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 de.jungblut.math.activation.ActivationFunction

    for (int i = 0; i < layers; i++) {
      MatrixWritable mv = new MatrixWritable();
      mv.readFields(in);
      array[i] = mv.getMatrix();
    }
    ActivationFunction func = null;
    try {
      func = (ActivationFunction) Class.forName(in.readUTF()).newInstance();
    } catch (InstantiationException | IllegalAccessException
        | ClassNotFoundException e) {
      throw new RuntimeException(e);

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Examples of eas.simulation.brain.neural.functions.ActivationFunction

    }
    
    public void propagate() {
        this.neuronVector.mult(this.weightMatrix);
        for (int i = 0; i < this.neuronVector.getRowCount(); i++) {
            ActivationFunction act = this.getActivationFunction(i);
            this.neuronVector.set(0, i, act.activationPhi(this.neuronVector.get(0, i)));
        }
    }

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Examples of eas.simulation.brain.neural.functions.ActivationFunction

    public void propagateReverse() {
        if (this.inverseWeightMatrix == null) {
            this.createInverseWeightMatrix();
        }
        for (int i = 0; i < this.neuronVector.getRowCount(); i++) {
            ActivationFunction act = this.getActivationFunction(i);
            
            try {
                this.neuronVector.set(0, i, act.inverseActivationPhi(this.neuronVector.get(0, i)));
            } catch (InversionNotSupportedException e) {}
        }
        this.neuronVector.mult(this.inverseWeightMatrix);
    }

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Examples of eas.simulation.brain.neural.functions.ActivationFunction

    private void createInverseWeightMatrix() {
        this.inverseWeightMatrix = this.weightMatrix.inverse();
    }
    
    public ActivationFunction getActivationFunction(int neuronID) {
        ActivationFunction a = this.activationFunctions.get(neuronID);
        if (a == null) {
            return this.standardActFct;
        } else {
            return a;
        }

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Examples of eas.users.lukas.neuroCEGPM.simpleNeural.activationFunction.ActivationFunction

        if (endTime - startTime > longTime) {
            GlobalVariables.getPrematureParameters().logWarning("Very long matrix multiplication in propagate: " + (endTime - startTime));
        }


        for (int i = 0; i < this.neuronVector.getRowCount(); i++) {
            ActivationFunction act = this.getActivationFunction(i);
            this.neuronVector.set(0, i, act.activationPhi(this.neuronVector.get(0, i)));
        }
        
        if (normalize) {
            this.normalizeNeuronVector(maxNeuronValue);
        }

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Examples of eas.users.lukas.neuroCEGPM.simpleNeural.activationFunction.ActivationFunction

    public void propagateReverse(boolean normalize) {
        if (this.inverseWeightMatrix == null) {
            this.createInverseWeightMatrix();
        }
        for (int i = 0; i < this.neuronVector.getRowCount(); i++) {
            ActivationFunction act = this.getActivationFunction(i);
            
            try {
                this.neuronVector.set(0, i, act.inverseActivationPhi(this.neuronVector.get(0, i)));
            } catch (InversionNotSupportedException e) {}
        }
        
        long startTime = System.currentTimeMillis();
        this.neuronVector.mult(this.inverseWeightMatrix);

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Examples of eas.users.lukas.neuroCEGPM.simpleNeural.activationFunction.ActivationFunction

    private void createInverseWeightMatrix() {
        this.inverseWeightMatrix = this.weightMatrix.inverse();
    }
    
    public ActivationFunction getActivationFunction(int neuronID) {
        ActivationFunction a = this.activationFunctions.get(neuronID);
        if (a == null) {
            return this.standardActFct;
        } else {
            return a;
        }

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Examples of eas.users.lukas.neuroCEGPM.simpleNeural.activationFunction.ActivationFunction

     * @param sequence  The sequence to translate.
     * 
     * @return  The translation.
     */
    public NeuroController translateGenomeToController(List<BigDecimal> sequence, boolean normalize) {
        ActivationFunction func = this.getStandardActFct();
        int scale = MatrixBigDecimal.GLOBAL_SCALE;
        MatrixBigDecimal.GLOBAL_SCALE = 10;
        this.setStandardActFct(new ActivationFunctionSQRT());
        
//        System.out.println();

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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_LAYER_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_LAYER_TYPE__ACTIVATION_FUNCTION, oldActivationFunction, ACTIVATION_FUNCTION_EDEFAULT, oldActivationFunctionESet));

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