Examples of FlatNetwork

org.encog.engine.network.flat.FlatNetwork
Implements a flat (vector based) neural network in the Encog Engine. This is meant to be a very highly efficient feedforward, or simple recurrent, neural network. It uses a minimum of objects and is designed with one principal in mind-- SPEED. Readability, code reuse, object oriented programming are all secondary in consideration. Vector based neural networks are also very good for GPU processing. The flat network classes will make use of the GPU if you have enabled GPU processing. See the Encog class for more info.
org.encog.neural.flat.FlatNetwork
Implements a flat (vector based) neural network in the Encog Engine. This is meant to be a very highly efficient feedforward, or simple recurrent, neural network. It uses a minimum of objects and is designed with one principal in mind-- SPEED. Readability, code reuse, object oriented programming are all secondary in consideration. Vector based neural networks are also very good for GPU processing. The flat network classes will make use of the GPU if you have enabled GPU processing. See the Encog class for more info.

Examples of org.encog.neural.flat.FlatNetwork


    return sw.getElapsedMilliseconds();
  }


  public static long BenchmarkEncogFlat(double[][] input, double[][] output) {
    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);


    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);

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Examples of org.encog.neural.flat.FlatNetwork


  public static void main(String[] args) {
    
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);


    FlatNetwork network = createNetwork();


    System.out.println("Starting Weights:");
    displayWeights(network);
    evaluate(network,trainingSet);

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Examples of org.encog.neural.flat.FlatNetwork


  public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };


  public static void main(final String args[]) {
    
    FlatNetwork network = new FlatNetwork(2,4,0,1,false);
    network.randomize();
    
    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    
    
    TrainFlatNetworkResilient train = new TrainFlatNetworkResilient(network,trainingSet);
    
    //Encog.getInstance().initCL();
    //train.setTargetDevice(Encog.getInstance().getCL().getDevices().get(0));
    
    int epoch = 1;


    do {
      train.iteration();
      System.out
          .println("Epoch #" + epoch + " Error:" + train.getError());
      epoch++;
    } while(train.getError() > 0.01 );


    double[] output = new double[1];
    // test the neural network
    System.out.println("Neural Network Results:");
    for(MLDataPair pair: trainingSet ) {
      double[] input = pair.getInput().getData();
      network.compute(input, output);
      System.out.println(input[0] + "," + input[1] + ":" + output[0]);
    }
  }

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Examples of org.encog.neural.flat.FlatNetwork

  public static final int HIDDEN_COUNT = 20;
  public static final int ITERATIONS = 10;
  public static final int AVG_COUNT = 20;


  public static long benchmarkEncogFlat(double[][] input, double[][] output) {
    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);


    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);

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Examples of org.encog.neural.flat.FlatNetwork

    layer1.setBiasActivation(0.5);
    layer2.setBiasActivation(-1.0);
    network.getStructure().finalizeStructure();
    network.reset();
    
    FlatNetwork flat = network.getStructure().getFlat();
    
    Assert.assertNotNull(flat);
    double[] layerOutput = flat.getLayerOutput();
    Assert.assertEquals(-1, layerOutput[5], 2 );
    Assert.assertEquals(0.5, layerOutput[8], 2 );  
  }

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Examples of org.encog.neural.flat.FlatNetwork

    network.reset();
    
    network.setLayerBiasActivation(0,0.5);
    network.setLayerBiasActivation(1,-1.0);
    
    FlatNetwork flat = network.getStructure().getFlat();
    
    Assert.assertNotNull(flat);
    double[] layerOutput = flat.getLayerOutput();
    Assert.assertEquals(layerOutput[5], -1.0);
    Assert.assertEquals(layerOutput[8], 0.5);  
  }

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Examples of org.encog.neural.flat.FlatNetwork

      report.header("Context Target Offset");
      report.header("Context Count");
      report.endRow();


      BasicNetwork network = (BasicNetwork) method;
      FlatNetwork flat = network.getStructure().getFlat();
      int layerCount = network.getLayerCount();


      for (int l = 0; l < layerCount; l++) {
        report.beginRow();
        StringBuilder str = new StringBuilder();
        str.append(Format.formatInteger(l + 1));
        if (l == 0) {
          str.append(" (Output)");
        } else if (l == network.getLayerCount() - 1) {
          str.append(" (Input)");
        }
        report.cell(str.toString());
        report.cell(Format.formatInteger(flat.getLayerCounts()[l]));
        report.cell(Format.formatInteger(flat.getLayerFeedCounts()[l]));
        report.cell(flat.getActivationFunctions()[l].getClass()
            .getSimpleName());
        report.cell(Format.formatDouble(flat.getBiasActivation()[l], 4));
        report.cell(Format.formatInteger(flat.getContextTargetSize()[l]));
        report.cell(Format.formatInteger(flat.getContextTargetOffset()[l]));
        report.cell(Format.formatInteger(flat.getLayerContextCount()[l]));
        report.endRow();
      }
      report.endTable();
    }

