org.deeplearning4j.nn.conf.misc

Class DummyConfig

java.lang.Object

org.deeplearning4j.nn.conf.misc.DummyConfig

All Implemented Interfaces:

TrainingConfig

public class DummyConfig
extends Object
implements TrainingConfig

Constructor Summary

Constructors

Constructor and Description
DummyConfig()

Method Summary

Modifier and Type	Method and Description
GradientNormalization	getGradientNormalization()
double	getGradientNormalizationThreshold()
String	getLayerName()
List<Regularization>	getRegularizationByParam(String paramName) Get the regularization types (l1/l2/weight decay) for the given parameter.
IUpdater	getUpdaterByParam(String paramName) Get the updater for the given parameter.
boolean	isPretrainParam(String paramName) Is the specified parameter a layerwise pretraining only parameter? For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop. Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
void	setDataType(DataType dataType)

Methods inherited from class java.lang.Object

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

Constructor Detail

DummyConfig

public DummyConfig()

Method Detail

getLayerName

public String getLayerName()

Specified by:

getLayerName in interface TrainingConfig

Returns:

Name of the layer

getRegularizationByParam

public List<Regularization> getRegularizationByParam(String paramName)

Description copied from interface: TrainingConfig

Get the regularization types (l1/l2/weight decay) for the given parameter. Different parameters may have different regularization types.

Specified by:

getRegularizationByParam in interface TrainingConfig

Parameters:

paramName - Parameter name ("W", "b" etc)

Returns:

Regularization types (if any) for the specified parameter

isPretrainParam

public boolean isPretrainParam(String paramName)

Description copied from interface: TrainingConfig

Is the specified parameter a layerwise pretraining only parameter?
For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used during supervised backprop.
Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.

Specified by:

isPretrainParam in interface TrainingConfig

Parameters:

paramName - Parameter name/key

Returns:

True if the parameter is for layerwise pretraining only, false otherwise

getUpdaterByParam

public IUpdater getUpdaterByParam(String paramName)

Description copied from interface: TrainingConfig

Get the updater for the given parameter. Typically the same updater will be used for all updaters, but this is not necessarily the case

Specified by:

getUpdaterByParam in interface TrainingConfig

Parameters:

paramName - Parameter name

Returns:

IUpdater for the parameter

getGradientNormalization

public GradientNormalization getGradientNormalization()

Specified by:

getGradientNormalization in interface TrainingConfig

Returns:

The gradient normalization configuration

getGradientNormalizationThreshold

public double getGradientNormalizationThreshold()

Specified by:

getGradientNormalizationThreshold in interface TrainingConfig

Returns:

The gradient normalization threshold

setDataType

public void setDataType(DataType dataType)

Specified by:

setDataType in interface TrainingConfig