public FuNet1()
{
try
{
foreach (UIManager.LookAndFeelInfo info in UIManager.InstalledLookAndFeels)
{
if ("Nimbus".Equals(info.Name))
{
UIManager.LookAndFeel = info.ClassName;
break;
}
}
}
catch (Exception)
{
// If Nimbus is not available, you can set the GUI to another look and feel.
}
try
{
network = network.load(new FileInputStream("/home/mithquissir/Desktop/cnn/5-50-100/30.nnet"));
testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000);
}
catch (Exception e)
{
Console.WriteLine(e.ToString());
Console.Write(e.StackTrace);
}
initComponents();
}
/// <param name="args"> Command line parameters used to initialize parameters of multi layer neural network optimizer
/// [0] - maximal number of epochs during learning
/// [1] - learning error stop condition
/// [2] - learning rate used during learning process
/// [3] - number of validation folds
/// [4] - max number of layers in neural network
/// [5] - min neuron count per layer
/// [6] - max neuron count per layer
/// [7] - neuron increment count </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)
{
int maxIter = 10000; //Integer.parseInt(args[0]);
double maxError = 0.01; // Double.parseDouble(args[1]);
double learningRate = 0.2; // Double.parseDouble(args[2]);
int validationFolds = Convert.ToInt32(args[3]);
int maxLayers = Convert.ToInt32(args[4]);
int minNeuronCount = Convert.ToInt32(args[5]);
int maxNeuronCount = Convert.ToInt32(args[6]);
int neuronIncrement = Convert.ToInt32(args[7]);
LOG.info("MLP learning for MNIST started.....");
DataSet trainSet = MNISTDataSet.createFromFile(MNISTDataSet.TRAIN_LABEL_NAME, MNISTDataSet.TRAIN_IMAGE_NAME, 60000);
DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000);
BackPropagation bp = new BackPropagation();
bp.MaxIterations = maxIter;
bp.MaxError = maxError;
bp.LearningRate = learningRate;
// commented out due to errors
// KFoldCrossValidation errorEstimationMethod = new KFoldCrossValidation(neuralNet, trainSet, validationFolds);
//
// NeuralNetwork neuralNet = new MultilayerPerceptronOptimazer<>()
// .withLearningRule(bp)
// .withErrorEstimationMethod(errorEstimationMethod)
// .withMaxLayers(maxLayers)
// .withMaxNeurons(maxNeuronCount)
// .withMinNeurons(minNeuronCount)
// .withNeuronIncrement(neuronIncrement)
// .createOptimalModel(trainSet);
LOG.info("Evaluating model on Test Set.....");
// commented out due to errors
// Evaluation.runFullEvaluation(neuralNet, testSet);
LOG.info("MLP learning for MNIST successfully finished.....");
}
/// <param name="args"> Command line parameters used to initialize parameters of convolutional network
/// [0] - maximal number of epochs during learning
/// [1] - learning error stop condition
/// [2] - learning rate used during learning process
/// [3] - number of feature maps in 1st convolutional layer
/// [4] - number of feature maps in 2nd convolutional layer
/// [5] - number of feature maps in 3rd convolutional layer </param>
public static void Main(string[] args)
{
try
{
int maxIter = 10000; // Integer.parseInt(args[0]);
double maxError = 0.01; //Double.parseDouble(args[1]);
double learningRate = 0.2; // Double.parseDouble(args[2]);
int layer1 = Convert.ToInt32(args[3]);
int layer2 = Convert.ToInt32(args[4]);
int layer3 = Convert.ToInt32(args[5]);
LOG.info("{}-{}-{}", layer1, layer2, layer3);
DataSet trainSet = MNISTDataSet.createFromFile(MNISTDataSet.TRAIN_LABEL_NAME, MNISTDataSet.TRAIN_IMAGE_NAME, 100);
DataSet testSet = MNISTDataSet.createFromFile(MNISTDataSet.TEST_LABEL_NAME, MNISTDataSet.TEST_IMAGE_NAME, 10000);
Dimension2D inputDimension = new Dimension2D(32, 32);
Dimension2D convolutionKernel = new Dimension2D(5, 5);
Dimension2D poolingKernel = new Dimension2D(2, 2);
ConvolutionalNetwork convolutionNetwork = (new ConvolutionalNetwork.Builder()).withInputLayer(32, 32, 1).withConvolutionLayer(5, 5, layer1).withPoolingLayer(2, 2).withConvolutionLayer(5, 5, layer2).withPoolingLayer(2, 2).withConvolutionLayer(5, 5, layer3).withFullConnectedLayer(10).build();
ConvolutionalBackpropagation backPropagation = new ConvolutionalBackpropagation();
backPropagation.LearningRate = learningRate;
backPropagation.MaxError = maxError;
backPropagation.MaxIterations = maxIter;
backPropagation.addListener(new LearningListener(convolutionNetwork, testSet));
backPropagation.ErrorFunction = new MeanSquaredError();
convolutionNetwork.LearningRule = backPropagation;
convolutionNetwork.learn(trainSet);
Evaluation.runFullEvaluation(convolutionNetwork, testSet);
}
catch (IOException e)
{
Console.WriteLine(e.ToString());
Console.Write(e.StackTrace);
}
}