layer_arma_conv: ARMAConv

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

Description

\loadmathjax

A graph convolutional layer with \mjeqn\mathrmARMA _ K filters, as presented by Bianchi et al. (2019).

Mode: single, disjoint, mixed, batch.

This layer computes: \mjdeqn Z = \frac1K \sum\limits_k=1^K \bar X_k^(T), where \mjeqnK is the order of the \mjeqn\mathrmARMA _ K filter, and where: \mjdeqn \bar X_k^(t + 1) = \sigma \left(\tilde L \bar X^(t) W^(t) + X V^(t) \right) is a recursive approximation of an \mjeqn\mathrmARMA _ 1 filter, where \mjeqn \bar X^(0) = X and \mjdeqn \tilde L = \frac2\lambda_max \cdot (I - D^-1/2 A D^-1/2) - I is the normalized Laplacian with a rescaled spectrum.

Input

• Node features of shape ([batch], N, F);

• Normalized and rescaled Laplacian of shape ([batch], N, N); can be computed with spektral.utils.convolution.normalized_laplacian and spektral.utils.convolution.rescale_laplacian.

Output

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

Usage

layer_arma_conv(
  object,
  channels,
  order = 1,
  iterations = 1,
  share_weights = FALSE,
  gcn_activation = "relu",
  dropout_rate = 0,
  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	number of output channels
order	order of the full ARMA\(_K\) filter, i.e., the number of parallel stacks in the layer
iterations	number of iterations to compute each ARMA\(_1\) approximation
share_weights	share the weights in each ARMA\(_1\) stack.
gcn_activation	activation function to use to compute each ARMA\(_1\) stack
dropout_rate	dropout rate for skip connection
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.