tf.nn.embedding_lookup_sparse

Looks up embeddings for the given ids and weights from a list of tensors.

This op assumes that there is at least one id for each row in the dense tensor represented by sp_ids (i.e. there are no rows with empty features), and that all the indices of sp_ids are in canonical row-major order.

sp_ids and sp_weights (if not None) are SparseTensors with rank of 2. Embeddings are always aggregated along the last dimension.

It also assumes that all id values lie in the range [0, p0), where p0 is the sum of the size of params along dimension 0.

If len(params) > 1, each element of sp_ids is partitioned between the elements of params according to the "div" partition strategy, which means we assign ids to partitions in a contiguous manner. For instance, 13 ids are split across 5 partitions as: [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]].

If the id space does not evenly divide the number of partitions, each of the first (max_id + 1) % len(params) partitions will be assigned one more id.

params A single tensor representing the complete embedding tensor, or a list of tensors all of same shape except for the first dimension, representing sharded embedding tensors following "div" partition strategy.
sp_ids N x M SparseTensor of int64 ids where N is typically batch size and M is arbitrary.
sp_weights either a SparseTensor of float / double weights, or None to indicate all weights should be taken to be 1. If specified, sp_weights must have exactly the same shape and indices as sp_ids.