Examples of ActivationFunction

de.jungblut.math.activation.ActivationFunction
Squashing function interface to provide multiple activation functions, e.G. in the {@link MultilayerPerceptron}. @author thomas.jungblut
eas.simulation.brain.neural.functions.ActivationFunction
Interface modeling the activation function Phi: R=> R of a neuron. Do not confuse with TransferFunction which models the computation of the weighted input of a neuron. (In literature the term transfer function is often referring to the activation function as well.) IMPORTANT: Activation functions are constrained to return values from [0, 1]. @author Lukas König
eas.users.lukas.neuroCEGPM.simpleNeural.activationFunction.ActivationFunction
Interface modeling the activation function Phi: R=> R of a neuron. Do not confuse with TransferFunction which models the computation of the weighted input of a neuron. (In literature the term transfer function is often referring to the activation function as well.) IMPORTANT: Activation functions are constrained to return values from [0, 1]. @author Lukas König
org.dmg.pmml._40.ACTIVATIONFUNCTION
An implementation of the model object 'Vector Dictionary Type'.
The following features are implemented:
- {@link org.dmg.pmml._40.impl.VectorDictionaryTypeImpl#getExtension Extension}
- {@link org.dmg.pmml._40.impl.VectorDictionaryTypeImpl#getVectorFields Vector Fields}
- {@link org.dmg.pmml._40.impl.VectorDictionaryTypeImpl#getVectorInstance Vector Instance}
- {@link org.dmg.pmml._40.impl.VectorDictionaryTypeImpl#getNumberOfVectors Number Of Vectors}
@generated
org.encog.engine.network.activation.ActivationFunction
This interface allows various activation functions to be used with the neural network. Activation functions are applied to the output from each layer of a neural network. Activation functions scale the output into the desired range. Methods are provided both to process the activation function, as well as the derivative of the function. Some training algorithms, particularly back propagation, require that it be possible to take the derivative of the activation function. Not all activation functions support derivatives. If you implement an activation function that is not derivable then an exception should be thrown inside of the derivativeFunction method implementation. Non-derivable activation functions are perfectly valid, they simply cannot be used with every training algorithm.
tv.floe.metronome.classification.neuralnetworks.activation.ActivationFunction
@author josh
tv.floe.metronome.deeplearning.neuralnetwork.activation.ActivationFunction

Examples of org.dmg.pmml._40.ACTIVATIONFUNCTION

   * <!-- begin-user-doc -->
   * <!-- end-user-doc -->
   * @generated
   */
  public void setActivationFunction(ACTIVATIONFUNCTION newActivationFunction) {
    ACTIVATIONFUNCTION oldActivationFunction = activationFunction;
    activationFunction = newActivationFunction == null ? ACTIVATION_FUNCTION_EDEFAULT : newActivationFunction;
    boolean oldActivationFunctionESet = activationFunctionESet;
    activationFunctionESet = true;
    if (eNotificationRequired())
      eNotify(new ENotificationImpl(this, Notification.SET, _40Package.NEURAL_NETWORK_TYPE__ACTIVATION_FUNCTION, oldActivationFunction, activationFunction, !oldActivationFunctionESet));

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Examples of org.dmg.pmml._40.ACTIVATIONFUNCTION

   * <!-- begin-user-doc -->
   * <!-- end-user-doc -->
   * @generated
   */
  public void unsetActivationFunction() {
    ACTIVATIONFUNCTION oldActivationFunction = activationFunction;
    boolean oldActivationFunctionESet = activationFunctionESet;
    activationFunction = ACTIVATION_FUNCTION_EDEFAULT;
    activationFunctionESet = false;
    if (eNotificationRequired())
      eNotify(new ENotificationImpl(this, Notification.UNSET, _40Package.NEURAL_NETWORK_TYPE__ACTIVATION_FUNCTION, oldActivationFunction, ACTIVATION_FUNCTION_EDEFAULT, oldActivationFunctionESet));

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Examples of org.dmg.pmml._40.ACTIVATIONFUNCTION

   * <!-- end-user-doc -->
   * @generated
   */
  @Override
  public String getText(Object object) {
    ACTIVATIONFUNCTION labelValue = ((NeuralLayerType)object).getActivationFunction();
    String label = labelValue == null ? null : labelValue.toString();
    return label == null || label.length() == 0 ?
      getString("_UI_NeuralLayerType_type") :
      getString("_UI_NeuralLayerType_type") + " " + label;
  }

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


    Class<?> t = transferFunctions.iterator().next();


    double slope = 1;


    ActivationFunction activation = null;
    
    if (inputLayer)
      activation = new ActivationLinear();
    else if (t == Linear.class) {
      slope = ((Linear) transfer).getSlope();

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

  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
    
  private NEATNetwork create()
  {
    List<NEATNeuron> neurons = new ArrayList<NEATNeuron>();
    ActivationFunction afSigmoid = new ActivationSigmoid();
    ActivationFunction afStep = new ActivationStep();
    
    // create the neurons
    NEATNeuron input1 = new NEATNeuron(
        NEATNeuronType.Input, 
        1,

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


        flat.setActivationFunctions(new ActivationFunction[flat
            .getLayerCounts().length]);


        for (final String line : section.getLines()) {
          ActivationFunction af = null;
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          final String name = "org.encog.engine.network.activation."
              + cols.get(0);
          try {
            final Class<?> clazz = Class.forName(name);
            af = (ActivationFunction) clazz.newInstance();
          } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
          } catch (final InstantiationException e) {
            throw new PersistError(e);
          } catch (final IllegalAccessException e) {
            throw new PersistError(e);
          }


          for (int i = 0; i < af.getParamNames().length; i++) {
            af.setParam(i,
                CSVFormat.EG_FORMAT.parse(cols.get(i + 1)));
          }


          flat.getActivationFunctions()[index++] = af;
        }

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

   *            True if this is a tanh activation, false for sigmoid.
   */
  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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Examples of org.encog.engine.network.activation.ActivationFunction


        flat.setActivationFunctions(new ActivationFunction[flat
            .getLayerCounts().length]);


        for (final String line : section.getLines()) {
          ActivationFunction af = null;
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          final String name = "org.encog.engine.network.activation."
              + cols.get(0);
          try {
            final Class<?> clazz = Class.forName(name);
            af = (ActivationFunction) clazz.newInstance();
          } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
          } catch (final InstantiationException e) {
            throw new PersistError(e);
          } catch (final IllegalAccessException e) {
            throw new PersistError(e);
          }


          for (int i = 0; i < af.getParamNames().length; i++) {
            af.setParam(i,
                CSVFormat.EG_FORMAT.parse(cols.get(i + 1)));
          }


          flat.getActivationFunctions()[index++] = af;
        }

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

    } else {
      name = fn.toLowerCase();
      params = new double[0];
    }


    ActivationFunction af = allocateAF(name);
    
    if( af==null ) {
      return null;
    }


    if (af.getParamNames().length != params.length) {
      throw new EncogError(name + " expected "
          + af.getParamNames().length + ", but " + params.length
          + " were provided.");
    }


    for (int i = 0; i < af.getParamNames().length; i++) {
      af.setParam(i, params[i]);
    }


    return af;
  }

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

   */
  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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