tfrs.layers.factorized_top_k.BruteForce

Brute force retrieval.

Inherits From: TopK

tfrs.layers.factorized_top_k.BruteForce(
    query_model: Optional[tf.keras.Model] = None,
    k: int = 10,
    name: Optional[Text] = None
)

Used in the tutorials
Efficient serving Recommending movies: retrieval TensorFlow Recommenders: Quickstart

Args
`query_model`	Optional Keras model for representing queries. If provided, will be used to transform raw features into query embeddings when querying the layer. If not provided, the layer will expect to be given query embeddings as inputs.
`k`	Default k.
`name`	Name of the layer.

Methods

call(
    queries: Union[tf.Tensor, Dict[Text, tf.Tensor]],
    k: Optional[int] = None
) -> Tuple[tf.Tensor, tf.Tensor]

Query the index.

Args
`queries`	Query features. If `query_model` was provided in the constructor, these can be raw query features that will be processed by the query model before performing retrieval. If `query_model` was not provided, these should be pre-computed query embeddings.
`k`	The number of candidates to retrieve. If not supplied, defaults to the k value supplied in the constructor.

Returns
Tuple of (top candidate scores, top candidate identifiers).

Raises
ValueError if `index` has not been called.

index(
    candidates: Union[tf.Tensor, tf.data.Dataset],
    identifiers: Optional[Union[tf.Tensor, tf.data.Dataset]] = None
) -> "BruteForce"

Builds the retrieval index.

When called multiple times the existing index will be dropped and a new one created.

Args
`candidates`	Matrix (or dataset) of candidate embeddings.
`identifiers`	Optional tensor (or dataset) of candidate identifiers. If given these will be return to identify top candidates when performing searches. If not given, indices into the candidates tensor will be given instead.

Returns
Self.