contrib.layers.safe_embedding_lookup_sparse
tf.contrib.layers.safe_embedding_lookup_sparse
tf.contrib.layers.safe_embedding_lookup_sparse
safe_embedding_lookup_sparse( embedding_weights, sparse_ids, sparse_weights=None, combiner=None, default_id=None, name=None, partition_strategy='div', max_norm=None )
Defined in tensorflow/contrib/layers/python/layers/embedding_ops.py
.
See the guide: Layers (contrib) > Higher level ops for building neural network layers
Lookup embedding results, accounting for invalid IDs and empty features.
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.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 ofP
float tensors or values representing partitioned embedding tensors. Alternatively, aPartitionedVariable
, created by partitioning along dimension 0. The total unpartitioned shape should be[e_0, e_1, ..., e_m]
, wheree_0
represents the vocab size ande_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 assparse_ids
, containing float weights corresponding tosparse_ids
, orNone
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:
Dense tensor of shape [d_0, d_1, ..., d_{n-1}, e_1, ..., e_m]
.
Raises:
-
ValueError
: ifembedding_weights
is empty.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/layers/safe_embedding_lookup_sparse