merlin.models.tf.DLRMModel
-
merlin.models.tf.
DLRMModel
(schema: merlin.schema.schema.Schema, embedding_dim: int, 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
embedding_dim (int) – Dimension of the embeddings
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.
- Returns
- Return type
Model