internal static void backPropogationDemo()
{
try
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
INumerizer numerizer = new IrisDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(irisDataSet, numerizer);
NeuralNetworkConfig config = new NeuralNetworkConfig();
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS,
6);
config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.SetTrainingScheme(new BackPropagationLearning(0.1, 0.9));
ffnn.TrainOn(innds, 1000);
innds.RefreshDataset();
int[] result = ffnn.TestOnDataSet(innds);
System.Console.WriteLine(result[0] + " right, " + result[1] + " wrong");
}
catch (Exception e)
{
throw e;
}
}
public void testDataSetPopulation()
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
INumerizer numerizer = new IrisDataSetNumerizer();
NeuralNetworkDataSet innds = new IrisNeuralNetworkDataSet();
innds.CreateExamplesFromDataSet(irisDataSet, numerizer);
NeuralNetworkConfig config = new NeuralNetworkConfig();
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6);
config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.SetTrainingScheme(new BackPropagationLearning(0.1, 0.9));
ffnn.TrainOn(innds, 10);
innds.RefreshDataset();
ffnn.TestOnDataSet(innds);
}
internal static void backPropogationDemo()
{
try
{
System.Console.WriteLine(Util.ntimes("*", 100));
System.Console.WriteLine(
"\n BackpropagationDemo - Running BackProp on Iris data Set with {0} epochs of learning ",
epochs);
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(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 20);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.SetConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, numNeuronsPerLayer);
config.SetConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.SetConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.SetTrainingScheme(new BackPropagationLearning(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;
}
}
