Uses of Interface
org.deeplearning4j.nn.graph.vertex.GraphVertex

Packages that use GraphVertex

Package	Description
org.deeplearning4j.nn.conf.graph
org.deeplearning4j.nn.conf.graph.rnn
org.deeplearning4j.nn.graph
org.deeplearning4j.nn.graph.vertex
org.deeplearning4j.nn.graph.vertex.impl
org.deeplearning4j.nn.graph.vertex.impl.rnn

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

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

Modifier and Type	Method and Description
GraphVertex	StackVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
GraphVertex	LayerVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
GraphVertex	SubsetVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
GraphVertex	PreprocessorVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
GraphVertex	UnstackVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
GraphVertex	MergeVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
GraphVertex	ElementWiseVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)
abstract GraphVertex	GraphVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams) Create a GraphVertex instance, for the given computation graph, given the configuration instance.
GraphVertex	L2Vertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)

Uses of GraphVertex in org.deeplearning4j.nn.conf.graph.rnn

Methods in org.deeplearning4j.nn.conf.graph.rnn that return GraphVertex

Modifier and Type	Method and Description
GraphVertex	DuplicateToTimeSeriesVertex.instantiate(ComputationGraph graph, String name, int idx, org.nd4j.linalg.api.ndarray.INDArray paramsView, boolean initializeParams)

Uses of GraphVertex in org.deeplearning4j.nn.graph

Fields in org.deeplearning4j.nn.graph declared as GraphVertex

Modifier and Type	Field and Description
protected GraphVertex[]	ComputationGraph.vertices All GraphVertex objects in the network.

Fields in org.deeplearning4j.nn.graph with type parameters of type GraphVertex

Modifier and Type	Field and Description
protected Map<String,GraphVertex>	ComputationGraph.verticesMap Map of vertices by name

Methods in org.deeplearning4j.nn.graph that return GraphVertex

Modifier and Type	Method and Description
GraphVertex	ComputationGraph.getVertex(String name) Return a given GraphVertex by name, or null if no vertex with that name exists
GraphVertex[]	ComputationGraph.getVertices() Returns an array of all GraphVertex objects.

Uses of GraphVertex in org.deeplearning4j.nn.graph.vertex

Classes in org.deeplearning4j.nn.graph.vertex that implement GraphVertex

Modifier and Type	Class and Description
class	BaseGraphVertex BaseGraphVertex defines a set of common functionality for GraphVertex instances.

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

Classes in org.deeplearning4j.nn.graph.vertex.impl that implement GraphVertex

Modifier and Type	Class and Description
class	ElementWiseVertex An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner For example, the activations may be combined by addition, subtraction or multiplication.
class	InputVertex An InputVertex simply defines the location (and connection structure) of inputs to the ComputationGraph.
class	L2Vertex L2Vertex calculates the L2 least squares error of two inputs.
class	LayerVertex LayerVertex is a GraphVertex with a neural network Layer (and, optionally an InputPreProcessor) in it
class	MergeVertex A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging. Exactly how this is done depends on the type of input. For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2] For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength]) -> [numExamples,layerSize1 + layerSize2,timeSeriesLength] For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height]) -> [numExamples,depth1 + depth2,width,height]
class	PreprocessorVertex PreprocessorVertex is a simple adaptor class that allows a InputPreProcessor to be used in a ComputationGraph GraphVertex, without it being associated with a layer.
class	StackVertex StackVertex allows for stacking of inputs so that they may be forwarded through a network.
class	SubsetVertex SubsetVertex is used to select a subset of the activations out of another GraphVertex. For example, a subset of the activations out of a layer. Note that this subset is specifying by means of an interval of the original activations.
class	UnstackVertex UnstackVertex allows for unstacking of inputs so that they may be forwarded through a network.

Uses of GraphVertex in org.deeplearning4j.nn.graph.vertex.impl.rnn

Classes in org.deeplearning4j.nn.graph.vertex.impl.rnn that implement GraphVertex

Modifier and Type	Class and Description
class	DuplicateToTimeSeriesVertex DuplicateToTimeSeriesVertex is a vertex that goes from 2d activations to a 3d time series activations, by means of duplication.
class	LastTimeStepVertex LastTimeStepVertex is used in the context of recurrent neural network activations, to go from 3d (time series) activations to 2d activations, by extracting out the last time step of activations for each example. This can be used for example in sequence to sequence architectures, and potentially for sequence classification.