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);
}
