Lookup embedding results, accounting for invalid IDs and empty features.
tf.compat.v1.nn.safe_embedding_lookup_sparse(
embedding_weights,
sparse_ids,
sparse_weights=None,
combiner='mean',
default_id=None,
name=None,
partition_strategy='div',
max_norm=None
)
The partitioned embedding in embedding_weights
must all be the same shape
except for the first dimension. The first dimension is allowed to vary as the
vocabulary size is not necessarily a multiple of P
. embedding_weights
may be a PartitionedVariable
as returned by using
tf.compat.v1.get_variable()
with a
partitioner.
Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs
with non-positive weight. For an entry with no features, the embedding vector
for default_id
is returned, or the 0-vector if default_id
is not supplied.
The ids and weights may be multi-dimensional. Embeddings are always aggregated
along the last dimension.
Args |
embedding_weights
|
A single tensor representing the complete embedding
tensor, or a list 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 .
|
sparse_ids
|
SparseTensor of shape [d_0, d_1, ..., d_n] containing the
ids. d_0 is typically batch size.
|
sparse_weights
|
SparseTensor of same shape as sparse_ids , containing
float weights corresponding to sparse_ids , or None if all weights are
be assumed to be 1.0.
|
combiner
|
A string specifying how to combine embedding results for each
entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the
default.
|
default_id
|
The id to use for an entry with no features.
|
name
|
A name for this operation (optional).
|
partition_strategy
|
A string specifying the partitioning strategy. Currently
"div" and "mod" are supported. Default is "div" .
|
max_norm
|
If not None , all embeddings are l2-normalized to max_norm 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
shape(combined embedding_weights) = [p0, p1, ..., pm]
and
shape(sparse_ids) = shape(sparse_weights) = [d0, d1, ..., dn]
then
shape(output) = [d0, d1, ... dn-1, p1, ..., pm] .
For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
[0, 0]: id 1, weight 2.0
[0, 1]: id 3, weight 0.5
[1, 0]: id -1, weight 1.0
[2, 3]: id 1, weight 3.0
default_id is 0.
with combiner ="mean", then the output will be a 3x20 matrix where
output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
output[1, :] = (params[0, :] * 1.0) / 1.0
output[2, :] = (params[1, :] * 3.0) / 3.0
|
Raises |
ValueError
|
if embedding_weights is empty.
|