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

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

See the guide: Neural Network > Embeddings

Looks up ids in a list of embedding tensors.

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.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:].

#### 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 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: Whether or not to validate gather indices.
• max_norm: If not None, embedding values are l2-normalized to the value of max_norm.

#### Returns:

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

#### Raises:

• ValueError: If params is empty.