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# Copyright 2015 Google Inc. 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.
# ==============================================================================
"""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
import contextlib
import copy
import linecache
import re
import sys
import threading
import weakref
import tensorflow.python.platform
import six
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import registry
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import versions
from tensorflow.python.util import compat
from tensorflow.python.platform import logging
def _convert_stack(stack):
"""Converts a stack extracted using _extract_stack() to a traceback stack.
Args:
stack: A list of n 4-tuples, (filename, lineno, name, frame_globals).
Returns:
A list of n 4-tuples (filename, lineno, name, code), where the code tuple
element is calculated from the corresponding elements of the input tuple.
"""
ret = []
for filename, lineno, name, frame_globals in stack:
linecache.checkcache(filename)
line = linecache.getline(filename, lineno, frame_globals)
if line:
line = line.strip()
else:
line = None
ret.append((filename, lineno, name, line))
return ret
# pylint: disable=line-too-long
def _extract_stack():
"""A lightweight re-implementation of traceback.extract_stack.
NOTE(mrry): traceback.extract_stack eagerly retrieves the line of code for
each stack frame using linecache, which results in an abundance of stat()
calls. This implementation does not retrieve the code, and any consumer
should apply _convert_stack to the result to obtain a traceback that can
be formatted etc. using traceback methods.
Returns:
A list of 4-tuples (filename, lineno, name, frame_globals) corresponding to
the call stack of the current thread.
"""
# pylint: enable=line-too-long
try:
raise ZeroDivisionError
except ZeroDivisionError:
f = sys.exc_info()[2].tb_frame.f_back
ret = []
while f is not None:
lineno = f.f_lineno
co = f.f_code
filename = co.co_filename
name = co.co_name
frame_globals = f.f_globals
ret.append((filename, lineno, name, frame_globals))
f = f.f_back
ret.reverse()
return ret
def _as_graph_element(obj):
"""Convert `obj` to a graph element if possible, otherwise return `None`.
Args:
obj: Object to convert.
Returns:
The result of `obj._as_graph_element()` if that method is available;
otherwise `None`.
"""
conv_fn = getattr(obj, "_as_graph_element", None)
if conv_fn and callable(conv_fn):
return conv_fn()
return None
class Tensor(object):
"""Represents a value produced by an `Operation`.
A `Tensor` is a symbolic handle to one of the outputs of an
`Operation`. It does not hold the values of that operation's output,
but instead provides a means of computing those values in a
TensorFlow [`Session`](../../api_docs/python/client.md#Session).
This class has two primary purposes:
1. A `Tensor` can be passed as an input to another `Operation`.
This builds a dataflow connection between operations, which
enables TensorFlow to execute an entire `Graph` that represents a
large, multi-step computation.
2. After the graph has been launched in a session, the value of the
`Tensor` can be computed by passing it to
[`Session.run()`](../../api_docs/python/client.md#Session.run).
`t.eval()` is a shortcut for calling
`tf.get_default_session().run(t)`.
In the following example, `c`, `d`, and `e` are symbolic `Tensor`
objects, whereas `result` is a numpy array that stores a concrete
value:
```python
# Build a dataflow graph.
c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)
# Construct a `Session` to execute the graph.
sess = tf.Session()
# Execute the graph and store the value that `e` represents in `result`.
result = sess.run(e)
```
@@dtype
@@name
@@value_index
@@graph
@@op
@@consumers
@@eval
@@get_shape
@@set_shape
"""
# List of Python operators that we allow to override.
OVERLOADABLE_OPERATORS = {
# Binary.
"__add__",
"__radd__",
"__sub__",
"__rsub__",
"__mul__",
"__rmul__",
"__div__",
"__rdiv__",
"__truediv__",
"__rtruediv__",
"__floordiv__",
"__rfloordiv__",
"__mod__",
"__rmod__",
"__lt__",
"__le__",
"__gt__",
"__ge__",
"__and__",
"__rand__",
"__or__",
"__ror__",
"__xor__",
"__rxor__",
"__getitem__",
"__pow__",
"__rpow__",
# Unary.
"__invert__",
"__neg__",
"__abs__"
}
def __init__(self, op, value_index, dtype):
"""Creates a new `Tensor`.
Args:
op: An `Operation`. `Operation` that computes this tensor.
value_index: An `int`. Index of the operation's endpoint that produces
this tensor.
dtype: A `DType`. Type of elements stored in this tensor.
Raises:
TypeError: If the op is not an `Operation`.
