public void testFeedForwardAndBAckLoopWorksWithMomentum()
{
// example 11.14 of Neural Network Design by Hagan, Demuth and Beale
Matrix hiddenLayerWeightMatrix = new Matrix(2, 1);
hiddenLayerWeightMatrix.Set(0, 0, -0.27);
hiddenLayerWeightMatrix.Set(1, 0, -0.41);
Vector hiddenLayerBiasVector = new Vector(2);
hiddenLayerBiasVector.SetValue(0, -0.48);
hiddenLayerBiasVector.SetValue(1, -0.13);
Vector input = new Vector(1);
input.SetValue(0, 1);
Matrix outputLayerWeightMatrix = new Matrix(1, 2);
outputLayerWeightMatrix.Set(0, 0, 0.09);
outputLayerWeightMatrix.Set(0, 1, -0.17);
Vector outputLayerBiasVector = new Vector(1);
outputLayerBiasVector.SetValue(0, 0.48);
Vector error = new Vector(1);
error.SetValue(0, 1.261);
double learningRate = 0.1;
double momentumFactor = 0.5;
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(
hiddenLayerWeightMatrix, hiddenLayerBiasVector,
outputLayerWeightMatrix, outputLayerBiasVector);
ffnn.SetTrainingScheme(new BackPropagationLearning(learningRate,
momentumFactor));
ffnn.ProcessInput(input);
ffnn.ProcessError(error);
Matrix finalHiddenLayerWeights = ffnn.GetHiddenLayerWeights();
Assert.AreEqual(-0.2675, finalHiddenLayerWeights.Get(0, 0), 0.001);
Assert.AreEqual(-0.4149, finalHiddenLayerWeights.Get(1, 0), 0.001);
Vector hiddenLayerBias = ffnn.GetHiddenLayerBias();
Assert.AreEqual(-0.4775, hiddenLayerBias.GetValue(0), 0.001);
Assert.AreEqual(-0.1349, hiddenLayerBias.GetValue(1), 0.001);
Matrix finalOutputLayerWeights = ffnn.GetOutputLayerWeights();
Assert.AreEqual(0.1304, finalOutputLayerWeights.Get(0, 0), 0.001);
Assert.AreEqual(-0.1235, finalOutputLayerWeights.Get(0, 1), 0.001);
Vector outputLayerBias = ffnn.GetOutputLayerBias();
Assert.AreEqual(0.6061, outputLayerBias.GetValue(0), 0.001);
}
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;
}
}