Uses of Class
org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer

Packages that use AbstractSameDiffLayer

Package	Description
org.deeplearning4j.nn.conf.layers
org.deeplearning4j.nn.conf.layers.samediff
org.deeplearning4j.nn.layers.util
org.deeplearning4j.nn.params

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

Subclasses of AbstractSameDiffLayer in org.deeplearning4j.nn.conf.layers

Modifier and Type	Class and Description
class	CapsuleLayer An implementation of the DigiCaps layer from Dynamic Routing Between Capsules Input should come from a PrimaryCapsules layer and be of shape [mb, inputCaps, inputCapDims].
class	CapsuleStrengthLayer An layer to get the "strength" of each capsule, that is, the probability of it being in the input.
class	LearnedSelfAttentionLayer Implements Dot Product Self Attention with learned queries Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using learned queries.
class	LocallyConnected1D SameDiff version of a 1D locally connected layer.
class	LocallyConnected2D SameDiff version of a 2D locally connected layer.
class	PrimaryCapsules An implementation of the PrimaryCaps layer from Dynamic Routing Between Capsules Is a reshaped 2D convolution, and the input should be 2D convolutional ([mb, c, h, w]).
class	RecurrentAttentionLayer Implements Recurrent Dot Product Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using the hidden state as the query and all time steps as keys/values.
class	SelfAttentionLayer Implements Dot Product Self Attention Takes in RNN style input in the shape of [batchSize, features, timesteps] and applies dot product attention using each timestep as the query.

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

Subclasses of AbstractSameDiffLayer in org.deeplearning4j.nn.conf.layers.samediff

Modifier and Type	Class and Description
class	SameDiffLambdaLayer SameDiffLambdaLayer is defined to be used as the base class for implementing lambda layers using SameDiff Lambda layers are layers without parameters - and as a result, have a much simpler API - users need only extend SameDiffLambdaLayer and implement a single method
class	SameDiffLayer A base layer used for implementing Deeplearning4j layers using SameDiff.
class	SameDiffOutputLayer A base layer used for implementing Deeplearning4j Output layers using SameDiff.

Uses of AbstractSameDiffLayer in org.deeplearning4j.nn.layers.util

Subclasses of AbstractSameDiffLayer in org.deeplearning4j.nn.layers.util

Modifier and Type	Class and Description
class	IdentityLayer Identity layer, passes data through unaltered.

Uses of AbstractSameDiffLayer in org.deeplearning4j.nn.params

Methods in org.deeplearning4j.nn.params with parameters of type AbstractSameDiffLayer

Modifier and Type	Method and Description
Map<String,INDArray>	SameDiffParamInitializer.subsetAndReshape(List<String> params, Map<String,long[]> paramShapes, INDArray view, AbstractSameDiffLayer sdl, SameDiffVertex sdv)