Uses of Interface
org.deeplearning4j.nn.conf.InputPreProcessor

Packages that use InputPreProcessor

Package	Description
org.deeplearning4j.nn.conf
org.deeplearning4j.nn.conf.graph
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.convolutional
org.deeplearning4j.nn.conf.layers.misc
org.deeplearning4j.nn.conf.layers.objdetect
org.deeplearning4j.nn.conf.layers.recurrent
org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.conf.layers.util
org.deeplearning4j.nn.conf.layers.wrapper
org.deeplearning4j.nn.conf.preprocessor
org.deeplearning4j.nn.conf.serde.legacyformat
org.deeplearning4j.nn.graph.vertex.impl
org.deeplearning4j.nn.transferlearning

Uses of InputPreProcessor in org.deeplearning4j.nn.conf

Fields in org.deeplearning4j.nn.conf with type parameters of type InputPreProcessor

Modifier and Type	Field and Description
protected Map<Integer,InputPreProcessor>	MultiLayerConfiguration.inputPreProcessors
protected Map<Integer,InputPreProcessor>	MultiLayerConfiguration.Builder.inputPreProcessors
protected Map<String,InputPreProcessor>	ComputationGraphConfiguration.GraphBuilder.inputPreProcessors

Methods in org.deeplearning4j.nn.conf that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	InputPreProcessor.clone()
InputPreProcessor	MultiLayerConfiguration.getInputPreProcess(int curr)

Methods in org.deeplearning4j.nn.conf with parameters of type InputPreProcessor

Modifier and Type	Method and Description
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.addLayer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs) Add a layer and an InputPreProcessor, with the specified name and specified inputs.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.appendLayer(String layerName, Layer layer, InputPreProcessor preProcessor) Add a layer and an InputPreProcessor, with the specified name and input from the last added layer/vertex.
MultiLayerConfiguration.Builder	MultiLayerConfiguration.Builder.inputPreProcessor(Integer layer, InputPreProcessor processor) Specify the processors.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.inputPreProcessor(String layer, InputPreProcessor processor) Specify the processors for a given layer These are used at each layer for doing things like normalization and shaping of input. Note: preprocessors can also be defined using the ComputationGraphConfiguration.GraphBuilder.addLayer(String, Layer, InputPreProcessor, String...) method.
ComputationGraphConfiguration.GraphBuilder	ComputationGraphConfiguration.GraphBuilder.layer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs) Add a layer and an InputPreProcessor, with the specified name and specified inputs.

Method parameters in org.deeplearning4j.nn.conf with type arguments of type InputPreProcessor

Modifier and Type	Method and Description
MultiLayerConfiguration.Builder	MultiLayerConfiguration.Builder.inputPreProcessors(Map<Integer,InputPreProcessor> processors)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.graph

Methods in org.deeplearning4j.nn.conf.graph that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	LayerVertex.getPreProcessor()

Constructors in org.deeplearning4j.nn.conf.graph with parameters of type InputPreProcessor

Constructor and Description
LayerVertex(NeuralNetConfiguration layerConf, InputPreProcessor preProcessor)
PreprocessorVertex(InputPreProcessor preProcessor)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers

Methods in org.deeplearning4j.nn.conf.layers that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	ZeroPadding3DLayer.getPreProcessorForInputType(InputType inputType)
abstract InputPreProcessor	Layer.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 appropriate InputPreProcessor for this layer, such as a CnnToFeedForwardPreProcessor
InputPreProcessor	RecurrentAttentionLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	ZeroPadding1DLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	BaseUpsamplingLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	LocalResponseNormalization.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	RnnOutputLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	PReLULayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Cnn3DLossLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	SpaceToDepthLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	LocallyConnected2D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Upsampling3D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	EmbeddingSequenceLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Convolution3D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	ZeroPaddingLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	SubsamplingLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	FeedForwardLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	SelfAttentionLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	LearnedSelfAttentionLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	BaseRecurrentLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Subsampling1DLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	ConvolutionLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	RnnLossLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	CnnLossLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	GlobalPoolingLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	BatchNormalization.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Subsampling3DLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	DropoutLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	SpaceToBatchLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	ActivationLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Upsampling1D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Upsampling2D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	LocallyConnected1D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Convolution1DLayer.getPreProcessorForInputType(InputType inputType)
static InputPreProcessor	InputTypeUtil.getPreProcessorForInputTypeCnn3DLayers(InputType inputType, String layerName) Utility method for determining the appropriate preprocessor for CNN layers, such as ConvolutionLayer and SubsamplingLayer
static InputPreProcessor	InputTypeUtil.getPreProcessorForInputTypeCnnLayers(InputType inputType, String layerName) Utility method for determining the appropriate preprocessor for CNN layers, such as ConvolutionLayer and SubsamplingLayer
static InputPreProcessor	InputTypeUtil.getPreprocessorForInputTypeRnnLayers(InputType inputType, String layerName)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.convolutional

