static void Main(string[] argv)
{
const uint max_epochs = 1000;
uint num_threads = 1;
TrainingData data;
NeuralNet net;
long before;
float error;
if (argv.Length == 2)
num_threads = UInt32.Parse(argv[1]);
using (data = new TrainingData("..\\..\\..\\datasets\\mushroom.train"))
using (net = new NeuralNet(NetworkType.LAYER, 3, data.InputCount, 32, data.OutputCount))
{
net.ActivationFunctionHidden = ActivationFunction.SIGMOID_SYMMETRIC;
net.ActivationFunctionOutput = ActivationFunction.SIGMOID;
before = Environment.TickCount;
for (int i = 1; i <= max_epochs; i++)
{
error = num_threads > 1 ? net.TrainEpochIrpropmParallel(data, num_threads) : net.TrainEpoch(data);
Console.WriteLine("Epochs {0}. Current error: {1}", i.ToString("00000000"), error.ToString("0.0000000000"));
}
Console.WriteLine("ticks {0}", Environment.TickCount - before);
Console.ReadKey();
}
}
private static void TrainOnSteepnessFile(NeuralNet net, string filename,
uint max_epochs, uint epochs_between_reports,
float desired_error, float steepness_start,
float steepness_step, float steepness_end)
{
float error;
using (TrainingData data = new TrainingData())
{
data.ReadTrainFromFile(filename);
if (epochs_between_reports != 0)
{
Console.WriteLine("Max epochs {0}. Desired error: {1}", max_epochs.ToString("00000000"), desired_error.ToString("0.0000000000"));
}
net.ActivationSteepnessHidden = steepness_start;
net.ActivationSteepnessOutput = steepness_start;
for (int i = 1; i <= max_epochs; i++)
{
error = net.TrainEpoch(data);
if(epochs_between_reports != 0 && (i % epochs_between_reports == 0 || i == max_epochs || i == 1 || error < desired_error))
{
Console.WriteLine("Epochs {0}. Current error: {1}", i.ToString("00000000"), error.ToString("0.0000000000"));
}
if(error < desired_error)
{
steepness_start += steepness_step;
if(steepness_start <= steepness_end)
{
Console.WriteLine("Steepness: {0}", steepness_start);
net.ActivationSteepnessHidden = steepness_start;
net.ActivationSteepnessOutput = steepness_start;
}
else
{
break;
}
}
}
}
}