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tf.compat.v1.nn.safe_embedding_lookup_sparse

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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 single tensor representing the complete embedding tensor, or a list tensors all of same shape except for the first dimension, representing sharded embedding tensors. Alternatively, a PartitionedVariable, created by partitioning along dimension 0. Each element must be appropriately sized for the given partition_strategy.
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.

A dense tensor representing the combined embeddings for the sparse ids. For each row in the dense tensor represented by sp_ids, the op looks up the embeddings for all ids in that row, multiplies them by the corresponding weight, and combines these embeddings as specified.

In other words, if

shape(combined embedding_weights) = [p0, p1, ..., pm]

and

shape(sparse_ids) = shape(sparse_weights) = [d0, d1, ..., dn]

then

shape(output) = [d0, d1, ... dn-1, p1, ..., pm].

For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are

[0, 0]: id 1, weight 2.0
[0, 1]: id 3, weight 0.5
[1, 0]: id -1, weight 1.0
[2, 3]: id 1, weight 3.0

default_id is 0.

with combiner="mean", then the output will be a 3x20 matrix where

output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
output[1, :] = (params[0, :] * 1.0) / 1.0
output[2, :] = (params[1, :] * 3.0) / 3.0

ValueError if embedding_weights is empty.