Methods in org.deeplearning4j.nn.conf.layers.convolutional that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	Cropping1D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Cropping2D.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	Cropping3D.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.misc

Methods in org.deeplearning4j.nn.conf.layers.misc that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	FrozenLayer.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.objdetect

Methods in org.deeplearning4j.nn.conf.layers.objdetect that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	Yolo2OutputLayer.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.recurrent

Methods in org.deeplearning4j.nn.conf.layers.recurrent that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	Bidirectional.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.samediff

Methods in org.deeplearning4j.nn.conf.layers.samediff that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	AbstractSameDiffLayer.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.util

Methods in org.deeplearning4j.nn.conf.layers.util that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	MaskZeroLayer.getPreProcessorForInputType(InputType inputType)
InputPreProcessor	MaskLayer.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.layers.wrapper

Methods in org.deeplearning4j.nn.conf.layers.wrapper that return InputPreProcessor

Modifier and Type	Method and Description
InputPreProcessor	BaseWrapperLayer.getPreProcessorForInputType(InputType inputType)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.preprocessor

Classes in org.deeplearning4j.nn.conf.preprocessor that implement InputPreProcessor

Modifier and Type	Class and Description
class	BaseInputPreProcessor
class	Cnn3DToFeedForwardPreProcessor A preprocessor to allow CNN and standard feed-forward network layers to be used together. For example, CNN3D -> Denselayer This does two things: (b) Reshapes 5d activations out of CNN layer, with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) into 2d activations (with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) for use in feed forward layer (a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputDepth* inputHeight*inputWidth*numChannels]) into 5d epsilons (with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) suitable to feed into CNN layers. Note: numChannels is equivalent to featureMaps referenced in different literature
class	CnnToFeedForwardPreProcessor A preprocessor to allow CNN and standard feed-forward network layers to be used together. For example, CNN -> Denselayer This does two things: (b) Reshapes 4d activations out of CNN layer, with shape [numExamples, numChannels, inputHeight, inputWidth]) into 2d activations (with shape [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer (a) Reshapes epsilons (weights*deltas) out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputHeight*inputWidth*numChannels]) into 4d epsilons (with shape [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers. Note: numChannels is equivalent to channels or featureMaps referenced in different literature
class	CnnToRnnPreProcessor A preprocessor to allow CNN and RNN layers to be used together. For example, ConvolutionLayer -> GravesLSTM Functionally equivalent to combining CnnToFeedForwardPreProcessor + FeedForwardToRnnPreProcessor Specifically, this does two things: (a) Reshape 4d activations out of CNN layer, with shape [timeSeriesLength*miniBatchSize, numChannels, inputHeight, inputWidth]) into 3d (time series) activations (with shape [numExamples, inputHeight*inputWidth*numChannels, timeSeriesLength]) for use in RNN layers (b) Reshapes 3d epsilons (weights.*deltas) out of RNN layer (with shape [miniBatchSize,inputHeight*inputWidth*numChannels,timeSeriesLength]) into 4d epsilons with shape [miniBatchSize*timeSeriesLength, numChannels, inputHeight, inputWidth] suitable to feed into CNN layers.
class	ComposableInputPreProcessor Composable input pre processor
class	FeedForwardToCnn3DPreProcessor A preprocessor to allow 3D CNN and standard feed-forward network layers to be used together. For example, DenseLayer -> Convolution3D This does two things: (a) Reshapes activations out of FeedFoward layer (which is 2D with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) into 5d activations (with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) suitable to feed into CNN layers. (b) Reshapes 5d epsilons from 3D CNN layer, with shape [numExamples, numChannels, inputDepth, inputHeight, inputWidth]) into 2d epsilons (with shape [numExamples, inputDepth*inputHeight*inputWidth*numChannels]) for use in feed forward layer
class	FeedForwardToCnnPreProcessor A preprocessor to allow CNN and standard feed-forward network layers to be used together. For example, DenseLayer -> CNN This does two things: (a) Reshapes activations out of FeedFoward layer (which is 2D or 3D with shape [numExamples, inputHeight*inputWidth*numChannels]) into 4d activations (with shape [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers. (b) Reshapes 4d epsilons (weights*deltas) from CNN layer, with shape [numExamples, numChannels, inputHeight, inputWidth]) into 2d epsilons (with shape [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer Note: numChannels is equivalent to channels or featureMaps referenced in different literature
class	FeedForwardToRnnPreProcessor A preprocessor to allow RNN and feed-forward network layers to be used together. For example, DenseLayer -> GravesLSTM This does two things: (a) Reshapes activations out of FeedFoward layer (which is 2D with shape [miniBatchSize*timeSeriesLength,layerSize]) into 3d activations (with shape [miniBatchSize,layerSize,timeSeriesLength]) suitable to feed into RNN layers. (b) Reshapes 3d epsilons (weights*deltas from RNN layer, with shape [miniBatchSize,layerSize,timeSeriesLength]) into 2d epsilons (with shape [miniBatchSize*timeSeriesLength,layerSize]) for use in feed forward layer
class	RnnToCnnPreProcessor A preprocessor to allow RNN and CNN layers to be used together For example, time series (video) input -> ConvolutionLayer, or conceivable GravesLSTM -> ConvolutionLayer Functionally equivalent to combining RnnToFeedForwardPreProcessor + FeedForwardToCnnPreProcessor Specifically, this does two things: (a) Reshape 3d activations out of RNN layer, with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength]) into 4d (CNN) activations (with shape [numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight]) (b) Reshapes 4d epsilons (weights.*deltas) out of CNN layer (with shape [numExamples*timeSeriesLength, numChannels, inputHeight, inputWidth]) into 3d epsilons with shape [miniBatchSize, numChannels*inputHeight*inputWidth, timeSeriesLength] suitable to feed into CNN layers.
class	RnnToFeedForwardPreProcessor A preprocessor to allow RNN and feed-forward network layers to be used together. For example, GravesLSTM -> OutputLayer or GravesLSTM -> DenseLayer This does two things: (a) Reshapes activations out of RNN layer (which is 3D with shape [miniBatchSize,layerSize,timeSeriesLength]) into 2d activations (with shape [miniBatchSize*timeSeriesLength,layerSize]) suitable for use in feed-forward layers. (b) Reshapes 2d epsilons (weights*deltas from feed forward layer, with shape [miniBatchSize*timeSeriesLength,layerSize]) into 3d epsilons (with shape [miniBatchSize,layerSize,timeSeriesLength]) for use in RNN layer

