Imports

Types

GCNGate[TT] {.final.} = ref object of Gate[TT]: Source Edit
GCNLayer[T] = object weight*: Variable[Tensor[T]] bias*: Variable[Tensor[T]]: Source Edit

proc forward[T](self: GCNLayer[T]; input, adjacency: Variable[Tensor[T]]): Variable[ Tensor[T]]: Source Edit
proc gcn[TT](input, adjacency, weight: Variable[TT]; bias: Variable[TT] = nil): Variable[ TT]: Input:
A x Variable of shape nodes, in_features

An adjacency matrix of shape nodes, nodes

A weight Variable of shape out_features, in_features

Optionally a bias Variable of shape 1, out_features

Return:

(AX)W+b

Source Edit
proc init[T](ctx: Context[Tensor[T]]; layerType: typedesc[GCNLayer[T]]; numInput, numOutput: int): GCNLayer[T]: Initializes a graph convolutional layer with num_input input features and num_output output features. Using Kaiming He initialisation for weights to provide decent performance in most cases. Biases are set to zero. Source Edit
func inShape[T](self: GCNLayer[T]): seq[int]: Source Edit
func outShape[T](self: GCNLayer[T]): seq[int]: Source Edit