdate, maxStep);
FlatTraining = rpropFlat;
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
IMLDataSet trainingSet = new BasicMLDataSet(XORInput, XORIdeal);
FlatNetwork network = CreateNetwork();
Console.WriteLine(@"Starting Weights:");
DisplayWeights(network);
Evaluate(network, trainingSet);
TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(
network, trainingSet);
for (int iteration = 1; iteration <= ITERATIONS; iteration++)
{
train.Iteration();
Console.WriteLine();
Console.WriteLine(@"*** Iteration #" + iteration);
Console.WriteLine(@"Error: " + train.Error);
Evaluate(network, trainingSet);
Console.WriteLine(@"LastGrad:"
+ FormatArray(train.LastGradient));
Console.WriteLine(@"Updates :"
+ FormatArray(train.UpdateValues));
DisplayWeights(network);
}
}
public ResilientPropagation(IContainsFlat network, IMLDataSet training, double initialUpdate, double maxStep)
: base(network, training)
{
TrainFlatNetworkResilient resilient = new TrainFlatNetworkResilient(network.Flat, this.Training, 1E-17, initialUpdate, maxStep);
base.FlatTraining = resilient;
}