"""
if not isinstance(op, Operation):
raise TypeError("op needs to be an Operation: %s" % op)
self._op = op
self._value_index = value_index
self._dtype = dtypes.as_dtype(dtype)
self._shape = tensor_shape.unknown_shape()
# List of operations that use this Tensor as input. We maintain this list
# to easily navigate a computation graph.
self._consumers = []
@property
def op(self):
"""The `Operation` that produces this tensor as an output."""
return self._op
@property
def dtype(self):
"""The `DType` of elements in this tensor."""
return self._dtype
@property
def graph(self):
"""The `Graph` that contains this tensor."""
return self._op.graph
@property
def name(self):
"""The string name of this tensor."""
if not self._op.name:
raise ValueError("Operation was not named: %s" % self._op)
return "%s:%d" % (self._op.name, self._value_index)
@property
def device(self):
"""The name of the device on which this tensor will be produced, or None."""
return self._op.device
def _shape_as_list(self):
if self._shape.ndims is not None:
return [dim.value for dim in self._shape.dims]
else:
return None
def get_shape(self):
"""Returns the `TensorShape` that represents the shape of this tensor.
The shape is computed using shape inference functions that are
registered for each `Operation` type using `tf.RegisterShape`.
See [`TensorShape`](../../api_docs/python/framework.md#TensorShape) for more
details of what a shape represents.
The inferred shape of a tensor is used to provide shape
information without having to launch the graph in a session. This
can be used for debugging, and providing early error messages. For
example:
```python
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
print(c.get_shape())
==> TensorShape([Dimension(2), Dimension(3)])
d = tf.constant([[1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0]])
print(d.get_shape())
==> TensorShape([Dimension(4), Dimension(2)])
# Raises a ValueError, because `c` and `d` do not have compatible
# inner dimensions.
e = tf.matmul(c, d)
f = tf.matmul(c, d, transpose_a=True, transpose_b=True)
print(f.get_shape())
==> TensorShape([Dimension(3), Dimension(4)])
```
In some cases, the inferred shape may have unknown dimensions. If
the caller has additional information about the values of these
dimensions, `Tensor.set_shape()` can be used to augment the
inferred shape.
Returns:
A `TensorShape` representing the shape of this tensor.
"""
return self._shape
def set_shape(self, shape):
"""Updates the shape of this tensor.
This method can be called multiple times, and will merge the given
`shape` with the current shape of this tensor. It can be used to
provide additional information about the shape of this tensor that
cannot be inferred from the graph alone. For example, this can be used
to provide additional information about the shapes of images:
```python
_, image_data = tf.TFRecordReader(...).read(...)
image = tf.image.decode_png(image_data, channels=3)
# The height and width dimensions of `image` are data dependent, and
# cannot be computed without executing the op.
print(image.get_shape())
==> TensorShape([Dimension(None), Dimension(None), Dimension(3)])
# We know that each image in this dataset is 28 x 28 pixels.
image.set_shape([28, 28, 3])
print(image.get_shape())
==> TensorShape([Dimension(28), Dimension(28), Dimension(3)])
```
Args:
shape: A `TensorShape` representing the shape of this tensor.
Raises:
ValueError: If `shape` is not compatible with the current shape of
this tensor.
"""
self._shape = self._shape.merge_with(shape)
@property
def value_index(self):
"""The index of this tensor in the outputs of its `Operation`."""
return self._value_index
def consumers(self):
"""Returns a list of `Operation`s that consume this tensor.
Returns:
A list of `Operation`s.
"""
return self._consumers
def _add_consumer(self, consumer):
"""Add a consumer to this tensor.
Args:
consumer: an Operation.
Raises:
TypeError: if the consumer is not an Operation.
"""
if not isinstance(consumer, Operation):
raise TypeError("Consumer must be an Operation: %s" % consumer)
self._consumers.append(consumer)
def _as_node_def_input(self):
"""Return a value to use for the NodeDef "input" attribute.
The returned string can be used in a NodeDef "input" attribute
to indicate that the NodeDef uses this Tensor as input.
Raises:
ValueError: if this Tensor's Operation does not have a name.
Returns:
a string.
