Class ZeroPadding3DLayer
- java.lang.Object
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- org.deeplearning4j.nn.conf.layers.Layer
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- org.deeplearning4j.nn.conf.layers.NoParamLayer
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- org.deeplearning4j.nn.conf.layers.ZeroPadding3DLayer
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- All Implemented Interfaces:
Serializable
,Cloneable
,TrainingConfig
public class ZeroPadding3DLayer extends NoParamLayer
- See Also:
- Serialized Form
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
ZeroPadding3DLayer.Builder
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Field Summary
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Fields inherited from class org.deeplearning4j.nn.conf.layers.Layer
constraints, iDropout, layerName
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description 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
ParamInitializer
initializer()
Layer
instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
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.NoParamLayer
getGradientNormalization, getGradientNormalizationThreshold, getRegularizationByParam
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Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer
clone, getUpdaterByParam, initializeConstraints, 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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Method Detail
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instantiate
public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
- Specified by:
instantiate
in classLayer
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initializer
public ParamInitializer initializer()
- Overrides:
initializer
in classNoParamLayer
- 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- Overrides:
setNIn
in classNoParamLayer
- Parameters:
inputType
- Input type for this layeroverride
- 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.
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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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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
- Overrides:
isPretrainParam
in classNoParamLayer
- 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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