Examples of org.encog.engine.network.activation.ActivationTANH

Package org.encog.engine.network.activation

Examples of org.encog.engine.network.activation.ActivationTANH

org.encog.engine.network.activation.ActivationTANH
The hyperbolic tangent activation function takes the curved shape of the hyperbolic tangent. This activation function produces both positive and negative output. Use this activation function if both negative and positive output is desired.

    } else if (t == Sigmoid.class) {
      slope = ((Sigmoid) transfer).getSlope();
      activation = new ActivationSigmoid();
    } else if (t == Tanh.class) {
      slope = ((Tanh) transfer).getSlope();
      activation = new ActivationTANH();
    } else
      return null;


    if (biasCount > 1)
      return null;

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   *
   * @param neuronCount
   *            How many neurons in this layer.
   */
  public BasicLayer(final int neuronCount) {
    this(new ActivationTANH(), true, neuronCount);
  }

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  public FlatNetwork(final int input, final int hidden1, final int hidden2,
      final int output, final boolean tanh) {


    final ActivationFunction linearAct = new ActivationLinear();
    FlatLayer[] layers;
    final ActivationFunction act = tanh ? new ActivationTANH()
        : new ActivationSigmoid();


    if ((hidden1 == 0) && (hidden2 == 0)) {
      layers = new FlatLayer[2];
      layers[0] = new FlatLayer(linearAct, input,

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      final boolean tanh) {
    final FeedForwardPattern pattern = new FeedForwardPattern();
    pattern.setInputNeurons(input);
    pattern.setOutputNeurons(output);
    if (tanh) {
      pattern.setActivationFunction(new ActivationTANH());
    } else {
      pattern.setActivationFunction(new ActivationSigmoid());
    }


    if (hidden1 > 0) {

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    if (name.equalsIgnoreCase(MLActivationFactory.AF_STEP)) {
      return new ActivationStep();
    }


    if (name.equalsIgnoreCase(MLActivationFactory.AF_TANH)) {
      return new ActivationTANH();
    }


    return null;
  }

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   */
  public FlatNetwork(final int input, final int hidden1, final int hidden2,
      final int output, final boolean tanh) {
    final double[] params = new double[1];
    FlatLayer[] layers;
    final ActivationFunction act = tanh ? new ActivationTANH()
        : new ActivationSigmoid();
    params[0] = 1; // slope


    if ((hidden1 == 0) && (hidden2 == 0)) {
      layers = new FlatLayer[2];

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import org.junit.Test;


public class TestActivationTANH extends TestCase {
  @Test
  public void testTANH() throws Throwable {
    ActivationTANH activation = new ActivationTANH();
    Assert.assertTrue(activation.hasDerivative());


    ActivationTANH clone = (ActivationTANH) activation.clone();
    Assert.assertNotNull(clone);


    double[] input = { 0.0  };


    activation.activationFunction(input,0,input.length);

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  {
    FeedForwardPattern pattern = new FeedForwardPattern();
    pattern.setInputNeurons(3);
    pattern.addHiddenLayer(50);
    pattern.setOutputNeurons(1);
    pattern.setActivationFunction(new ActivationTANH());
    BasicNetwork network = (BasicNetwork)pattern.generate();
    network.reset();
    return network;
  }

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      break;
    case 8:
      newActivation = new ActivationStep();
      break;            
    case 9:
      newActivation = new ActivationTANH();
      break;
    case 10:
      newActivation = new ActivationRamp();
      break;

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    if (dialog.process()) {
      JordanPattern jordan = new JordanPattern();
      jordan.setInputNeurons(dialog.getInputCount().getValue());
      jordan.addHiddenLayer(dialog.getHiddenCount().getValue());
      jordan.setOutputNeurons(dialog.getOutputCount().getValue());
      jordan.setActivationFunction(new ActivationTANH());
      return jordan.generate();
    } else
      return null;


  }

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Related Classes of org.encog.engine.network.activation.ActivationTANH

org.encog.engine.network.flat.FlatNetwork

org.encog.examples.neural.lunar.LunarLander

org.encog.ml.data.temporal.TestTemporal

org.encog.neural.activation.TestActivationTANH

org.encog.neural.flat.FlatNetwork

org.encog.neural.freeform.FreeformNetwork

org.encog.neural.networks.layers.BasicLayer

org.encog.plugin.system.SystemActivationPlugin

org.encog.util.simple.EncogUtility

org.encog.workbench.dialogs.activation.ActivationDialog

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