object GCN extends Serializable
Linear Supertypes
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def computeSparseAdjacency[S](valueOpt: STenOptions, edgeI: STen, edgeJ: STen, numNodes: Long)(implicit arg0: Sc[S]): (Constant, Constant)
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- def gcn[S](in: Int, out: Int, tOpt: STenOptions, dropout: Double = 0d, nonLinearity: Boolean = true)(implicit arg0: Sc[S]): GCN[ResidualModule[EitherModule[Variable, Variable, Seq4[Variable, Variable, Variable, Variable, Variable, Linear with GenericModule[Variable, Variable], BatchNorm with GenericModule[Variable, Variable], Fun with GenericModule[Variable, Variable], Dropout with GenericModule[Variable, Variable]], Seq2[Variable, Variable, Variable, Linear with GenericModule[Variable, Variable], BatchNorm with GenericModule[Variable, Variable]]]]]
- def gcnAggregation[S](nodeFeatures: Variable, degrees: Variable, a: Variable)(implicit arg0: Sc[S]): Variable
- def gcnAggregation[S](nodeFeatures: Variable, edgeI: STen, edgeJ: STen)(implicit arg0: Sc[S]): Variable
Performs D-0.5 (A+A'+I) D-0.5 W where are node features (N x D) A is the asymmetric adjacency matrix without self loops, elements in {0,1} I is identity D is degree(A+I)
Performs D-0.5 (A+A'+I) D-0.5 W where are node features (N x D) A is the asymmetric adjacency matrix without self loops, elements in {0,1} I is identity D is degree(A+I)
- nodeFeatures
N x D node features
- returns
N x D aggregated features
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- implicit def load[M <: Module](implicit arg0: Load[M]): Load[GCN[M]]
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- implicit def trainingMode[M <: Module](implicit arg0: TrainingMode[M]): TrainingMode[GCN[M]]
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- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)