|TensorFlow 1 version||View source on GitHub|
ids in a list of embedding tensors.
Compat aliases for migration
See Migration guide for more details.
tf.nn.embedding_lookup( params, ids, max_norm=None, name=None )
Used in the notebooks
|Used in the guide|
This function is used to perform parallel lookups on the list of
params. It is a generalization of
interpreted as a partitioning of a large embedding tensor.
params may be
PartitionedVariable as returned by using
len(params) > 1, each element
ids is partitioned between
the elements of
params according to the
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.
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:].
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'
int64containing the ids to be looked up in
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).
Tensor with the same type as the tensors in