/// <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]);
}
}
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;
}
