merlin.models.tf.DLRMBlock#
- merlin.models.tf.DLRMBlock(schema: merlin.schema.schema.Schema, *, embedding_dim: Optional[int] = None, embedding_options: Optional[merlin.models.tf.inputs.embedding.EmbeddingOptions] = None, embeddings: Optional[merlin.models.tf.core.base.Block] = None, bottom_block: Optional[merlin.models.tf.core.base.Block] = None, top_block: Optional[merlin.models.tf.core.base.Block] = None) merlin.models.tf.core.combinators.SequentialBlock[source]#
- Builds the DLRM architecture, as proposed in the following
`paper https://arxiv.org/pdf/1906.00091.pdf`_ 1.
- 1
Naumov, Maxim, et al. “Deep learning recommendation model for personalization and recommendation systems.” arXiv preprint arXiv:1906.00091 (2019).
- schemaSchema
The Schema with the input features
- embedding_dimOptional[int], optional
Dimension of the embeddings, by default None
- embedding_optionsEmbeddingOptions
Options for the input embeddings. - embedding_dim_default: int - Default dimension of the embedding table, when the feature is not found in
embedding_dims, by default 64 - infer_embedding_sizes : bool, Automatically defines the embedding dimension from the feature cardinality in the schema, by default False, which needs to be kept False for the DLRM architecture.
- embeddings: Optional[Block]
If provided creates a ParallelBlock with an EmbeddingTable for each categorical feature in the schema.
- bottom_blockBlock
The Block that combines the continuous features (typically a MLPBlock)
- top_blockOptional[Block], optional
The optional Block that combines the outputs of bottom layer and of the factorization machine layer, by default None
- SequentialBlock
The DLRM block
- ValueError
The schema is required by DLRM
- ValueError
The bottom_block is required by DLRM
- ValueError
The embedding_dim (X) needs to match the last layer of bottom MLP (Y).
- ValueError
Only one-of embeddings or embedding_options can be used.