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; } }