Package org.encog.neural.networks.layers

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

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  }

  @Override
  public MLMethod createML(int inputs, int outputs) {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(activation,false,inputs)); //(inputs));
    for (Integer layerSize: layers)
      network.addLayer(new BasicLayer(activation,true,layerSize));
    network.addLayer(new BasicLayer(activation,true,outputs));
    network.getStructure().finalizeStructure();
    network.reset();
    return network;
  }
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  }
 
  public void testPersistMediumEG()
  {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,10));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,10));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,10));
    network.getStructure().finalizeStructure();
    network.reset();

    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    BasicNetwork network2 = (BasicNetwork)EncogDirectoryPersistence.loadObject(EG_FILENAME);
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  }
 
  public void testPersistLargeEG()
  {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,200));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,200));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,200));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,200));
    network.getStructure().finalizeStructure();
    network.reset();

    EncogDirectoryPersistence.saveObject(EG_FILENAME, network);
    BasicNetwork network2 = (BasicNetwork)EncogDirectoryPersistence.loadObject(EG_FILENAME);
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    }
   
    public static BasicNetwork createThreeLayerNet()
    {
      BasicNetwork network = new BasicNetwork();
      network.addLayer(new BasicLayer(2));
      network.addLayer(new BasicLayer(3));
      network.addLayer(new BasicLayer(1));
      network.getStructure().finalizeStructure();
      network.reset();
      return network;
    }
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  public void testLayerOutput()
  {
    Layer layer1, layer2;
    BasicNetwork network = new BasicNetwork();
    network.addLayer(layer1 = new BasicLayer(null, true,2));
    network.addLayer(layer2 = new BasicLayer(new ActivationSigmoid(), true,4));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false,1));
    int i = 0;
    i++;
    layer1.setBiasActivation(0.5);
    layer2.setBiasActivation(-1.0);
    network.getStructure().finalizeStructure();
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  }
 
  public void testLayerOutputPostFinalize()
  {
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null, true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), true,4));
    network.addLayer(new BasicLayer(new ActivationSigmoid(), false,1));

    network.getStructure().finalizeStructure();
    network.reset();
   
    network.setLayerBiasActivation(0,0.5);
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public class TestFreeform extends TestCase {
 
  public void testCreation() {
    // create a neural network, without using a factory
    BasicNetwork basicNetwork = new BasicNetwork();
    basicNetwork.addLayer(new BasicLayer(null, true, 2));
    basicNetwork.addLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
    basicNetwork
        .addLayer(new BasicLayer(new ActivationSigmoid(), false, 1));
    basicNetwork.getStructure().finalizeStructure();
    basicNetwork.reset();

    FreeformNetwork freeformNetwork = new FreeformNetwork(basicNetwork);
    Assert.assertEquals(basicNetwork.getInputCount(),
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  }
 
  public void testSingleOutput() {
   
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
   
    (new ConsistentRandomizer(-1,1)).randomize(network);
   
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);   
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  }
 
  public void testDualOutput() {
   
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,2));
    network.getStructure().finalizeStructure();
   
    (new ConsistentRandomizer(-1,1)).randomize(network);
   
    MLDataSet trainingData = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL2);   
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  public static BasicNetwork createXORNetworkUntrained()
  {
    // random matrix data.  However, it provides a constant starting point
    // for the unit tests.   
    BasicNetwork network = new BasicNetwork();
    network.addLayer(new BasicLayer(null,true,2));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),true,4));
    network.addLayer(new BasicLayer(new ActivationSigmoid(),false,1));
    network.getStructure().finalizeStructure();
   
    (new ConsistentRandomizer(-1,1)).randomize(network);
   
    return network;
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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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