/// <param name="args"> command line arguments which represent paths to persisted neural network /// [0] - location of neural network </param> //JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET: //ORIGINAL LINE: public static void main(String[] args) throws java.io.IOException public static void Main(string[] args) { DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000); NeuralNetwork nn = NeuralNetwork.load(new FileInputStream(args[0])); Evaluation.runFullEvaluation(nn, testSet); }
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); }
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET: //ORIGINAL LINE: public static void main(String[] args) throws java.io.IOException public static void Main(string[] args) { DataSet trainSet = MNISTDataSet.createFromFile(MNISTDataSet.TRAIN_LABEL_NAME, MNISTDataSet.TRAIN_IMAGE_NAME, 200); DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000); BackPropagation learningRule = createLearningRule(); NeuralNetwork neuralNet = (new MultilayerPerceptronOptimazer <>()).withLearningRule(learningRule).createOptimalModel(trainSet); Evaluation.runFullEvaluation(neuralNet, testSet); }
public static void Main(string[] args) { string inputFileName = "/iris_data.txt"; DataSet irisDataSet = DataSet.createFromFile(inputFileName, 4, 3, ",", false); BackPropagation learningRule = createLearningRule(); NeuralNetwork neuralNet = (new MultilayerPerceptronOptimazer <>()).withLearningRule(learningRule).createOptimalModel(irisDataSet); 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); }