/// <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);
}