"""
if not self._op.name:
raise ValueError("Operation was not named: %s" % self._op)
if self._value_index == 0:
return self._op.name
else:
return "%s:%d" % (self._op.name, self._value_index)
def __str__(self):
return "Tensor(\"%s\"%s%s%s)" % (
self.name,
(", shape=%s" % self.get_shape())
if self.get_shape().ndims is not None else "",
(", dtype=%s" % self._dtype.name) if self._dtype else "",
(", device=%s" % self.device) if self.device else "")
def __repr__(self):
return "<tf.Tensor '%s' shape=%s dtype=%s>" % (
self.name, self.get_shape(), self._dtype.name)
def __hash__(self):
# Necessary to support Python's collection membership operators
return id(self)
def __eq__(self, other):
# Necessary to support Python's collection membership operators
return id(self) == id(other)
# NOTE(mrry): This enables the Tensor's overloaded "right" binary
# operators to run when the left operand is an ndarray, because it
# accords the Tensor class higher priority than an ndarray, or a
# numpy matrix.
# TODO(mrry): Convert this to using numpy's __numpy_ufunc__
# mechanism, which allows more control over how Tensors interact
# with ndarrays.
__array_priority__ = 100
@staticmethod
def _override_operator(operator, func):
"""Overrides (string) operator on Tensors to call func.
Args:
operator: the string name of the operator to override.
func: the function that replaces the overriden operator.
Raises:
ValueError: If operator has already been overwritten,
or if operator is not allowed to be overwritten.
"""
existing = getattr(Tensor, operator, None)
if existing is not None:
# Check to see if this is a default method-wrapper or slot wrapper which
# will be true for the comparison operators.
if not isinstance(existing, type(object.__lt__)):
raise ValueError("operator %s cannot be overwritten again." % operator)
if operator not in Tensor.OVERLOADABLE_OPERATORS:
raise ValueError("Overriding %s is disallowed" % operator)
setattr(Tensor, operator, func)
def __iter__(self):
"""Dummy method to prevent iteration. Do not call.
NOTE(mrry): If we register __getitem__ as an overloaded operator,
Python will valiantly attempt to iterate over the Tensor from 0 to
infinity. Declaring this method prevents this unintended
behavior.
Raises:
TypeError: when invoked.
"""
raise TypeError("'Tensor' object is not iterable")
def eval(self, feed_dict=None, session=None):
"""Evaluates this tensor in a `Session`.
Calling this method will execute all preceding operations that
produce the inputs needed for the operation that produces this
tensor.
*N.B.* Before invoking `Tensor.eval()`, its graph must have been
launched in a session, and either a default session must be
available, or `session` must be specified explicitly.
Args:
feed_dict: A dictionary that maps `Tensor` objects to feed values.
See [`Session.run()`](../../api_docs/python/client.md#Session.run) for a
description of the valid feed values.
session: (Optional.) The `Session` to be used to evaluate this tensor. If
none, the default session will be used.
Returns:
A numpy array corresponding to the value of this tensor.
"""
return _eval_using_default_session(self, feed_dict, self.graph, session)
def _TensorTensorConversionFunction(t, dtype=None, name=None, as_ref=False):
_ = name, as_ref
if dtype and not dtype.is_compatible_with(t.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
% (dtype.name, t.dtype.name, str(t)))
return t
_tensor_conversion_func_registry = {
0: [(Tensor, _TensorTensorConversionFunction)]}
def convert_to_tensor(value, dtype=None, name=None, as_ref=False):
"""Converts the given `value` to a `Tensor`.
This function converts Python objects of various types to `Tensor`
objects. It accepts `Tensor` objects, numpy arrays, Python lists,
and Python scalars. For example:
```python
import numpy as np
array = np.random.rand(32, 100, 100)
def my_func(arg):
arg = tf.convert_to_tensor(arg, dtype=tf.float32)
return tf.matmul(arg, arg) + arg
# The following calls are equivalent.
value_1 = my_func(tf.constant([[1.0, 2.0], [3.0, 4.0]]))
value_2 = my_func([[1.0, 2.0], [3.0, 4.0]])
value_3 = my_func(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32))
```
This function can be useful when composing a new operation in Python
(such as `my_func` in the example above). All standard Python op
constructors apply this function to each of their Tensor-valued
inputs, which allows those ops to accept numpy arrays, Python lists,
and scalars in addition to `Tensor` objects.
Args:
value: 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.
as_ref: True if we want the result as a ref tensor.
Returns:
A `Tensor` based on `value`.
Raises:
TypeError: If no conversion function is registered for `value`.
RuntimeError: If a registered conversion function returns an invalid value.
