Stay organized with collections Save and categorize content based on your preferences.

View source on GitHub

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

The partitioned embedding in embedding_weights must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of P. embedding_weights may be a PartitionedVariable as returned by using tf.compat.v1.get_variable() with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and 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.

embedding_weights A list of P float Tensors or values representing partitioned embedding Tensors. Alternatively, a PartitionedVariable created by partitioning along dimension 0. The total unpartitioned shape should be [e_0, e_1, ..., e_m], where e_0 represents the vocab size and e_1, ..., e_m are the embedding dimensions.
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 (optional).
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.

Dense Tensor of shape [d_0, d_1, ..., d_{n-1}, e_1, ..., e_m].

ValueError if embedding_weights is empty.