graphstorm.model.GSNodeEncoderInputLayer
- class graphstorm.model.GSNodeEncoderInputLayer(g, feat_size, embed_size, activation=None, dropout=0.0, use_node_embeddings=False, force_no_embeddings=None, num_ffn_layers_in_input=0, ffn_activation=<function relu>, cache_embed=False, use_wholegraph_sparse_emb=False)
The input encoder layer for all nodes in a heterogeneous graph.
The input layer adds learnable embeddings on nodes if the nodes do not have features. It adds a linear layer on nodes with node features and the linear layer projects the node features to a specified dimension. A user can add learnable embeddings on the nodes with node features. In this case, the input layer combines the node features with the learnable embeddings and project them to the specified dimension.
Parameters
- g: DistGraph
The distributed graph
- feat_sizedict of int
The original feat sizes of each node type
- embed_sizeint
The embedding size
- activationfunc
The activation function
- dropoutfloat
The dropout parameter
- use_node_embeddingsbool
Whether we will use learnable embeddings for individual nodes even when node features are available.
- force_no_embeddingslist of str
The list node types that are forced to not have learnable embeddings.
- num_ffn_layers_in_input: int, optional
Number of layers of feedforward neural network for each node type in the input layers
- ffn_activationcallable
The activation function for the feedforward neural networks.
- cache_embedbool
Whether or not to cache the embeddings.
- use_wholegraph_sparse_embbool
Whether or not to use WholeGraph to host embeddings for sparse updates.
Examples:
from graphstorm import get_node_feat_size from graphstorm.model import GSgnnNodeModel, GSNodeEncoderInputLayer from graphstorm.dataloading import GSgnnNodeTrainData np_data = GSgnnNodeTrainData(...) model = GSgnnEdgeModel(alpha_l2norm=0) feat_size = get_node_feat_size(np_data.g, 'feat') encoder = GSNodeEncoderInputLayer(g, feat_size, 4, dropout=0, use_node_embeddings=True) model.set_node_input_encoder(encoder)