Package org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold

Class FixedThresholdAlgorithm

java.lang.Object

org.deeplearning4j.optimize.solvers.accumulation.encoding.threshold.FixedThresholdAlgorithm

All Implemented Interfaces:

Serializable, ThresholdAlgorithm

public class FixedThresholdAlgorithm
extends Object
implements ThresholdAlgorithm

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	FixedThresholdAlgorithm.FixedAlgorithmThresholdReducer

Constructor Summary

Constructors

Constructor	Description
FixedThresholdAlgorithm()

Method Summary

Modifier and Type	Method	Description
double	calculateThreshold(int iteration, int epoch, Double lastThreshold, Boolean lastWasDense, Double lastSparsityRatio, INDArray updatesPlusResidual)
FixedThresholdAlgorithm	clone()
ThresholdAlgorithmReducer	newReducer()	Create a new ThresholdAlgorithmReducer.

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

FixedThresholdAlgorithm

public FixedThresholdAlgorithm()

Method Detail

calculateThreshold

public double calculateThreshold(int iteration,
                                 int epoch,
                                 Double lastThreshold,
                                 Boolean lastWasDense,
                                 Double lastSparsityRatio,
                                 INDArray updatesPlusResidual)

Specified by:

calculateThreshold in interface ThresholdAlgorithm

Parameters:

iteration - Current neural network training iteration

epoch - Current neural network training epoch

lastThreshold - The encoding threshold used in the last iteration - if available. May be null for first iteration in an epoch (where no 'last iteration' value is available)

lastWasDense - Whether the last encoding was dense (true) or sparse (false). May be null for the first iteration in an epoch (where no 'last iteration' value is available)

lastSparsityRatio - The sparsity ratio of the last iteration. Sparsity ratio is defined as numElements(encoded)/length(updates). A sparsity ratio of 1.0 would mean all entries present in encoded representation; a sparsity ratio of 0.0 would mean the encoded vector did not contain any values. Note: when the last encoding was dense, lastSparsityRatio is always null - this means that the sparsity ratio is larger than 1/16 = 0.0625

updatesPlusResidual - The actual array (updates plus residual) that will be encoded using the threshold calculated/returned by this method

Returns:

newReducer

public ThresholdAlgorithmReducer newReducer()

Description copied from interface: ThresholdAlgorithm

Create a new ThresholdAlgorithmReducer. Note that implementations should NOT add the curret ThresholdAlgorithm to it.

Specified by:

newReducer in interface ThresholdAlgorithm

Returns:

ThresholdAlgorithmReducer

clone

public FixedThresholdAlgorithm clone()

Specified by:

clone in interface ThresholdAlgorithm

Overrides:

clone in class Object

Returns:

A clone of the current threshold algorithm