tf.nn.embedding_lookup( params, ids, partition_strategy='mod', name=None, validate_indices=True, max_norm=None )
See the guide: Neural Network > Embeddings
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
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.
len(params) > 1, each element
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.
"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]]
"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
int64containing the ids to be looked up in
partition_strategy: A string specifying the partitioning strategy, relevant if
len(params) > 1. Currently
"mod"are supported. Default is
name: A name for the operation (optional).
validate_indices: DEPRECATED. If this operation is assigned to CPU, values in
indicesare 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 provided, embedding values are l2-normalized to the value of max_norm.
Tensor with the same type as the tensors in
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