"""
error_prefix = "" if name is None else "%s: " % name
if dtype is not None:
dtype = dtypes.as_dtype(dtype)
for _, funcs_at_priority in sorted(_tensor_conversion_func_registry.items()):
for base_type, conversion_func in funcs_at_priority:
if isinstance(value, base_type):
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
if not isinstance(ret, Tensor):
raise RuntimeError(
"%sConversion function %r for type %s returned non-Tensor: %r"
% (error_prefix, conversion_func, base_type, ret))
if dtype and not dtype.is_compatible_with(ret.dtype):
raise RuntimeError(
"%sConversion function %r for type %s returned incompatible "
"dtype: requested = %s, actual = %s"
% (error_prefix, conversion_func, base_type,
dtype.name, ret.dtype.name))
return ret
raise TypeError("%sCannot convert %r with type %s to Tensor: "
"no conversion function registered."
% (error_prefix, value, type(value)))
def convert_n_to_tensor(values, dtype=None, name=None, as_ref=False):
"""Converts `values` to a list of `Tensor` objects.
Args:
values: A list of objects that can be consumed by `tf.convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` objects.
name: (Optional.) A name prefix to used when a new `Tensor` is
created, in which case element `i` will be given the name `name
+ '_' + i`.
as_ref: True if the caller wants the results as ref tensors.
Returns:
A list of `Tensor` and/or `IndexedSlices` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
if not isinstance(values, collections.Sequence):
raise TypeError("values must be a list.")
ret = []
for i, value in enumerate(values):
n = None if name is None else "%s_%d" % (name, i)
ret.append(convert_to_tensor(value, dtype=dtype, name=n, as_ref=as_ref))
return ret
def convert_to_tensor_or_indexed_slices(value, dtype=None, name=None,
as_ref=False):
"""Converts the given object to a `Tensor` or an `IndexedSlices`.
If `value` is an `IndexedSlices` it is returned
unmodified. Otherwise, it is converted to a `Tensor` using
`convert_to_tensor()`.
Args:
value: An `IndexedSlices` or an object that can be consumed by
`convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor` or
`IndexedSlices`.
name: (Optional.) A name to use if a new `Tensor` is created.
as_ref: True if the caller wants the results as ref tensors.
Returns:
An `Tensor` or an `IndexedSlices` based on `value`.
Raises:
ValueError: If `dtype` does not match the element type of `value`.
"""
if isinstance(value, IndexedSlices):
if dtype and not dtypes.as_dtype(dtype).is_compatible_with(value.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for Tensor with dtype %s: %r"
% (dtypes.as_dtype(dtype).name, value.dtype.name, str(value)))
return value
else:
return convert_to_tensor(value, dtype=dtype, name=name, as_ref=as_ref)
def convert_n_to_tensor_or_indexed_slices(values, dtype=None, name=None,
as_ref=False):
"""Converts `values` to a list of `Tensor` or `IndexedSlices` objects.
Args:
values: A list of `None`, `IndexedSlices`, or objects that can be consumed
by `convert_to_tensor()`.
dtype: (Optional.) The required `DType` of the returned `Tensor`
`IndexedSlices`.
name: (Optional.) A name prefix to used when a new `Tensor` is
created, in which case element `i` will be given the name `name
+ '_' + i`.
as_ref: True if the caller wants the results as ref tensors.
Returns:
A list of `Tensor` and/or `IndexedSlices` objects.
Raises:
TypeError: If no conversion function is registered for an element in
`values`.
RuntimeError: If a registered conversion function returns an invalid
value.
"""
if not isinstance(values, collections.Sequence):
raise TypeError("values must be a list.")
ret = []
for i, value in enumerate(values):
if value is None:
ret.append(value)
else:
n = None if name is None else "%s_%d" % (name, i)
ret.append(
convert_to_tensor_or_indexed_slices(value, dtype=dtype, name=n,
as_ref=as_ref))
return ret
def register_tensor_conversion_function(base_type, conversion_func,
priority=100):
"""Registers a function for converting objects of `base_type` to `Tensor`.
The conversion function must have the following signature:
def conversion_func(value, dtype=None, name=None, as_ref=False):
# ...
It must return a `Tensor` with the given `dtype` if specified. If the
conversion function creates a new `Tensor`, it should use the given
`name` if specified. All exceptions will be propagated to the caller.
If `as_ref` is true, the function must return a `Tensor` reference,
such as a `Variable`.
NOTE: The conversion functions will execute in order of priority,
followed by order of registration. To ensure that a conversion function
`F` runs before another conversion function `G`, ensure that `F` is
registered with a smaller priority than `G`.
