Class CenterLossOutputLayer
- java.lang.Object
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- org.deeplearning4j.nn.conf.layers.Layer
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- org.deeplearning4j.nn.conf.layers.BaseLayer
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- org.deeplearning4j.nn.conf.layers.FeedForwardLayer
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- org.deeplearning4j.nn.conf.layers.BaseOutputLayer
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- org.deeplearning4j.nn.conf.layers.CenterLossOutputLayer
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- All Implemented Interfaces:
Serializable
,Cloneable
,TrainingConfig
public class CenterLossOutputLayer extends BaseOutputLayer
- See Also:
- Serialized Form
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Nested Class Summary
Nested Classes Modifier and Type Class Description static class
CenterLossOutputLayer.Builder
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Field Summary
Fields Modifier and Type Field Description protected double
alpha
protected boolean
gradientCheck
protected double
lambda
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Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
hasBias, lossFn
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Fields inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
nIn, nOut, timeDistributedFormat
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Fields inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer
activationFn, biasInit, biasUpdater, gainInit, gradientNormalization, gradientNormalizationThreshold, iUpdater, regularization, regularizationBias, weightInitFn, weightNoise
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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
CenterLossOutputLayer(CenterLossOutputLayer.Builder builder)
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description double
getAlpha()
boolean
getGradientCheck()
double
getLambda()
LayerMemoryReport
getMemoryReport(InputType inputType)
This is a report of the estimated memory consumption for the given layerIUpdater
getUpdaterByParam(String paramName)
Get the updater for the given parameter.ParamInitializer
initializer()
Layer
instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
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Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseOutputLayer
hasBias
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Methods inherited from class org.deeplearning4j.nn.conf.layers.FeedForwardLayer
getOutputType, getPreProcessorForInputType, isPretrainParam, setNIn
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Methods inherited from class org.deeplearning4j.nn.conf.layers.BaseLayer
clone, getGradientNormalization, getRegularizationByParam, resetLayerDefaultConfig
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Methods inherited from class org.deeplearning4j.nn.conf.layers.Layer
initializeConstraints, 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
getGradientNormalizationThreshold, getLayerName
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Constructor Detail
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CenterLossOutputLayer
protected CenterLossOutputLayer(CenterLossOutputLayer.Builder builder)
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Method Detail
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instantiate
public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType)
- Specified by:
instantiate
in classLayer
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initializer
public ParamInitializer initializer()
- Specified by:
initializer
in classLayer
- Returns:
- The parameter initializer for this model
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getUpdaterByParam
public IUpdater getUpdaterByParam(String paramName)
Description copied from class:BaseLayer
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 interfaceTrainingConfig
- Overrides:
getUpdaterByParam
in classBaseLayer
- Parameters:
paramName
- Parameter name- Returns:
- IUpdater for the parameter
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getAlpha
public double getAlpha()
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getLambda
public double getLambda()
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getGradientCheck
public boolean getGradientCheck()
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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- Overrides:
getMemoryReport
in classBaseOutputLayer
- 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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