/// <summary> /// Runs this sample /// </summary> //JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET: //ORIGINAL LINE: public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException public static void Main(string[] args) { // create neural network MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(2, 3, 1); // use file provided in org.neuroph.sample.data package string inputFileName = typeof(FileIOSample).getResource("data/xor_data.txt").File; // create file input adapter using specifed file FileInputAdapter fileIn = new FileInputAdapter(inputFileName); // create file output adapter using specified file name FileOutputAdapter fileOut = new FileOutputAdapter("some_output_file.txt"); double[] input; // input buffer used for reading network input from file // read network input using input adapter while ((input = fileIn.readInput()) != null) { // feed neywork with input neuralNet.Input = input; // calculate network ... neuralNet.calculate(); // .. and get network output double[] output = neuralNet.Output; // write network output using output adapter fileOut.writeOutput(output); } // close input and output files fileIn.close(); fileOut.close(); // Also note that shorter way for this is using org.neuroph.util.io.IOHelper class }
public MatrixMultiLayerPerceptron(MultiLayerPerceptron sourceNetwork) { this.sourceNetwork = sourceNetwork; // copy layers, input and output neurons createMatrixLayers(); this.LearningRule = new MatrixMomentumBackpropagation(); }
public static void Main(string[] args) { DataSet irisDataSet = loadDataSet(); MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(4, 15, 3); configureLearningRule(neuralNet); neuralNet.learn(irisDataSet); Evaluation.runFullEvaluation(neuralNet, irisDataSet); }
public static void Main(string[] args) { DataSet trainingSet = new DataSet(2, 1); trainingSet.addRow(new DataSetRow(new double[] { 0, 0 }, new double[] { 0 })); trainingSet.addRow(new DataSetRow(new double[] { 0, 1 }, new double[] { 1 })); trainingSet.addRow(new DataSetRow(new double[] { 1, 0 }, new double[] { 1 })); trainingSet.addRow(new DataSetRow(new double[] { 1, 1 }, new double[] { 0 })); MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 2, 3, 1); neuralNet.learn(trainingSet); Evaluation.runFullEvaluation(neuralNet, trainingSet); }
private static void configureLearningRule(MultiLayerPerceptron neuralNet) { neuralNet.LearningRule.LearningRate = 0.02; neuralNet.LearningRule.MaxError = 0.01; neuralNet.LearningRule.ErrorFunction = new MeanSquaredError(); }