//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)
{
// path to image directory
string imageDir = "/home/zoran/Downloads/MihailoHSLTest/trening";
// image names - used for output neuron labels
List <string> imageLabels = new ArrayList();
imageLabels.Add("bird");
imageLabels.Add("cat");
imageLabels.Add("dog");
// create dataset
IDictionary <string, FractionRgbData> map = ImageRecognitionHelper.getFractionRgbDataForDirectory(new File(imageDir), new Dimension(20, 20));
DataSet dataSet = ImageRecognitionHelper.createRGBTrainingSet(imageLabels, map);
// create neural network
List <int?> hiddenLayers = new List <int?>();
hiddenLayers.Add(12);
NeuralNetwork nnet = ImageRecognitionHelper.createNewNeuralNetwork("someNetworkName", new Dimension(20, 20), ColorMode.COLOR_RGB, imageLabels, hiddenLayers, TransferFunctionType.SIGMOID);
// set learning rule parameters
MomentumBackpropagation mb = (MomentumBackpropagation)nnet.LearningRule;
mb.LearningRate = 0.2;
mb.MaxError = 0.9;
mb.Momentum = 1;
// traiin network
Console.WriteLine("NNet start learning...");
nnet.learn(dataSet);
Console.WriteLine("NNet learned");
}
public virtual void run()
{
Console.WriteLine("Creating training and test set from file...");
string trainingSetFileName = "data_sets/segment challenge.txt";
string testSetFileName = "data_sets/segment test.txt";
int inputsCount = 19;
int outputsCount = 7;
//Create training data set from file
DataSet trainingSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");
Console.WriteLine("Training set size: " + trainingSet.Rows.Count);
trainingSet.shuffle();
trainingSet.shuffle();
//Normalizing training data set
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(trainingSet);
//Create test data set from file
DataSet testSet = DataSet.createFromFile(testSetFileName, inputsCount, outputsCount, ",");
Console.WriteLine("Test set size: " + testSet.Rows.Count);
Console.WriteLine("--------------------------------------------------");
testSet.shuffle();
testSet.shuffle();
//Normalizing training data set
normalizer.normalize(testSet);
Console.WriteLine("Creating neural network...");
//Create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 17, 10, outputsCount);
//attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.addListener(this);
learningRule.LearningRate = 0.01;
learningRule.MaxError = 0.001;
learningRule.MaxIterations = 12000;
Console.WriteLine("Training network...");
//train the network with training set
neuralNet.learn(trainingSet);
Console.WriteLine("Testing network...\n\n");
testNeuralNetwork(neuralNet, testSet);
Console.WriteLine("Done.");
Console.WriteLine("**************************************************");
// }
}
//Creating and saving neural network to file
public virtual void createNeuralNetwork()
{
Console.WriteLine("Creating neural network... ");
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(config.InputCount, config.FirstHiddenLayerCount, config.SecondHiddenLayerCount, config.OutputCount);
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.LearningRate = 0.01;
learningRule.MaxError = 0.1;
learningRule.MaxIterations = 1000;
Console.WriteLine("Saving neural network to file... ");
neuralNet.save(config.TrainedNetworkFileName);
Console.WriteLine("Neural network successfully saved!");
}
public virtual void run()
{
Console.WriteLine("Creating training and test set from file...");
string trainingSetFileName = "data_sets/diabetes.txt";
int inputsCount = 8;
int outputsCount = 2;
//Create data set from file
DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",");
dataSet.shuffle();
//Normalizing data set
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(dataSet);
//Creatinig training set (70%) and test set (30%)
DataSet[] trainingAndTestSet = dataSet.createTrainingAndTestSubsets(70, 30);
DataSet trainingSet = trainingAndTestSet[0];
DataSet testSet = trainingAndTestSet[1];
// for (int i = 0; i < 21; i++) {
Console.WriteLine("Creating neural network...");
//Create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 9, outputsCount);
// System.out.println("HIDDEN COUNT: " + i);
//attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.addListener(this);
learningRule.LearningRate = 0.05;
learningRule.MaxError = 0.01;
learningRule.MaxIterations = 10000;
Console.WriteLine("Training network...");
//train the network with training set
neuralNet.learn(trainingSet);
Console.WriteLine("Testing network...\n\n");
testNeuralNetwork(neuralNet, testSet);
Console.WriteLine("Done.");
Console.WriteLine("**************************************************");
// }
}
public virtual void run()
{
Console.WriteLine("Creating training set...");
string trainingSetFileName = "data_sets/cpu_data.txt";
int inputsCount = 7;
int outputsCount = 1;
// create training set from file
DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, ",", false);
Normalizer normalizer = new MaxNormalizer();
normalizer.normalize(dataSet);
Console.WriteLine("Creating neural network...");
// create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 16, outputsCount);
// attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.addListener(this);
// set learning rate and max error
learningRule.LearningRate = 0.2;
learningRule.MaxError = 0.01;
Console.WriteLine("Training network...");
// train the network with training set
neuralNet.learn(dataSet);
Console.WriteLine("Training completed.");
Console.WriteLine("Testing network...");
testNeuralNetwork(neuralNet, dataSet);
Console.WriteLine("Saving network");
// save neural network to file
neuralNet.save("MyNeuralNetCPU.nnet");
Console.WriteLine("Done.");
}
public virtual void train()
{
// get the path to file with data
string inputFileName = "C:\\timeseries\\BSW15";
// create MultiLayerPerceptron neural network
neuralNet = new MultiLayerPerceptron(TransferFunctionType.TANH, 5, 10, 1);
MomentumBackpropagation learningRule = (MomentumBackpropagation)neuralNet.LearningRule;
learningRule.LearningRate = 0.2;
learningRule.Momentum = 0.5;
// learningRule.addObserver(this);
learningRule.addListener(this);
// create training set from file
trainingSet = DataSet.createFromFile(inputFileName, 5, 1, "\t", false);
// train the network with training set
neuralNet.learn(trainingSet);
System.Console.WriteLine("Done training.");
}
