Package org.deeplearning4j.nn.conf.layers

Class NoParamLayer

java.lang.Object

org.deeplearning4j.nn.conf.layers.Layer

org.deeplearning4j.nn.conf.layers.NoParamLayer

All Implemented Interfaces:

Serializable, Cloneable, TrainingConfig

Direct Known Subclasses:

ActivationLayer, BaseUpsamplingLayer, Cropping1D, Cropping2D, Cropping3D, GlobalPoolingLayer, MaskLayer, SpaceToBatchLayer, SpaceToDepthLayer, Subsampling3DLayer, SubsamplingLayer, ZeroPadding1DLayer, ZeroPadding3DLayer, ZeroPaddingLayer

public abstract class NoParamLayer
extends Layer

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class org.deeplearning4j.nn.conf.layers.Layer

Layer.Builder<T extends Layer.Builder<T>>

Field Summary

Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer

constraints, iDropout, layerName

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	NoParamLayer(Layer.Builder builder)

Method Summary

Modifier and Type	Method	Description
GradientNormalization	getGradientNormalization()
double	getGradientNormalizationThreshold()
List<Regularization>	getRegularizationByParam(String paramName)	Get the regularization types (l1/l2/weight decay) for the given parameter.
ParamInitializer	initializer()
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	setNIn(InputType inputType, boolean override)	Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer

clone, getMemoryReport, getOutputType, getPreProcessorForInputType, getUpdaterByParam, initializeConstraints, instantiate, resetLayerDefaultConfig, setDataType

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.deeplearning4j.nn.api.TrainingConfig

getLayerName

Constructor Detail

NoParamLayer

protected NoParamLayer(Layer.Builder builder)

Method Detail

initializer

public ParamInitializer initializer()

Specified by:

initializer in class Layer

Returns:

The parameter initializer for this model

setNIn

public void setNIn(InputType inputType,
                   boolean override)

Description copied from class: Layer

Set the nIn value (number of inputs, or input channels for CNNs) based on the given input type

Specified by:

setNIn in class Layer

Parameters:

inputType - Input type for this layer

override - If false: only set the nIn value if it's not already set. If true: set it regardless of whether it's already set or not.

getRegularizationByParam

public List<Regularization> getRegularizationByParam(String paramName)

Description copied from class: Layer

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

Specified by:

getRegularizationByParam in class Layer

Parameters:

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

Returns:

Regularization types (if any) for the specified parameter

getGradientNormalization

public GradientNormalization getGradientNormalization()

Returns:

The gradient normalization configuration

getGradientNormalizationThreshold

public double getGradientNormalizationThreshold()

Returns:

The gradient normalization threshold

isPretrainParam

public boolean isPretrainParam(String paramName)

Description copied from class: Layer

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

Specified by:

isPretrainParam in class Layer

Parameters:

paramName - Parameter name/key

Returns:

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