layer_tag_conv: TAGConv

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

Description

\loadmathjax

A Topology Adaptive Graph Convolutional layer (TAG) as presented by Du et al. (2017).

Mode: single, disjoint.

This layer expects a sparse adjacency matrix.

This layer computes: \mjdeqn Z = \sum\limits_k=0^K D^-1/2A^kD^-1/2XW^(k)

Input

• Node features of shape (N, F);

• Binary adjacency matrix of shape (N, N).

Output

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

Usage

layer_tag_conv(
  object,
  channels,
  K = 3,
  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,
  ...
)

Arguments

channels	integer, number of output channels
K	the order of the layer (i.e., the layer will consider a K-hop neighbourhood for each node)
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.

rdinnager/rspektral documentation built on June 12, 2021, 1:26 a.m.