layer_cheb_conv: ChebConv

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

Description

\loadmathjax

A Chebyshev convolutional layer as presented by Defferrard et al. (2016).

Mode: single, disjoint, mixed, batch.

This layer computes: \mjdeqn Z = \sum \limits_k=0^K - 1 T^(k) W^(k) + b^(k), where \mjeqn T^(0), ..., T^(K - 1) are Chebyshev polynomials of \mjeqn\tilde L defined as \mjdeqn T^(0) = X \ T^(1) = \tilde L X \ T^(k \ge 2) = 2 \cdot \tilde L T^(k - 1) - T^(k - 2), where \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);

• A list of K Chebyshev polynomials of shape [([batch], N, N), ..., ([batch], N, N)]; can be computed with spektral.utils.convolution.chebyshev_filter.

Output

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

Usage

layer_cheb_conv(
  object,
  channels,
  K = 1,
  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
K	order of the Chebyshev polynomials
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.