Looks up `ids`

in a list of embedding tensors.

tf.compat.v2.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. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/nn/embedding_lookup