private static BackPropagation createLearningRule() { BackPropagation learningRule = new BackPropagation(); learningRule.MaxIterations = 50; learningRule.MaxError = 0.0001; return(learningRule); }
public virtual void handleLearningEvent(LearningEvent @event) { BackPropagation bp = (BackPropagation)@event.Source; LOG.info("Current iteration: " + bp.CurrentIteration); LOG.info("Error: " + bp.TotalNetworkError); LOG.info("Calculation time: " + (DateTimeHelperClass.CurrentUnixTimeMillis() - start) / 1000.0); // neuralNetwork.save(bp.getCurrentIteration() + "CNN_MNIST" + bp.getCurrentIteration() + ".nnet"); start = DateTimeHelperClass.CurrentUnixTimeMillis(); // NeuralNetworkEvaluationService.completeEvaluation(neuralNetwork, testSet); }
//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); }