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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.
# ==============================================================================
"""TensorFlow Ops to work with embeddings (deprecated).
This module and all its submodules are deprecated. See
[contrib/learn/README.md](https://www.tensorflow.org/code/tensorflow/contrib/learn/README.md)
for migration instructions.
Note: categorical variables are handled via embeddings in many cases.
For example, in case of words.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.framework import deprecated
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops as array_ops_
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
@deprecated('2016-12-01', 'Use `tf.embedding_lookup` instead.')
def embedding_lookup(params, ids, name='embedding_lookup'):
"""Provides a N dimensional version of tf.embedding_lookup.
Ids are flattened to a 1d tensor before being passed to embedding_lookup
then, they are unflattend to match the original ids shape plus an extra
leading dimension of the size of the embeddings.
Args:
params: List of tensors of size D0 x D1 x ... x Dn-2 x Dn-1.
ids: N-dimensional tensor of B0 x B1 x .. x Bn-2 x Bn-1.
Must contain indexes into params.
name: Optional name for the op.
Returns:
A tensor of size B0 x B1 x .. x Bn-2 x Bn-1 x D1 x ... x Dn-2 x Dn-1
containing the values from the params tensor(s) for indecies in ids.
Raises:
ValueError: if some parameters are invalid.
"""
with ops.name_scope(name, 'embedding_lookup', [params, ids]):
params = ops.convert_to_tensor(params)
ids = ops.convert_to_tensor(ids)
shape = array_ops_.shape(ids)
ids_flat = array_ops_.reshape(
ids, math_ops.reduce_prod(shape, keepdims=True))
embeds_flat = nn.embedding_lookup(params, ids_flat, name)
embed_shape = array_ops_.concat([shape, [-1]], 0)
embeds = array_ops_.reshape(embeds_flat, embed_shape)
embeds.set_shape(ids.get_shape().concatenate(params.get_shape()[1:]))
return embeds
@deprecated('2016-12-01', 'Use `tf.contrib.layers.embed_sequence` instead.')
def categorical_variable(tensor_in, n_classes, embedding_size, name):
"""Creates an embedding for categorical variable with given number of classes.
Args:
tensor_in: Input tensor with class identifier (can be batch or
N-dimensional).
n_classes: Number of classes.
embedding_size: Size of embedding vector to represent each class.
name: Name of this categorical variable.
Returns:
Tensor of input shape, with additional dimension for embedding.
Example:
Calling categorical_variable([1, 2], 5, 10, "my_cat"), will return 2 x 10
tensor, where each row is representation of the class.
"""
with vs.variable_scope(name):
embeddings = vs.get_variable(name + '_embeddings',
[n_classes, embedding_size])
return embedding_lookup(embeddings, tensor_in)
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