merlin.models.tf.DLRMModel#

merlin.models.tf.DLRMModel(schema: merlin.schema.schema.Schema, *, embeddings: Optional[merlin.models.tf.core.base.Block] = None, embedding_dim: Optional[int] = None, embedding_options: Optional[merlin.models.tf.inputs.embedding.EmbeddingOptions] = None, bottom_block: Optional[merlin.models.tf.core.base.Block] = None, top_block: Optional[merlin.models.tf.core.base.Block] = None, prediction_tasks: Optional[Union[merlin.models.tf.prediction_tasks.base.PredictionTask, List[merlin.models.tf.prediction_tasks.base.PredictionTask], merlin.models.tf.prediction_tasks.base.ParallelPredictionBlock]] = None) → merlin.models.tf.models.base.Model[source]#

DLRM-model architecture.

Example Usage::

dlrm = DLRMModel(schema, embedding_dim=64, bottom_block=MLPBlock([256, 64])) dlrm.compile(optimizer=”adam”) dlrm.fit(train_data, epochs=10)

References

[1] Naumov, Maxim, et al. “Deep learning recommendation model for

personalization and recommendation systems.” arXiv preprint arXiv:1906.00091 (2019).

Parameters

  • schema (Schema) – The Schema with the input features

  • embeddings (Optional[Block]) – Optional block for categorical embeddings. Overrides the default embeddings inferred from the schema.

  • embedding_dim (int) – Dimension of the embeddings

  • embedding_options (Optional[EmbeddingOptions]) – Configuration for categorical embeddings. Alternatively use the embeddings parameter.

  • bottom_block (Block) – The Block that combines the continuous features (typically a MLPBlock)

  • top_block (Optional[Block], optional) – The optional Block that combines the outputs of bottom layer and of the factorization machine layer, by default None

  • prediction_tasks (optional) – The prediction tasks to be used, by default this will be inferred from the Schema.

Return type

Model