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

\loadmathjax

An edge-conditioned convolutional layer (ECC) as presented by Simonovsky & Komodakis (2017).

Mode: single, disjoint, batch.

Notes:

This layer expects dense inputs and self-loops when working in batch mode.
In single mode, if the adjacency matrix is dense it will be converted to a SparseTensor automatically (which is an expensive operation).

For each node \mjeqn i , this layer computes: \mjdeqn Z_i = X_i W_\textrmroot + \sum\limits_j \in \mathcalN(i) X_j \textrmMLP(E_ji) + b where \mjeqn\textrmMLP is a multi-layer perceptron that outputs an edge-specific weight as a function of edge attributes.

Input

Node features of shape ([batch], N, F);
Binary adjacency matrices of shape ([batch], N, N);
Edge features. In single mode, shape (num_edges, S); in batch mode, shape (batch, N, N, S).

Output

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

layer_edge_conditioned_conv(
  object,
  channels,
  kernel_network = NULL,
  root = TRUE,
  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
`kernel_network`	a list of integers representing the hidden neurons of the kernel-generating network
`root`	NA
`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.