layer_gated_graph_conv: GatedGraphConv
In rdinnager/rspektral: What the Package Does (One Line, Title Case)

\loadmathjax

A gated graph convolutional layer as presented by Li et al. (2018).

Mode: single, disjoint.

This layer expects a sparse adjacency matrix.

This layer repeatedly applies a GRU cell \mjeqnL times to the node attributes \mjdeqn \beginalign & h^(0) _ i = X_i \| \mathbf0 \ & m^(l) _ i = \sum\limits_j \in \mathcalN(i) h^(l - 1) _ j W \ & h^(l) _ i = \textrmGRU \left(m^(l) _ i, h^(l - 1) _ i \right) \ & Z_i = h^(L) _ i \endalign where \mjeqn\textrmGRU is the GRU cell.

Input

Node features of shape (N, F); note that F must be smaller or equal than channels.
Binary adjacency matrix of shape (N, N).

Output

Node features with the same shape of the input, but the last dimension changed to channels.

layer_gated_graph_conv(
  object,
  channels,
  n_layers,
  activation = NULL,
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  bias_initializer = "zeros",
  kernel_regularizer = NULL,
  bias_regularizer = NULL,
  activity_regularizer = NULL,
  kernel_constraint = NULL,
  bias_constraint = NULL,
  ...
)

`channels`	integer, number of output channels
`n_layers`	integer, number of iterations with the GRU cell
`activation`	activation function to use
`use_bias`	bool, add a bias vector to the output
`kernel_initializer`	initializer for the weights
`bias_initializer`	initializer for the bias vector
`kernel_regularizer`	regularization applied to the weights
`bias_regularizer`	regularization applied to the bias vector
`activity_regularizer`	regularization applied to the output
`kernel_constraint`	constraint applied to the weights
`bias_constraint`	constraint applied to the bias vector.