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# Copyright 2015 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Classes and functions used to construct graphs."""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=protected-access
_TensorLike = ops._TensorLike
_eval_using_default_session = ops._eval_using_default_session
_override_helper = ops._override_helper
# pylint: enable=protected-access
class SparseTensor(_TensorLike):
"""Represents a sparse tensor.
TensorFlow represents a sparse tensor as three separate dense tensors:
`indices`, `values`, and `dense_shape`. In Python, the three tensors are
collected into a `SparseTensor` class for ease of use. If you have separate
`indices`, `values`, and `dense_shape` tensors, wrap them in a `SparseTensor`
object before passing to the ops below.
Concretely, the sparse tensor `SparseTensor(indices, values, dense_shape)`
comprises the following components, where `N` and `ndims` are the number
of values and number of dimensions in the `SparseTensor`, respectively:
* `indices`: A 2-D int64 tensor of dense_shape `[N, ndims]`, which specifies
the indices of the elements in the sparse tensor that contain nonzero
values (elements are zero-indexed). For example, `indices=[[1,3], [2,4]]`
specifies that the elements with indexes of [1,3] and [2,4] have
nonzero values.
* `values`: A 1-D tensor of any type and dense_shape `[N]`, which supplies the
values for each element in `indices`. For example, given
`indices=[[1,3], [2,4]]`, the parameter `values=[18, 3.6]` specifies
that element [1,3] of the sparse tensor has a value of 18, and element
[2,4] of the tensor has a value of 3.6.
* `dense_shape`: A 1-D int64 tensor of dense_shape `[ndims]`, which specifies
the dense_shape of the sparse tensor. Takes a list indicating the number of
elements in each dimension. For example, `dense_shape=[3,6]` specifies a
two-dimensional 3x6 tensor, `dense_shape=[2,3,4]` specifies a
three-dimensional 2x3x4 tensor, and `dense_shape=[9]` specifies a
one-dimensional tensor with 9 elements.
The corresponding dense tensor satisfies:
dense.shape = dense_shape
dense[tuple(indices[i])] = values[i]
By convention, `indices` should be sorted in row-major order (or equivalently
lexicographic order on the tuples `indices[i]`). This is not enforced when
`SparseTensor` objects are constructed, but most ops assume correct ordering.
If the ordering of sparse tensor `st` is wrong, a fixed version can be
obtained by calling `tf.sparse_reorder(st)`.
Example: The sparse tensor
SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
represents the dense tensor
[[1, 0, 0, 0]
[0, 0, 2, 0]
[0, 0, 0, 0]]
def from_value(cls, sparse_tensor_value):
if not is_sparse(sparse_tensor_value):
raise TypeError("Neither a SparseTensor nor SparseTensorValue: %s." %
return SparseTensor(
def __init__(self, indices, values, dense_shape):
"""Creates a `SparseTensor`.
indices: A 2-D int64 tensor of shape `[N, ndims]`.
values: A 1-D tensor of any type and shape `[N]`.
dense_shape: A 1-D int64 tensor of shape `[ndims]`.
A `SparseTensor`.
with ops.name_scope(None, "SparseTensor",
[indices, values, dense_shape]):
indices = ops.convert_to_tensor(
indices, name="indices", dtype=dtypes.int64)
# Always pass as_ref=True because we want to be able to update
# values later if it is a VariableOp.
# TODO(touts): Consider adding mutable_values() when 'values'
# is a VariableOp and updating users of SparseTensor.
values = ops.internal_convert_to_tensor(
values, name="values", as_ref=True)
dense_shape = ops.convert_to_tensor(
dense_shape, name="dense_shape", dtype=dtypes.int64)
self._indices = indices
self._values = values
self._dense_shape = dense_shape
indices_shape = indices.get_shape().with_rank(2)
values_shape = values.get_shape().with_rank(1)
dense_shape_shape = dense_shape.get_shape().with_rank(1)
# Assert number of rows in indices match the number of elements in values.
# Assert number of columns in indices matches the number of elements in
# dense_shape.
def get_shape(self):
"""Get the `TensorShape` representing the shape of the dense tensor.
A `TensorShape` object.
return tensor_util.constant_value_as_shape(self._dense_shape)
def indices(self):
"""The indices of non-zero values in the represented dense tensor.
A 2-D Tensor of int64 with dense_shape `[N, ndims]`, where `N` is the
number of non-zero values in the tensor, and `ndims` is the rank.
return self._indices
def values(self):
"""The non-zero values in the represented dense tensor.
A 1-D Tensor of any data type.
return self._values
def op(self):
"""The `Operation` that produces `values` as an output."""
return self.values.op
def dtype(self):
"""The `DType` of elements in this tensor."""
return self._values.dtype
def dense_shape(self):
"""A 1-D Tensor of int64 representing the shape of the dense tensor."""
return self._dense_shape
def graph(self):
"""The `Graph` that contains the index, value, and dense_shape tensors."""
return self._indices.graph
def __str__(self):
return "SparseTensor(indices=%s, values=%s, dense_shape=%s)" % (
self._indices, self._values, self._dense_shape)
def eval(self, feed_dict=None, session=None):
"""Evaluates this sparse tensor in a `Session`.
Calling this method will execute all preceding operations that
produce the inputs needed for the operation that produces this
*N.B.* Before invoking `SparseTensor.eval()`, its graph must have been
launched in a session, and either a default session must be
available, or `session` must be specified explicitly.
feed_dict: A dictionary that maps `Tensor` objects to feed values.
See @{} for a
description of the valid feed values.
session: (Optional.) The `Session` to be used to evaluate this sparse
tensor. If none, the default session will be used.
A `SparseTensorValue` object.
indices, values, dense_shape = _eval_using_default_session(
[self.indices, self.values, self.dense_shape], feed_dict, self.graph,
return SparseTensorValue(indices, values, dense_shape)
def _override_operator(operator, func):
_override_helper(SparseTensor, operator, func)
SparseTensorValue = collections.namedtuple(
"SparseTensorValue", ["indices", "values", "dense_shape"])
def convert_to_tensor_or_sparse_tensor(value, dtype=None, name=None):
"""Converts value to a `SparseTensor` or `Tensor`.
value: A `SparseTensor`, `SparseTensorValue`, or an object whose type has a
registered `Tensor` conversion function.
dtype: Optional element type for the returned tensor. If missing, the
type is inferred from the type of `value`.
name: Optional name to use if a new `Tensor` is created.
A `SparseTensor` or `Tensor` based on `value`.
RuntimeError: If result type is incompatible with `dtype`.
if dtype is not None:
dtype = dtypes.as_dtype(dtype)
if isinstance(value, SparseTensorValue):
value = SparseTensor.from_value(value)
if isinstance(value, SparseTensor):
if dtype and not dtype.is_compatible_with(value.dtype):
raise RuntimeError(
"Sparse dtype: requested = %s, actual = %s" % (,
return value
return ops.internal_convert_to_tensor(
value, dtype=dtype, name=name)
def is_sparse(x):
"""Check whether `x` is sparse.
Check whether an object is a `tf.SparseTensor` or `tf.SparseTensorValue`.
x: A python object to check.
`True` iff `x` is a `tf.SparseTensor` or `tf.SparseTensorValue`.
return isinstance(x, (SparseTensor, SparseTensorValue))