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Computes embeddings for the given ids and weights.
tf.compat.v1.nn.embedding_lookup_sparse(
params, sp_ids, sp_weights, partition_strategy='mod', name=None, combiner=None,
max_norm=None
)
This op assumes that there is at least one id for each row in the dense tensor represented by sp_ids (i.e. there are no rows with empty features), and that all the indices of sp_ids are in canonical row-major order.
It also assumes that all id values lie in the range [0, p0), where p0 is the sum of the size of params along dimension 0.
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 .
|
sp_ids
|
N x M SparseTensor of int64 ids where N is typically batch size
and M is arbitrary.
|
sp_weights
|
either a SparseTensor of float / double weights, or None to
indicate all weights should be taken to be 1. If specified, sp_weights
must have exactly the same shape and indices as sp_ids .
|
partition_strategy
|
A string specifying the partitioning strategy, relevant
if len(params) > 1 . Currently "div" and "mod" are supported. Default
is "mod" . See tf.nn.embedding_lookup for more details.
|
name
|
Optional name for the op. |
combiner
|
A string specifying the reduction op. Currently "mean", "sqrtn" and "sum" are supported. "sum" computes the weighted sum of the embedding results for each row. "mean" is the weighted sum divided by the total weight. "sqrtn" is the weighted sum divided by the square root of the sum of the squares of the weights. |
max_norm
|
If not None , each embedding is clipped if its l2-norm is larger
than this value, before combining.
|
Returns | |
---|---|
A dense tensor representing the combined embeddings for the
sparse ids. For each row in the dense tensor represented by sp_ids , the op
looks up the embeddings for all ids in that row, multiplies them by the
corresponding weight, and combines these embeddings as specified.
In other words, if
and
then
For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
with
|
Raises | |
---|---|
TypeError
|
If sp_ids is not a SparseTensor , or if sp_weights is
neither None nor SparseTensor .
|
ValueError
|
If combiner is not one of {"mean", "sqrtn", "sum"}.
|