tf.nn.embedding_lookup

TensorFlow 2 version View source on GitHub

Looks up ids in a list of embedding tensors.

tf.nn.embedding_lookup(
    params, ids, partition_strategy='mod', name=None, validate_indices=True,
    max_norm=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.

If partition_strategy is "mod", we assign each id to partition p = id % len(params). For instance, 13 ids are split across 5 partitions as: [[0, 5, 10], [1, 6, 11], [2, 7, 12], [3, 8], [4, 9]]

If partition_strategy is "div", we assign ids to partitions in a contiguous manner. In this case, 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:].

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 given partition_strategy.
ids A Tensor with type int32 or int64 containing the ids to be looked up in params.
partition_strategy A string specifying the partitioning strategy, relevant if len(params) > 1. Currently "div" and "mod" are supported. Default is "mod".
name A name for the operation (optional).
validate_indices DEPRECATED. If this operation is assigned to CPU, values in indices are always validated to be within range. If assigned to GPU, out-of-bound indices result in safe but unspecified behavior, which may include raising an error.
max_norm If not None, each embedding is clipped if its l2-norm is larger than this value.

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

ValueError If params is empty.