internal static void backPropogationDeepLearningDemo()
{
try
{
System.Console.WriteLine(Util.ntimes("*", 100));
System.Console.WriteLine(
"\n BackpropagationnDemo - Running BackProp {1} hidden layers on Iris data Set with {0} epochs of learning ",
epochs, numHiddenLayers);
System.Console.WriteLine(Util.ntimes("*", 100));
DataSet animalDataSet = DataSetFactory.getAnimalDataSet();
INumerizer numerizer = new AnimalDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(animalDataSet, numerizer);
NeuralNetworkConfig config = new NeuralNetworkConfig();
config.SetConfig(FeedForwardDeepNeuralNetwork.NUMBER_OF_INPUTS, 20);
config.SetConfig(FeedForwardDeepNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.SetConfig(FeedForwardDeepNeuralNetwork.NUMBER_OF_HIDDEN_LAYERS, numHiddenLayers);
config.SetConfig(FeedForwardDeepNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS_PER_LAYER, numNeuronsPerLayer);
config.SetConfig(FeedForwardDeepNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.SetConfig(FeedForwardDeepNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardDeepNeuralNetwork ffnn = new FeedForwardDeepNeuralNetwork(config, new SoftSignActivationFunction());
ffnn.SetTrainingScheme(new BackPropagationDeepLearning(0.1, 0.9));
ffnn.TrainOn(innds, epochs);
innds.RefreshDataset();
int[] result = ffnn.TestOnDataSet(innds);
System.Console.WriteLine(result[0] + " right, " + result[1] + " wrong");
}
catch (Exception e)
{
throw e;
}
}
