Base layers
This module contains a miscellany of layers that are not specifically for graph neural networks.
InnerProduct
spektral.layers.InnerProduct(trainable_kernel=False, activation=None, kernel_initializer='glorot_uniform', kernel_regularizer=None, kernel_constraint=None)
Computes the inner product between elements of a 2d Tensor:
Mode: single.
Input
 Tensor of shape
(N, M)
;
Output
 Tensor of shape
(N, N)
.
Arguments

trainable_kernel
: add a trainable square matrix between the inner product (e.g.,X @ W @ X.T
); 
activation
: activation function to use; 
kernel_initializer
: initializer for the weights; 
kernel_regularizer
: regularization applied to the kernel; 
kernel_constraint
: constraint applied to the kernel;
MinkowskiProduct
spektral.layers.MinkowskiProduct(input_dim_1=None, activation=None)
Computes the hyperbolic inner product between elements of a rank 2 Tensor:
Mode: single.
Input
 Tensor of shape
(N, M)
;
Output
 Tensor of shape
(N, N)
.
Arguments

input_dim_1
: first dimension of the input Tensor; set this if you encounter issues with shapes in your model, in order to provide an explicit output shape for your layer. 
activation
: activation function to use;
Disjoint2Batch
spektral.layers.Disjoint2Batch()
Utility layer that converts data from disjoint mode to batch mode by zeropadding the node features and adjacency matrices.
Mode: disjoint.
This layer expects a sparse adjacency matrix.
Input
 Node features of shape
(N, F)
;  Binary adjacency matrix of shape
(N, N)
;  Graph IDs of shape
(N, )
;
Output
 Batched node features of shape
(batch, N_max, F)
;  Batched adjacency matrix of shape
(batch, N_max, N_max)
;