Base layers

This module contains a miscellany of layers that are not specifically for graph neural networks.

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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: $$\langle \x, \x \rangle = \x\x^\top.$$

Mode: single.

Input

• Tensor of shape (n_nodes, n_features);

Output

• Tensor of shape (n_nodes, n_nodes).

Arguments

• trainable_kernel: add a trainable square matrix between the inner product (e.g., X @ W @ X.T);

• activation: activation function;

• kernel_initializer: initializer for the weights;

• kernel_regularizer: regularization applied to the kernel;

• kernel_constraint: constraint applied to the kernel;

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Disjoint2Batch

spektral.layers.Disjoint2Batch()

Utility layer that converts data from disjoint mode to batch mode by zero-padding the node features and adjacency matrices.

Mode: disjoint.

This layer expects a sparse adjacency matrix.

Input

• Node features of shape (n_nodes, n_node_features);
• Binary adjacency matrix of shape (n_nodes, n_nodes);
• Graph IDs of shape (n_nodes, );

Output

• Batched node features of shape (batch, N_max, n_node_features);
• Batched adjacency matrix of shape (batch, N_max, N_max);

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MinkowskiProduct

spektral.layers.MinkowskiProduct(input_dim_1=None, activation=None)

Computes the hyperbolic inner product between elements of a rank 2 Tensor: $$\langle \x, \x \rangle = \x \, \begin{pmatrix} \I_{d \times d} & 0 \\ 0 & -1 \end{pmatrix} \, \x^\top.$$

Mode: single.

Input

• Tensor of shape (n_nodes, n_features);

Output

• Tensor of shape (n_nodes, n_nodes).

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;