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Lookup embedding results, accounting for invalid IDs and empty features.
tf.compat.v2.nn.safe_embedding_lookup_sparse(
embedding_weights, sparse_ids, sparse_weights=None, combiner='mean',
default_id=None, max_norm=None, name=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 list of P float Tensor s or values representing
partitioned embedding Tensor s. Alternatively, a PartitionedVariable
created by partitioning along dimension 0. The total unpartitioned shape
should be [e_0, e_1, ..., e_m] , where e_0 represents the vocab size
and e_1, ..., e_m are the embedding dimensions.
|
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. |
max_norm
|
If not None , all embeddings are l2-normalized to max_norm before
combining.
|
name
|
A name for this operation (optional). |
Returns | |
---|---|
Dense Tensor of shape [d_0, d_1, ..., d_{n-1}, e_1, ..., e_m] .
|
Raises | |
---|---|
ValueError
|
if embedding_weights is empty.
|