Args:
base_type: The base type or tuple of base types for all objects that
`conversion_func` accepts.
conversion_func: A function that converts instances of `base_type` to
`Tensor`.
priority: Optional integer that indicates the priority for applying this
conversion function. Conversion functions with smaller priority values
run earlier than conversion functions with larger priority values.
Defaults to 100.
Raises:
TypeError: If the arguments do not have the appropriate type.
"""
if not (isinstance(base_type, type) or
(isinstance(base_type, tuple)
and all(isinstance(x, type) for x in base_type))):
raise TypeError("base_type must be a type or a tuple of types.")
if not callable(conversion_func):
raise TypeError("conversion_func must be callable.")
try:
funcs_at_priority = _tensor_conversion_func_registry[priority]
except KeyError:
funcs_at_priority = []
_tensor_conversion_func_registry[priority] = funcs_at_priority
funcs_at_priority.append((base_type, conversion_func))
class IndexedSlices(object):
"""A sparse representation of a set of tensor slices at given indices.
This class is a simple wrapper for a pair of `Tensor` objects:
* `values`: A `Tensor` of any dtype with shape `[D0, D1, ..., Dn]`.
* `indices`: A 1-D integer `Tensor` with shape `[D0]`.
An `IndexedSlices` is typically used to represent a subset of a larger
tensor `dense` of shape `[LARGE0, D1, .. , DN]` where `LARGE0 >> D0`.
The values in `indices` are the indices in the first dimension of
the slices that have been extracted from the larger tensor.
The dense tensor `dense` represented by an `IndexedSlices` `slices` has
```python
dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]
```
The `IndexedSlices` class is used principally in the definition of
gradients for operations that have sparse gradients
(e.g. [`tf.gather`](../../api_docs/python/array_ops.md#gather)).
Contrast this representation with
[`SparseTensor`](../../api_docs/python/sparse_ops.md#SparseTensor),
which uses multi-dimensional indices and scalar values.
@@__init__
@@values
@@indices
@@dense_shape
@@name
@@dtype
@@device
@@op
"""
def __init__(self, values, indices, dense_shape=None):
"""Creates an `IndexedSlices`."""
_get_graph_from_inputs([values, indices, dense_shape])
self._values = values
self._indices = indices
self._dense_shape = dense_shape
@property
def values(self):
"""A `Tensor` containing the values of the slices."""
return self._values
@property
def indices(self):
"""A 1-D `Tensor` containing the indices of the slices."""
return self._indices
@property
def dense_shape(self):
"""A 1-D `Tensor` containing the shape of the corresponding dense tensor."""
return self._dense_shape
@property
def name(self):
"""The name of this `IndexedSlices`."""
return self.values.name
@property
def device(self):
"""The name of the device on which `values` will be produced, or `None`."""
return self.values.device
@property
def op(self):
"""The `Operation` that produces `values` as an output."""
return self.values.op
@property
def dtype(self):
"""The `DType` of elements in this tensor."""
return self.values.dtype
@property
def graph(self):
"""The `Graph` that contains the values, indices, and shape tensors."""
return self._values.graph
def __str__(self):
return "IndexedSlices(indices=%s, values=%s%s)" % (
self._indices, self._values,
(", dense_shape=%s" % self._dense_shape) if self._dense_shape else "")
IndexedSlicesValue = collections.namedtuple("IndexedSlicesValue",
["values", "indices", "dense_shape"])
class SparseTensor(object):
"""Represents a sparse tensor.
Tensorflow represents a sparse tensor as three separate dense tensors:
`indices`, `values`, and `shape`. In Python, the three tensors are
collected into a `SparseTensor` class for ease of use. If you have separate
`indices`, `values`, and `shape` tensors, wrap them in a `SparseTensor`
object before passing to the ops below.
Concretely, the sparse tensor `SparseTensor(values, indices, shape)` is
* `indices`: A 2-D int64 tensor of shape `[N, ndims]`.
* `values`: A 1-D tensor of any type and shape `[N]`.
* `shape`: A 1-D int64 tensor of shape `[ndims]`.
where `N` and `ndims` are the number of values, and number of dimensions in
the `SparseTensor` respectively.
The corresponding dense tensor satisfies
```python
dense.shape = 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
```python
SparseTensor(values=[1, 2], indices=[[0, 0], [1, 2]], shape=[3, 4])
```
represents the dense tensor
```python
[[1, 0, 0, 0]
[0, 0, 2, 0]
[0, 0, 0, 0]]
```
@@__init__
@@indices
@@values
@@dtype
@@shape
@@graph
"""
def __init__(self, indices, values, shape):
"""Creates a `SparseTensor`.
