Package org.deeplearning4j.nn.api
Interface TrainingConfig
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- All Known Implementing Classes:
AbstractLSTM
,AbstractSameDiffLayer
,ActivationLayer
,AttentionVertex
,AutoEncoder
,BaseLayer
,BaseOutputLayer
,BasePretrainNetwork
,BaseRecurrentLayer
,BaseUpsamplingLayer
,BaseWrapperLayer
,BatchNormalization
,Bidirectional
,CapsuleLayer
,CapsuleStrengthLayer
,CenterLossOutputLayer
,Cnn3DLossLayer
,CnnLossLayer
,Convolution1D
,Convolution1DLayer
,Convolution2D
,Convolution3D
,ConvolutionLayer
,Cropping1D
,Cropping2D
,Cropping3D
,Deconvolution2D
,Deconvolution3D
,DenseLayer
,DepthwiseConvolution2D
,DropoutLayer
,DummyConfig
,ElementWiseMultiplicationLayer
,EmbeddingLayer
,EmbeddingSequenceLayer
,FeedForwardLayer
,FrozenLayer
,FrozenLayerWithBackprop
,GlobalPoolingLayer
,GravesBidirectionalLSTM
,GravesLSTM
,IdentityLayer
,LastTimeStep
,Layer
,LearnedSelfAttentionLayer
,LocallyConnected1D
,LocallyConnected2D
,LocalResponseNormalization
,LossLayer
,LSTM
,MaskLayer
,MaskZeroLayer
,NoParamLayer
,OCNNOutputLayer
,OutputLayer
,Pooling1D
,Pooling2D
,PReLULayer
,PrimaryCapsules
,RecurrentAttentionLayer
,RepeatVector
,RnnLossLayer
,RnnOutputLayer
,SameDiffLambdaLayer
,SameDiffLambdaVertex
,SameDiffLayer
,SameDiffOutputLayer
,SameDiffVertex
,SelfAttentionLayer
,SeparableConvolution2D
,SimpleRnn
,SpaceToBatchLayer
,SpaceToDepthLayer
,Subsampling1DLayer
,Subsampling3DLayer
,SubsamplingLayer
,TimeDistributed
,Upsampling1D
,Upsampling2D
,Upsampling3D
,VariationalAutoencoder
,Yolo2OutputLayer
,ZeroPadding1DLayer
,ZeroPadding3DLayer
,ZeroPaddingLayer
public interface TrainingConfig
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method 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)
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Method Detail
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getLayerName
String getLayerName()
- Returns:
- Name of the layer
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getRegularizationByParam
List<Regularization> getRegularizationByParam(String paramName)
Get the regularization types (l1/l2/weight decay) for the given parameter. Different parameters may have different regularization types.- Parameters:
paramName
- Parameter name ("W", "b" etc)- Returns:
- Regularization types (if any) for the specified parameter
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isPretrainParam
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.- Parameters:
paramName
- Parameter name/key- Returns:
- True if the parameter is for layerwise pretraining only, false otherwise
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getUpdaterByParam
IUpdater getUpdaterByParam(String paramName)
Get the updater for the given parameter. Typically the same updater will be used for all updaters, but this is not necessarily the case- Parameters:
paramName
- Parameter name- Returns:
- IUpdater for the parameter
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getGradientNormalization
GradientNormalization getGradientNormalization()
- Returns:
- The gradient normalization configuration
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getGradientNormalizationThreshold
double getGradientNormalizationThreshold()
- Returns:
- The gradient normalization threshold
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setDataType
void setDataType(DataType dataType)
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