Class BaseWrapperLayer
- java.lang.Object
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- org.deeplearning4j.nn.conf.layers.Layer
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- org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer
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- All Implemented Interfaces:
Serializable
,Cloneable
,TrainingConfig
- Direct Known Subclasses:
FrozenLayerWithBackprop
,LastTimeStep
,MaskZeroLayer
,TimeDistributed
public abstract class BaseWrapperLayer extends Layer
- See Also:
- Serialized Form
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Nested Class Summary
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Nested classes/interfaces inherited from class org.deeplearning4j.nn.conf.layers.Layer
Layer.Builder<T extends Layer.Builder<T>>
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Field Summary
Fields Modifier and Type Field Description protected Layer
underlying
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Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer
constraints, iDropout, layerName
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Constructor Summary
Constructors Modifier Constructor Description protected
BaseWrapperLayer()
BaseWrapperLayer(Layer underlying)
protected
BaseWrapperLayer(Layer.Builder builder)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description GradientNormalization
getGradientNormalization()
double
getGradientNormalizationThreshold()
LayerMemoryReport
getMemoryReport(InputType inputType)
This is a report of the estimated memory consumption for the given layerInputType
getOutputType(int layerIndex, InputType inputType)
For a given type of input to this layer, what is the type of the output?InputPreProcessor
getPreProcessorForInputType(InputType inputType)
For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriateInputPreProcessor
for this layer, such as aCnnToFeedForwardPreProcessor
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
setLayerName(String layerName)
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, getUpdaterByParam, initializeConstraints, instantiate, resetLayerDefaultConfig, setDataType
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Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface org.deeplearning4j.nn.api.TrainingConfig
getLayerName
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Field Detail
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underlying
protected Layer underlying
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Constructor Detail
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BaseWrapperLayer
protected BaseWrapperLayer(Layer.Builder builder)
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BaseWrapperLayer
protected BaseWrapperLayer()
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BaseWrapperLayer
public BaseWrapperLayer(Layer underlying)
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Method Detail
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initializer
public ParamInitializer initializer()
- Specified by:
initializer
in classLayer
- Returns:
- The parameter initializer for this model
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getOutputType
public InputType getOutputType(int layerIndex, InputType inputType)
Description copied from class:Layer
For a given type of input to this layer, what is the type of the output?- Specified by:
getOutputType
in classLayer
- Parameters:
layerIndex
- Index of the layerinputType
- Type of input for the layer- Returns:
- Type of output from the layer
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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
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getPreProcessorForInputType
public InputPreProcessor getPreProcessorForInputType(InputType inputType)
Description copied from class:Layer
For the given type of input to this layer, what preprocessor (if any) is required?
Returns null if no preprocessor is required, otherwise returns an appropriateInputPreProcessor
for this layer, such as aCnnToFeedForwardPreProcessor
- Specified by:
getPreProcessorForInputType
in classLayer
- Parameters:
inputType
- InputType to this layer- Returns:
- Null if no preprocessor is required, otherwise the type of preprocessor necessary for this layer/input combination
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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 interfaceTrainingConfig
- Specified by:
getRegularizationByParam
in classLayer
- Parameters:
paramName
- Parameter name ("W", "b" etc)- Returns:
- Regularization types (if any) for the specified parameter
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getGradientNormalization
public GradientNormalization getGradientNormalization()
- Returns:
- The gradient normalization configuration
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getGradientNormalizationThreshold
public double getGradientNormalizationThreshold()
- Returns:
- The gradient normalization threshold
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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 interfaceTrainingConfig
- Specified by:
isPretrainParam
in classLayer
- Parameters:
paramName
- Parameter name/key- Returns:
- True if the parameter is for layerwise pretraining only, false otherwise
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getMemoryReport
public LayerMemoryReport getMemoryReport(InputType inputType)
Description copied from class:Layer
This is a report of the estimated memory consumption for the given layer- Specified by:
getMemoryReport
in classLayer
- Parameters:
inputType
- Input type to the layer. Memory consumption is often a function of the input type- Returns:
- Memory report for the layer
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setLayerName
public void setLayerName(String layerName)
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