tf.nn.embedding_lookup

Looks up ids in a list of embedding tensors.

tf.nn.embedding_lookup(
    params, ids, max_norm=None, name=None
)

This function is used to perform parallel lookups on the list of tensors in params. It is a generalization of tf.gather, where params is interpreted as a partitioning of a large embedding tensor. params may be a PartitionedVariable as returned by using tf.compat.v1.get_variable() with a partitioner.

If len(params) > 1, each element id of ids is partitioned between the elements of params according to the partition_strategy. In all strategies, 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.

The partition_strategy is always "div" currently. This means that 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]]

The results of the lookup are concatenated into a dense tensor. The returned tensor has shape shape(ids) + shape(params)[1:].

Args
params	A single tensor representing the complete embedding tensor, or a list of P 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 'div' partition_strategy.
ids	A Tensor with type int32 or int64 containing the ids to be looked up in params.
max_norm	If not None, each embedding is clipped if its l2-norm is larger than this value.
name	A name for the operation (optional).

Returns
A Tensor with the same type as the tensors in params.

Raises
ValueError	If params is empty.