Package zdenekdrahos.AI.Training.Builder

Examples of zdenekdrahos.AI.Training.Builder.TrainingBuilder

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        in.learningRate = 0.1;
        in.momentum = 0;
        in.inputs = input;
        in.targets = output;

        ITrainingBuilder trainBuilder = new TrainingBuilder();
        ITraining train = trainBuilder.build();
        TrainingOutput out = new TrainingOutput(0.00005);
        train.train(network, in, out);
        System.out.printf("Solution in %d iteration, minError = %f, lastError: %f\n\n", out.lastIterationNumber, out.minError, out.lastError);

        final XYSeries series2 = new XYSeries("Prediction");
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    private TrainingInput input;
    private INeuralNetwork network;
    private INetworkBuilder networkBuilder;

    public BackPropagationController() {
        ITrainingBuilder trainBuilder = new TrainingBuilder();
        train = trainBuilder.build();
        networkBuilder = new NetworkBuilder();
    }
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        in.learningRate = 0.8;
        in.momentum = 0.798;
        in.inputs = input;
        in.targets = output;
       
        ITrainingBuilder trainBuilder = new TrainingBuilder();
        ITraining train = trainBuilder.build();
        TrainingOutput out = new TrainingOutput(0.01);
        train.train(network, in, out);
        System.out.printf("Solution in %d iteration, minError = %f, lastError: %f\n\n", out.lastIterationNumber, out.minError, out.lastError);       
        //Printing.printNetworkWeights(network);
       
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Related Classes of zdenekdrahos.AI.Training.Builder.TrainingBuilder

  • zdenekdrahos.AI.BackPropagation.BackPropagation
  • zdenekdrahos.AI.FeedForward.FeedForward
  • zdenekdrahos.AI.Training.Sets.DataSeparator
  • zdenekdrahos.AI.Training.Simulation.Simulation
  • zdenekdrahos.AI.Training.Training
  • zdenekdrahos.AI.Training.TrainingSettings
  • zdenekdrahos.App.Controller.BackPropagationController
  • zdenekdrahos.Statistics.Characteristics.Mean
  • zdenekdrahos.Statistics.Errors.LeastSquares
  • zdenekdrahos.Testing.MainTrainingData
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