Args:
indices: A 2-D int64 tensor of shape `[N, ndims]`.
values: A 1-D tensor of any type and shape `[N]`.
shape: A 1-D int64 tensor of shape `[ndims]`.
Returns:
A `SparseTensor`
"""
with op_scope([indices, values, shape], None, "SparseTensor"):
indices = 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 = convert_to_tensor(values, name="values", as_ref=True)
shape = convert_to_tensor(shape, name="shape", dtype=dtypes.int64)
self._indices = indices
self._values = values
self._shape = shape
indices_shape = indices.get_shape().with_rank(2)
values_shape = values.get_shape().with_rank(1)
shape_shape = shape.get_shape().with_rank(1)
# Assert number of rows in indices match the number of elements in values.
indices_shape[0].merge_with(values_shape[0])
# Assert number of columns in indices matches the number of elements in
# shape.
indices_shape[1].merge_with(shape_shape[0])
@property
def indices(self):
"""The indices of non-zero values in the represented dense tensor.
Returns:
A 2-D Tensor of int64 with shape `[N, ndims]`, where `N` is the
number of non-zero values in the tensor, and `ndims` is the rank.
"""
return self._indices
@property
def values(self):
"""The non-zero values in the represented dense tensor.
Returns:
A 1-D Tensor of any data type.
"""
return self._values
@property
def dtype(self):
"""The `DType` of elements in this tensor."""
return self._values.dtype
@property
def shape(self):
"""A 1-D Tensor of int64 representing the shape of the dense tensor."""
return self._shape
@property
def graph(self):
"""The `Graph` that contains the index, value, and shape tensors."""
return self._indices.graph
def __str__(self):
return "SparseTensor(indices=%s, values=%s, shape=%s)" % (
self._indices, self._values, self._shape)
SparseTensorValue = collections.namedtuple("SparseTensorValue",
["indices", "values", "shape"])
def _device_string(dev_spec):
if isinstance(dev_spec, pydev.Device):
return dev_spec.to_string()
else:
return dev_spec
def _NodeDef(op_type, name, device=None, attrs=None):
"""Create a NodeDef proto.
Args:
op_type: Value for the "op" attribute of the NodeDef proto.
name: Value for the "name" attribute of the NodeDef proto.
device: string, device, or function from NodeDef to string.
Value for the "device" attribute of the NodeDef proto.
attrs: Optional dictionary where the key is the attribute name (a string)
and the value is the respective "attr" attribute of the NodeDef proto (an
AttrValue).
Returns:
A graph_pb2.NodeDef protocol buffer.
"""
node_def = graph_pb2.NodeDef()
node_def.op = compat.as_bytes(op_type)
node_def.name = compat.as_bytes(name)
if attrs is not None:
for k, v in six.iteritems(attrs):
node_def.attr[k].CopyFrom(v)
if device is not None:
if callable(device):
node_def.device = device(node_def)
else:
node_def.device = _device_string(device)
return node_def
# Copied from core/framework/node_def_util.cc
# TODO(mrry,josh11b): Consolidate this validation in C++ code.
_VALID_OP_NAME_REGEX = re.compile("[A-Za-z0-9.][A-Za-z0-9_.\\-/]*")
class Operation(object):
"""Represents a graph node that performs computation on tensors.
An `Operation` is a node in a TensorFlow `Graph` that takes zero or
more `Tensor` objects as input, and produces zero or more `Tensor`
objects as output. Objects of type `Operation` are created by
calling a Python op constructor (such as
[`tf.matmul()`](../../api_docs/python/math_ops.md#matmul))
or [`Graph.create_op()`](../../api_docs/python/framework.md#Graph.create_op).
For example `c = tf.matmul(a, b)` creates an `Operation` of type
"MatMul" that takes tensors `a` and `b` as input, and produces `c`
as output.
After the graph has been launched in a session, an `Operation` can
be executed by passing it to
[`Session.run()`](../../api_docs/python/client.md#Session.run).
`op.run()` is a shortcut for calling `tf.get_default_session().run(op)`.
@@name
@@type
@@inputs
@@control_inputs
@@outputs
@@device
@@graph
@@run
@@get_attr
@@traceback
"""
def __init__(self, node_def, g, inputs=None, output_types=None,
control_inputs=None, input_types=None, original_op=None,
op_def=None):
"""Creates an `Operation`.
NOTE: This constructor validates the name of the `Operation` (passed
as `node_def.name`). Valid `Operation` names match the following
regular expression: