Package org.encog.neural.networks.layers

Examples of org.encog.neural.networks.layers.BasicLayer

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        network = new BasicNetwork();
    }

    public int init() {
        int success;
        Layer outputLayer = new BasicLayer(new ActivationSigmoid(), true, 6);
        Layer hiddenLayer1 = new BasicLayer(new ActivationSigmoid(), true, 6);
        Layer inputLayer = new BasicLayer(new ActivationSigmoid(), false, 4);

        Synapse synapse1 = new WeightedSynapse(hiddenLayer1, outputLayer);
        Synapse synapse2 = new WeightedSynapse(inputLayer, hiddenLayer1);

        hiddenLayer1.addSynapse(synapse1);
        inputLayer.addSynapse(synapse2);

        network.tagLayer("INPUT", inputLayer);
        network.tagLayer("OUTPUT", outputLayer);

        network.getStructure().finalizeStructure();
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   *
   * @return The Elman neural network.
   */
  @Override
  public final MLMethod generate() {
    BasicLayer hidden, input;

    final BasicNetwork network = new BasicNetwork();
    network.addLayer(input = new BasicLayer(this.activation, true,
        this.inputNeurons));
    network.addLayer(hidden = new BasicLayer(this.activation, true,
        this.hiddenNeurons));
    network.addLayer(new BasicLayer(null, false, this.outputNeurons));
    input.setContextFedBy(hidden);
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }
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   * @return A Jordan neural network.
   */
  @Override
  public final MLMethod generate() {

    BasicLayer hidden, output;

    final BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null, true,
        this.inputNeurons));
    network.addLayer(hidden = new BasicLayer(this.activation, true,
        this.hiddenNeurons));
    network.addLayer(output = new BasicLayer(this.activation, false,
        this.outputNeurons));
    hidden.setContextFedBy(output);
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }
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  public final MLMethod generate() {

    if( this.activationOutput==null )
      this.activationOutput = this.activationHidden;
   
    final Layer input = new BasicLayer(null, true,
        this.inputNeurons);

    final BasicNetwork result = new BasicNetwork();
    result.addLayer(input);


    for (final Integer count : this.hidden) {

      final Layer hidden = new BasicLayer(this.activationHidden, true, count);

      result.addLayer(hidden);
    }

    final Layer output = new BasicLayer(this.activationOutput, false,
        this.outputNeurons);
    result.addLayer(output);

    result.getStructure().finalizeStructure();
    result.reset();
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   * @return The generated network.
   */
  public final MLMethod generate() {
    final BasicNetwork network = new BasicNetwork();

    final Layer inputLayer = new BasicLayer(new ActivationLinear(), true,
        this.inputNeurons);
    final Layer outputLayer = new BasicLayer(new ActivationLinear(), false,
        this.outputNeurons);

    network.addLayer(inputLayer);
    network.addLayer(outputLayer);
    network.getStructure().finalizeStructure();
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    }

    final FlatLayer[] flatLayers = new FlatLayer[this.layers.size()];

    for (int i = 0; i < this.layers.size(); i++) {
      final BasicLayer layer = (BasicLayer) this.layers.get(i);
      if (layer.getActivation() == null) {
        layer.setActivation(new ActivationLinear());
      }

      flatLayers[i] = layer;
    }
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    int failureCount = 0;
   
    for(int i=0;i<1000;i++) {
      // create a neural network, without using a factory
      BasicNetwork network = new BasicNetwork();
      network.addLayer(new BasicLayer(null, false, 2));
      network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
      network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
      network.getStructure().finalizeStructure();
      network.reset();
      (new ConsistentRandomizer(0,0.5,i)).randomize(network);

      // create training data
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  }
 
  public BasicNetwork createNetwork()
  {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(WINDOW_SIZE));
    network.addLayer(new BasicLayer(10));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }
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  public static double XOR_IDEAL[][] = { { 0.0 }, { 1.0 }, { 1.0 }, { 0.0 } };

  public void trainAndSave() throws IOException {
    System.out.println("Training XOR network to under 1% error rate.");
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();

    MLDataSet trainingSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
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  public final static String SQL_PWD = "xorpassword";
 
  public static void main(final String args[]) {
   
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(2));
    network.addLayer(new BasicLayer(1));
    network.getStructure().finalizeStructure();
    network.reset();

    MLDataSet trainingSet = new SQLNeuralDataSet(
        XORSQL.SQL,
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Related Classes of org.encog.neural.networks.layers.BasicLayer

  • com.wiieditor.neuralnetwork.NeuralNetworkWrapper
  • mechanics.NeuralNetwork
  • org.encog.engine.network.activation.ActivationTANH
  • org.encog.ensemble.ml.mlp.factory.MultiLayerPerceptronFactory
  • org.encog.examples.neural.activation.CustomActivation
  • org.encog.examples.neural.benchmark.MultiBench
  • org.encog.examples.neural.benchmark.SimpleBenchmark
  • org.encog.examples.neural.benchmark.TestConverge
  • org.encog.examples.neural.benchmark.ThreadCount
  • org.encog.examples.neural.cross.CrossValidateSunspot
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