org.joone.engine
Class TanhLayer

java.lang.Object
  org.joone.engine.Layer
      org.joone.engine.SimpleLayer
          org.joone.engine.TanhLayer

All Implemented Interfaces:

java.io.Serializable, java.lang.Runnable, Learnable, LearnableLayer, NeuralLayer, Inspectable

public class TanhLayer
extends SimpleLayer
implements LearnableLayer

Layer that applies the tangent hyperbolic transfer function to its input patterns

See Also:

Serialized Form

Field Summary

Fields inherited from class org.joone.engine.Layer

bias, gradientInps, gradientOuts, inps, inputPatternListeners, learnable, learning, m_batch, monitor, myLearner, outputPatternListeners, outs, running, step, STOP_FLAG

Constructor Summary

TanhLayer()
default constructor

TanhLayer(java.lang.String name)

Method Summary

void	backward(double[] pattern) Reverse transfer function of the component.
void	forward(double[] pattern) Transfer function to recall a result on a trained net
double	getDefaultState() Return the default state of a node in this layer, such as 0 for a tanh or 0.5 for a sigmoid layer
double	getDerivative(int i) Similar to the backward message and used by RTRL
double	getFlatSpotConstant() Gets the flat spot constant.
Learner	getLearner() Deprecated. - Used only for backward compatibility
double	getMaximumState() Return maximum value of a node in this layer
double	getMinimumState() Return minimum value of a node in this layer
void	setFlatSpotConstant(double aConstant) Sets the constant to overcome the flat spot problem.

Methods inherited from class org.joone.engine.SimpleLayer

getLearningRate, getLrate, getMomentum, setDimensions, setLrate, setMomentum, setMonitor

Methods inherited from class org.joone.engine.Layer

addInputSynapse, addNoise, addOutputSynapse, adjustSizeToFwdPattern, adjustSizeToRevPattern, check, checkInputEnabled, checkInputs, checkOutputs, copyInto, finalize, fireFwdGet, fireFwdPut, fireRevGet, fireRevPut, fwdRun, getAllInputs, getAllOutputs, getBias, getDimension, getLastGradientInps, getLastGradientOuts, getLastInputs, getLastOutputs, getLayerName, getMonitor, getRows, getThreadMonitor, hasStepCounter, init, initLearner, InspectableTitle, Inspections, isInputLayer, isOutputLayer, isRunning, join, randomize, randomizeBias, randomizeWeights, removeAllInputs, removeAllOutputs, removeInputSynapse, removeListener, removeOutputSynapse, resetInputListeners, revRun, run, setAllInputs, setAllOutputs, setBias, setConnDimensions, setInputDimension, setInputSynapses, setLastInputs, setLastOutputs, setLayerName, setOutputDimension, setOutputSynapses, setRows, start, stop, sumBackInput, sumInput, toString

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface org.joone.engine.Learnable

getMonitor, initLearner

Methods inherited from interface org.joone.engine.NeuralLayer

addInputSynapse, addNoise, addOutputSynapse, check, copyInto, getAllInputs, getAllOutputs, getBias, getLayerName, getMonitor, getRows, isRunning, removeAllInputs, removeAllOutputs, removeInputSynapse, removeOutputSynapse, setAllInputs, setAllOutputs, setBias, setLayerName, setMonitor, setRows, start

Constructor Detail

TanhLayer

public TanhLayer()

default constructor

TanhLayer

public TanhLayer(java.lang.String name)

Method Detail

backward

public void backward(double[] pattern)

Description copied from class: Layer

Reverse transfer function of the component.

Overrides:

backward in class SimpleLayer

Parameters:

pattern - input pattern on which to apply the transfer function

See Also:

(double[])

getDerivative

public double getDerivative(int i)

Similar to the backward message and used by RTRL

Specified by:

getDerivative in class Layer

forward

public void forward(double[] pattern)

Description copied from class: Layer

Transfer function to recall a result on a trained net

Specified by:

forward in class Layer

Parameters:

pattern - input pattern to which to apply the rtransfer function

See Also:

(double[])

getLearner

public Learner getLearner()

Deprecated. - Used only for backward compatibility

Description copied from class: Layer

Returns the appropriate Learner object for this class depending on the Monitor.learningMode property value

Specified by:

getLearner in interface Learnable

Overrides:

getLearner in class Layer

Returns:

the Learner object if applicable, otherwise null

See Also:

Learnable.getLearner()

setFlatSpotConstant

public void setFlatSpotConstant(double aConstant)

Sets the constant to overcome the flat spot problem. This problem is described in: S.E. Fahlman, "An emperical study of learning speed in backpropagation with good scaling properties," Dept. Comput. Sci. Carnegie Mellon Univ., Pittsburgh, PA, Tech. Rep., CMU-CS-88-162, 1988. Setting this constant to 0 (default value), the derivative of the sigmoid function is unchanged (normal function). An good value for this constant might be 0.1.

Parameters:

aConstant -

getFlatSpotConstant

public double getFlatSpotConstant()

Gets the flat spot constant.

Returns:

the flat spot constant.

getDefaultState

public double getDefaultState()

Description copied from class: Layer

Return the default state of a node in this layer, such as 0 for a tanh or 0.5 for a sigmoid layer

Specified by:

getDefaultState in class Layer

getMinimumState

public double getMinimumState()

Description copied from class: Layer

Return minimum value of a node in this layer

Specified by:

getMinimumState in class Layer

getMaximumState

public double getMaximumState()

Description copied from class: Layer

Return maximum value of a node in this layer

Specified by:

getMaximumState in class Layer