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Examples of org.encog.neural.flat.FlatNetwork

  public static final int HIDDEN_COUNT = 200;
  public static final int ITERATIONS = 10;
  public static final int AVG_COUNT = 20;


  public static long benchmarkEncogFlat(double[][] input, double[][] output) {
    FlatNetwork network = new FlatNetwork(input[0].length, HIDDEN_COUNT, 0,
        output[0].length, false);
    network.randomize();
    BasicMLDataSet trainingSet = new BasicMLDataSet(input, output);


    TrainFlatNetworkBackPropagation train = new TrainFlatNetworkBackPropagation(
        network, trainingSet, 0.7, 0.7);

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Examples of org.encog.neural.flat.FlatNetwork

    double[] p = null;


    if (methodFile.exists()) {
      method = (MLMethod) EncogDirectoryPersistence
          .loadObject(methodFile);
      final FlatNetwork flat = ((BasicNetwork) method).getFlat();


      contextTargetOffset = flat.getContextTargetOffset();
      contextTargetSize = flat.getContextTargetSize();
      hasContext = flat.getHasContext();
      inputCount = flat.getInputCount();
      layerContextCount = flat.getLayerContextCount();
      layerCounts = flat.getLayerCounts();
      layerFeedCounts = flat.getLayerFeedCounts();
      layerIndex = flat.getLayerIndex();
      layerOutput = flat.getLayerOutput();
      layerSums = flat.getLayerSums();
      outputCount = flat.getOutputCount();
      weightIndex = flat.getWeightIndex();
      weights = flat.getWeights();
      activation = createActivations(flat);
      p = createParams(flat);
      neuronCount = flat.getLayerOutput().length;
      layerCount = flat.getLayerCounts().length;
    }


    setIndentLevel(2);
    indentIn();
    addNameValue("string EXPORT_FILENAME", "\"" + processFile.getName()

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Examples of org.encog.neural.flat.FlatNetwork

    if (!(method instanceof MLFactory)) {
      throw new EncogError("Code generation not yet supported for: "
          + method.getClass().getName());
    }


    final FlatNetwork flat = ((ContainsFlat) method).getFlat();


    // header
    final StringBuilder line = new StringBuilder();
    line.append("public static MLMethod ");
    line.append(node.getName());
    line.append("() {");
    indentLine(line.toString());


    // create factory
    line.setLength(0);


    addLine("var network = ENCOG.BasicNetwork.create( null );");
    addLine("network.inputCount = " + flat.getInputCount() + ";");
    addLine("network.outputCount = " + flat.getOutputCount() + ";");
    addLine("network.layerCounts = "
        + toSingleLineArray(flat.getLayerCounts()) + ";");
    addLine("network.layerContextCount = "
        + toSingleLineArray(flat.getLayerContextCount()) + ";");
    addLine("network.weightIndex = "
        + toSingleLineArray(flat.getWeightIndex()) + ";");
    addLine("network.layerIndex = "
        + toSingleLineArray(flat.getLayerIndex()) + ";");
    addLine("network.activationFunctions = "
        + toSingleLineArray(flat.getActivationFunctions()) + ";");
    addLine("network.layerFeedCounts = "
        + toSingleLineArray(flat.getLayerFeedCounts()) + ";");
    addLine("network.contextTargetOffset = "
        + toSingleLineArray(flat.getContextTargetOffset()) + ";");
    addLine("network.contextTargetSize = "
        + toSingleLineArray(flat.getContextTargetSize()) + ";");
    addLine("network.biasActivation = "
        + toSingleLineArray(flat.getBiasActivation()) + ";");
    addLine("network.beginTraining = " + flat.getBeginTraining() + ";");
    addLine("network.endTraining=" + flat.getEndTraining() + ";");
    addLine("network.weights = WEIGHTS;");
    addLine("network.layerOutput = "
        + toSingleLineArray(flat.getLayerOutput()) + ";");
    addLine("network.layerSums = " + toSingleLineArray(flat.getLayerSums())
        + ";");


    // return
    addLine("return network;");

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