Package org.encog.neural.networks.training.simple

Examples of org.encog.neural.networks.training.simple.TrainAdaline

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    pattern.setOutputNeurons(outputNeurons);
    BasicNetwork network = (BasicNetwork)pattern.generate();
   
    // train it
    MLDataSet training = generateTraining();
    MLTrain train = new TrainAdaline(network,training,0.01);
   
    int epoch = 1;
    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01);
   
    //
    System.out.println("Error:" + network.calculateError(training));
   
    // test it
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    pattern.setOutputNeurons(1);
    BasicNetwork network = (BasicNetwork)pattern.generate();
   
    // train it
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    MLTrain train = new TrainAdaline(network,training,0.01);
    NetworkUtil.testTraining(train,0.01);
  }
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  private void performADALINE(ProjectEGFile file, MLDataSet trainingData) {
    InputADALINE dialog = new InputADALINE();
    if (dialog.process()) {
      double learningRate = dialog.getLearningRate().getValue();

      MLTrain train = new TrainAdaline((BasicNetwork) file.getObject(),
          trainingData, learningRate);
      startup(file, train, dialog.getMaxError().getValue() / 100.0);
    }

  }
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    pattern.setOutputNeurons(1);
    BasicNetwork network = (BasicNetwork)pattern.generate();
   
    // train it
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    MLTrain train = new TrainAdaline(network,training,0.01);
    NetworkUtil.testTraining(training,train,0.01);
  }
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Related Classes of org.encog.neural.networks.training.simple.TrainAdaline

  • org.encog.examples.neural.adaline.AdalineDigits
  • org.encog.mathutil.error.ErrorCalculation
  • org.encog.ml.data.MLData
  • org.encog.neural.networks.logic.TestADALINE
  • org.encog.neural.NeuralNetworkError
  • org.encog.workbench.process.TrainBasicNetwork
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