simbad.piconode.core.node
Class NeuronForBackPropLearning
java.lang.Object
simbad.piconode.core.node.Node
simbad.piconode.core.node.ConnectedNode
simbad.piconode.core.node.NeuronalNode
simbad.piconode.core.node.Neuron
simbad.piconode.core.node.NeuronForBackPropLearning
- All Implemented Interfaces:
- java.io.Serializable, XmlRepresentation
public class NeuronForBackPropLearning
- extends Neuron
- implements XmlRepresentation, java.io.Serializable
The NeuronForBackPropLearning class extends a Neuron with methods that makes it possible
to embed such neurons into NeuralNetworks with Back-propagation learning ability (e.g.
FeedForwardNeuralNetworkForBackPropLearning).
- See Also:
- Serialized Form
Fields inherited from class simbad.piconode.core.node.Node |
_name, nGene |
Methods inherited from class simbad.piconode.core.node.NeuronalNode |
addIncomingArc, checkNetwork, displayInformation, getIncomingArcAt, getIncomingArcsListSize, getNetwork, getOutputValue, isNetworkInput, isNetworkOutput, setInputValue, setNetworkInputFlag, setNetworkOutputFlag, setValue |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
_learningNodeFlag
private boolean _learningNodeFlag
_deltaErrorSignal
private double _deltaErrorSignal
NeuronForBackPropLearning
public NeuronForBackPropLearning(FeedForwardNeuralNetworkForBackPropLearning __network)
NeuronForBackPropLearning
public NeuronForBackPropLearning(FeedForwardNeuralNetworkForBackPropLearning __network,
ActivationFunctionWithDerivative __activationFunctionObject)
NeuronForBackPropLearning
public NeuronForBackPropLearning(FeedForwardNeuralNetworkForBackPropLearning __network,
java.lang.String __name)
NeuronForBackPropLearning
public NeuronForBackPropLearning(FeedForwardNeuralNetworkForBackPropLearning __network,
ActivationFunctionWithDerivative __activationFunctionObject,
java.lang.String __name)
isLearningNode
public boolean isLearningNode()
- Overrides:
isLearningNode
in class NeuronalNode
setDeltaErrorSignal
private void setDeltaErrorSignal(double __value)
getDeltaErrorSignal
public double getDeltaErrorSignal()
backprop_computeErrorTermOutputNeuron
protected void backprop_computeErrorTermOutputNeuron(double __targetValue)
- [backpropagation step 1]
delta_k = derivativeOfActivationFunction(x) * ( targetValue_k - outputValue_k )
- Parameters:
__targetValue
-
backprop_computeErrorTermHiddenNeuron
protected void backprop_computeErrorTermHiddenNeuron()
- [backpropagation step 2]
delta_k = derivativeOfActivationFunction(x) * ( Sum_allAxons ( weight_k * DeltaErrorSignal_targetedNeuronByk ) )
setLearningNodeFlag
public void setLearningNodeFlag(boolean __flag)
- set the neuron status toward learning ("true" by default -- i.e. will be considered during back-propagation).
For instance, this is particularly useful if you intend to implement nolfi/parisi types of neural networks where only part of the network is modified during learning.
Thus, if flag is *false*, every arcs targeting this neuron will *not* considered during learning.
- Parameters:
__flag
-
toXml
public java.lang.String toXml()
- Thomas Darde
TER Visual Neuron Editor
- Specified by:
toXml
in interface XmlRepresentation
- Overrides:
toXml
in class Neuron
- Returns:
- an Xml representation of this object