/// <summary>
/// The entry point for this example. If you would like to make this example
/// stand alone, then add to its own project and rename to Main.
/// </summary>
/// <param name="args">Not used.</param>
public static void ExampleMain(string[] args)
{
{
// Create an dbnn belief network.
int[] hidden = { 2, 3 };
DeepBeliefNetwork dbn = new DeepBeliefNetwork(TRAINING_INPUT[0].Length, hidden, TRAINING_IDEAL[0].Length);
dbn.Random = new MersenneTwisterGenerateRandom(54321);
dbn.Reset();
// Layer by layer unsupervised training.
for (int level = 0; level < hidden.Length; level++)
{
UnsupervisedTrainDBN trainUnsupervised = new UnsupervisedTrainDBN(
dbn, level, TRAINING_INPUT, LearningRateUnsupervised, K);
for (int i = 0; i < 2000; i++)
{
trainUnsupervised.Iteration();
}
}
// Supervised training.
SupervisedTrainDBN trainSupervised = new SupervisedTrainDBN(
dbn, TRAINING_INPUT, TRAINING_IDEAL, LearningRateSupervised);
int iteration = 0;
do
{
iteration++;
trainSupervised.Iteration();
Console.WriteLine("Iteration: " + iteration + ", Supervised training: error = "
+ trainSupervised.LastError);
} while (trainSupervised.LastError > 0.001);
// Use test data.
foreach (double[] input in TEST_INPUT)
{
double[] output = dbn.ComputeRegression(input);
Console.WriteLine(string.Join(",", input) + " -> " + string.Join(",", output));
}
}
}
