contrib.layers.bow_encoder
tf.contrib.layers.bow_encoder
tf.contrib.layers.bow_encoder
bow_encoder( ids, vocab_size, embed_dim, sparse_lookup=True, initializer=None, regularizer=None, trainable=True, scope=None, reuse=None )
Defined in tensorflow/contrib/layers/python/layers/encoders.py
.
Maps a sequence of symbols to a vector per example by averaging embeddings.
Args:
-
ids
:[batch_size, doc_length]
Tensor
orSparseTensor
of typeint32
orint64
with symbol ids. -
vocab_size
: Integer number of symbols in vocabulary. -
embed_dim
: Integer number of dimensions for embedding matrix. -
sparse_lookup
:bool
, ifTrue
, converts ids to aSparseTensor
and performs a sparse embedding lookup. This is usually faster, but not desirable if padding tokens should have an embedding. Empty rows are assigned a special embedding. -
initializer
: An initializer for the embeddings, ifNone
default for current scope is used. -
regularizer
: Optional regularizer for the embeddings. -
trainable
: IfTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable). -
scope
: Optional string specifying the variable scope for the op, required ifreuse=True
. -
reuse
: IfTrue
, variables inside the op will be reused.
Returns:
Encoding Tensor
[batch_size, embed_dim]
produced by averaging embeddings.
Raises:
-
ValueError
: Ifembed_dim
orvocab_size
are not specified.
© 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/bow_encoder