Constructors in org.deeplearning4j.nn.conf.preprocessor with parameters of type InputPreProcessor

Constructor and Description
ComposableInputPreProcessor(InputPreProcessor... inputPreProcessors)

Uses of InputPreProcessor in org.deeplearning4j.nn.conf.serde.legacyformat

Method parameters in org.deeplearning4j.nn.conf.serde.legacyformat with type arguments of type InputPreProcessor

Modifier and Type	Method and Description
static void	LegacyPreprocessorDeserializer.registerLegacyClassDefaultName(Class<? extends InputPreProcessor> clazz)
static void	LegacyPreprocessorDeserializer.registerLegacyClassSpecifiedName(String name, Class<? extends InputPreProcessor> clazz)

Uses of InputPreProcessor in org.deeplearning4j.nn.graph.vertex.impl

Constructors in org.deeplearning4j.nn.graph.vertex.impl with parameters of type InputPreProcessor

Constructor and Description
LayerVertex(ComputationGraph graph, String name, int vertexIndex, Layer layer, InputPreProcessor layerPreProcessor, boolean outputVertex, org.nd4j.linalg.api.buffer.DataType dataType) Create a network input vertex:
LayerVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices, VertexIndices[] outputVertices, Layer layer, InputPreProcessor layerPreProcessor, boolean outputVertex, org.nd4j.linalg.api.buffer.DataType dataType)
PreprocessorVertex(ComputationGraph graph, String name, int vertexIndex, InputPreProcessor preProcessor, org.nd4j.linalg.api.buffer.DataType dataType)
PreprocessorVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices, VertexIndices[] outputVertices, InputPreProcessor preProcessor, org.nd4j.linalg.api.buffer.DataType dataType)

Uses of InputPreProcessor in org.deeplearning4j.nn.transferlearning

Methods in org.deeplearning4j.nn.transferlearning with parameters of type InputPreProcessor

Modifier and Type	Method and Description
TransferLearning.GraphBuilder	TransferLearning.GraphBuilder.addLayer(String layerName, Layer layer, InputPreProcessor preProcessor, String... layerInputs) Add a layer with a specified preprocessor
TransferLearning.Builder	TransferLearning.Builder.setInputPreProcessor(int layer, InputPreProcessor processor) Specify the preprocessor for the added layers for cases where they cannot be inferred automatically.