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tfra.dynamic_embedding.safe_embedding_lookup_sparse

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Provides a dynamic version of tf.nn.safe_embedding_lookup_sparse.

tfra.dynamic_embedding.safe_embedding_lookup_sparse(
    embedding_weights,
    sparse_ids,
    sparse_weights=None,
    combiner='mean',
    default_id=None,
    name='safe_embedding_lookup_sparse',
    partition_strategy=None,
    max_norm=None,
    return_trainable=(False)
)

Lookup embedding results, accounting for empty features and invalid weights.

Any IDs will be treated as valid include non-positive IDs. Invalid weights (<= 0) are pruned from input weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for default_id is returned, or the 0-vector if default_id is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.

Args:

  • embedding_weights: A single dynamic_embedding.Variable instance representing the complete embedding tensor.
  • sparse_ids: SparseTensor of shape [d_0, d_1, ..., d_n] containing the ids. d_0 is typically batch size.
  • sparse_weights: SparseTensor of same shape as sparse_ids, containing float weights corresponding to sparse_ids, or None if all weights are be assumed to be 1.0.
  • combiner: A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
  • default_id: The id to use for an entry with no features.
  • name: A name for this operation. Name is optional in graph mode and required in eager mode.
  • partition_strategy: A string specifying the partitioning strategy. Currently "div" and "mod" are supported. Default is "div".
  • max_norm: If not None, all embeddings are l2-normalized to max_norm before combining.

Returns:

  • combined_embeddings: A dense Tensor of shape [d_0, d_1, ..., d_{n-1}, e_1, ..., e_m].
  • trainable_wrap: A TrainableWrapper object used to fill the Optimizers var_list Only provided if return_trainable is True.

Raises:

  • ValueError: if embedding_weights is empty.