class deephyper.keras.layers.SparseMPNN(*args: Any, **kwargs: Any)[source]#

Bases: Layer

Message passing cell.

  • state_dim (int) – number of output channels.

  • T (int) – number of message passing repetition.

  • attn_heads (int) – number of attention heads.

  • attn_method (str) – type of attention methods.

  • aggr_method (str) – type of aggregation methods.

  • activation (str) – type of activation functions.

  • update_method (str) – type of update functions.




Apply the layer on input tensors.

__call__(*args: Any, **kwargs: Any) Any#

Call self as a function.

call(inputs, **kwargs)[source]#

Apply the layer on input tensors.


inputs (list) – X (tensor): node feature tensor (batch size * # nodes * # node features) A (tensor): edge pair tensor (batch size * # edges * 2), one is source ID, one is target ID E (tensor): edge feature tensor (batch size * # edges * # edge features) mask (tensor): node mask tensor to mask out non-existent nodes (batch size * # nodes) degree (tensor): node degree tensor for GCN attention (batch size * # edges)


results after several repetitions of edge network, attention, aggregation and update function (batch size * # nodes * # node features)

Return type:

X (tensor)