public class RpropExtender extends DeltaRuleExtender
Modifier and Type | Field and Description |
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protected double[][] |
theDeltas
Each weight has its own individual update-value (delta_ij(t)) represented
by the next object.
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protected double[][] |
thePreviousGradients
The gradient pattern of the previous epoch (dE(t-1)/dW_ij).
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protected RpropParameters |
theRpropParameters
The parameters for the RPROP learning algorithm.
|
protected double[][] |
theSummedGradients
The current som of the gradients of all patterns seen so far.
|
Constructor and Description |
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RpropExtender()
Creates a new instance of RpropExtender
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Modifier and Type | Method and Description |
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double |
getDelta(double[] currentGradientOuts,
int j,
double aPreviousDelta)
Computes the delta value for a bias.
|
double |
getDelta(double[] currentInps,
int j,
double[] currentPattern,
int k,
double aPreviousDelta)
Computes the delta value for a weight.
|
RpropParameters |
getParameters()
Gets the parameters of this learning algorithm.
|
void |
postBiasUpdate(double[] currentGradientOuts)
Gives extenders a change to do some post-computing after the
biases are updated.
|
void |
postWeightUpdate(double[] currentPattern,
double[] currentInps)
Gives extenders a change to do some post-computing after the
weights are updated.
|
void |
preBiasUpdate(double[] currentGradientOuts)
Gives extenders a change to do some pre-computing before the
biases are updated.
|
void |
preWeightUpdate(double[] currentPattern,
double[] currentInps)
Gives extenders a change to do some pre-computing before the
weights are updated.
|
void |
reinit()
(Re)Initializes this RPROP learner.
|
void |
setParameters(RpropParameters aParameters)
Sets the parameters for this learning algorithm.
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protected double |
sign(double d)
Gets the sign of a double.
|
getLearner, isEnabled, setEnabled, setLearner
protected double[][] theDeltas
protected double[][] thePreviousGradients
protected RpropParameters theRpropParameters
protected double[][] theSummedGradients
public void reinit()
public double getDelta(double[] currentGradientOuts, int j, double aPreviousDelta)
DeltaRuleExtender
getDelta
in class DeltaRuleExtender
currentGradientOuts
- the back propagated gradients.j
- the index of the bias.aPreviousDelta
- a delta value calculated by a previous delta extender.public double getDelta(double[] currentInps, int j, double[] currentPattern, int k, double aPreviousDelta)
DeltaRuleExtender
getDelta
in class DeltaRuleExtender
currentInps
- the forwarded input.j
- the input index of the weight.currentPattern
- the back propagated gradients.k
- the output index of the weight.aPreviousDelta
- a delta value calculated by a previous delta extender.public void postBiasUpdate(double[] currentGradientOuts)
LearnerExtender
postBiasUpdate
in class LearnerExtender
currentGradientOuts
- the back propagated gradients.public void postWeightUpdate(double[] currentPattern, double[] currentInps)
LearnerExtender
postWeightUpdate
in class LearnerExtender
currentPattern
- the back propagated gradients.currentInps
- the forwarded input.public void preBiasUpdate(double[] currentGradientOuts)
LearnerExtender
preBiasUpdate
in class LearnerExtender
currentGradientOuts
- the back propagated gradients.public void preWeightUpdate(double[] currentPattern, double[] currentInps)
LearnerExtender
preWeightUpdate
in class LearnerExtender
currentPattern
- the back propagated gradients.currentInps
- the forwarded input.public RpropParameters getParameters()
public void setParameters(RpropParameters aParameters)
aParameters
- the parameters for this learning algorithm.protected double sign(double d)
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