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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Encoders to transform sequence of symbols into vector representation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.framework.python.ops import variables
from tensorflow.contrib.layers.python.layers import embedding_ops as contrib_embedding_ops
from tensorflow.contrib.layers.python.ops import sparse_ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope
__all__ = ['bow_encoder', 'embed_sequence']
def bow_encoder(ids,
vocab_size,
embed_dim,
sparse_lookup=True,
initializer=None,
regularizer=None,
trainable=True,
scope=None,
reuse=None):
"""Maps a sequence of symbols to a vector per example by averaging embeddings.
Args:
ids: `[batch_size, doc_length]` `Tensor` or `SparseTensor` of type
`int32` or `int64` with symbol ids.
vocab_size: Integer number of symbols in vocabulary.
embed_dim: Integer number of dimensions for embedding matrix.
sparse_lookup: `bool`, if `True`, converts ids to a `SparseTensor`
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, if `None` default for
current scope is used.
regularizer: Optional regularizer for the embeddings.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional string specifying the variable scope for the op, required
if `reuse=True`.
reuse: If `True`, variables inside the op will be reused.
Returns:
Encoding `Tensor` `[batch_size, embed_dim]` produced by
averaging embeddings.
Raises:
ValueError: If `embed_dim` or `vocab_size` are not specified.
"""
if not vocab_size or not embed_dim:
raise ValueError('Must specify vocab size and embedding dimension')
with variable_scope.variable_scope(
scope, 'bow_encoder', [ids], reuse=reuse):
embeddings = variables.model_variable(
'embeddings', shape=[vocab_size, embed_dim],
initializer=initializer, regularizer=regularizer,
trainable=trainable)
if sparse_lookup:
if isinstance(ids, sparse_tensor.SparseTensor):
sparse_ids = ids
else:
sparse_ids = sparse_ops.dense_to_sparse_tensor(ids)
return contrib_embedding_ops.safe_embedding_lookup_sparse(
[embeddings], sparse_ids, combiner='mean', default_id=0)
else:
if isinstance(ids, sparse_tensor.SparseTensor):
raise TypeError('ids are expected to be dense Tensor, got: %s', ids)
return math_ops.reduce_mean(
embedding_ops.embedding_lookup(embeddings, ids),
reduction_indices=1)
def embed_sequence(ids,
vocab_size=None,
embed_dim=None,
unique=False,
initializer=None,
regularizer=None,
trainable=True,
scope=None,
reuse=None):
"""Maps a sequence of symbols to a sequence of embeddings.
Typical use case would be reusing embeddings between an encoder and decoder.
Args:
ids: `[batch_size, doc_length]` `Tensor` of type `int32` or `int64`
with symbol ids.
vocab_size: Integer number of symbols in vocabulary.
embed_dim: Integer number of dimensions for embedding matrix.
unique: If `True`, will first compute the unique set of indices, and then
lookup each embedding once, repeating them in the output as needed.
initializer: An initializer for the embeddings, if `None` default for
current scope is used.
regularizer: Optional regularizer for the embeddings.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
scope: Optional string specifying the variable scope for the op, required
if `reuse=True`.
reuse: If `True`, variables inside the op will be reused.
Returns:
`Tensor` of `[batch_size, doc_length, embed_dim]` with embedded sequences.
Raises:
ValueError: if `embed_dim` or `vocab_size` are not specified when
`reuse` is `None` or `False`.
"""
if not (reuse or (vocab_size and embed_dim)):
raise ValueError('Must specify vocab size and embedding dimension when not'
'reusing. Got vocab_size=%s and embed_dim=%s' % (
vocab_size, embed_dim))
with variable_scope.variable_scope(
scope, 'EmbedSequence', [ids], reuse=reuse):
shape = [vocab_size, embed_dim]
if reuse and vocab_size is None or embed_dim is None:
shape = None
embeddings = variables.model_variable(
'embeddings', shape=shape,
initializer=initializer, regularizer=regularizer,
trainable=trainable)
if unique:
return contrib_embedding_ops.embedding_lookup_unique(embeddings, ids)
return embedding_ops.embedding_lookup(embeddings, ids)
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