# 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

#

# 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 six

from tensorflow.core.framework import attr_value_pb2

from tensorflow.core.framework import function_pb2

from tensorflow.core.framework import graph_pb2

from tensorflow.core.framework import node_def_pb2

from tensorflow.core.framework import versions_pb2

from tensorflow.python.framework import device as pydev

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import op_def_registry

from tensorflow.python.framework import registry

from tensorflow.python.framework import tensor_shape

from tensorflow.python.framework import versions

from tensorflow.python.platform import tf_logging as logging

from tensorflow.python.util import compat

def _override_helper(clazz_object, operator, func):

"""Overrides (string) operator on Tensors to call func.

Args:

clazz_object: the class to override for; either Tensor or SparseTensor.

operator: the string name of the operator to override.

func: the function that replaces the overridden operator.

Raises:

ValueError: If operator has already been overwritten,

or if operator is not allowed to be overwritten.

"""

existing = getattr(clazz_object, 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 on class %s." %

(operator, clazz_object))

if operator not in Tensor.OVERLOADABLE_OPERATORS:

raise ValueError("Overriding %s is disallowed" % operator)

setattr(clazz_object, operator, func)

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

_TENSOR_LIKE_TYPES = tuple()

def is_dense_tensor_like(t):

"""EXPERIMENTAL: Returns true if `t` implements the tensor interface.

See `register_dense_tensor_like_type()` for the current definition of a

"tensor-like type".

Args:

t: An object.

Returns:

True iff `t` is an instance of one of the registered "tensor-like" types.

"""

return isinstance(t, _TENSOR_LIKE_TYPES)

def register_dense_tensor_like_type(tensor_type):

"""EXPERIMENTAL: Registers `tensor_type` as implementing the tensor interface.

A "tensor-like type" can represent a single dense tensor, and implements

the `name` and `dtype` properties.

Args:

tensor_type: A type implementing the tensor interface.

Raises:

TypeError: If `tensor_type` does not implement the tensor interface.

"""

try:

if not isinstance(tensor_type.name, property):

raise TypeError("Type %s does not define a `name` property")

except AttributeError:

raise TypeError("Type %s does not define a `name` property")

try:

if not isinstance(tensor_type.dtype, property):

raise TypeError("Type %s does not define a `dtype` property")

except AttributeError:

raise TypeError("Type %s does not define a `dtype` property")

# We expect this list to be small, so choose quadratic complexity

# for registration, so that we have a tuple that can be used for

# more efficient `isinstance` checks later.

global _TENSOR_LIKE_TYPES

_TENSOR_LIKE_TYPES = tuple(list(_TENSOR_LIKE_TYPES) + [tensor_type])

class Tensor(object):

"""Represents one of the outputs of an `Operation`.

*Note:* the `Tensor` class will be replaced by `Output` in the future.

Currently these two are aliases for each other.

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):

_override_helper(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 __bool__(self):

"""Dummy method to prevent a tensor from being used as a Python `bool`.

This overload raises a `TypeError` when the user inadvertently

treats a `Tensor` as a boolean (e.g. in an `if` statement). For

example:

```python

if tf.constant(True): # Will raise.

# ...

if tf.constant(5) < tf.constant(7): # Will raise.

# ...

```

Raises:

`TypeError`.

"""

raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "

"Use `if t is not None:` instead of `if t:` to test if a "

"tensor is defined, and use the logical TensorFlow ops "

"to test the value of a tensor.")

def __nonzero__(self):

"""Dummy method to prevent a tensor from being used as a Python `bool`.

This is the Python 2.x counterpart to `__bool__()` above.

Raises:

`TypeError`.

"""

raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "

"Use `if t is not None:` instead of `if t:` to test if a "

"tensor is defined, and use the logical TensorFlow ops "

"to test the value of a tensor.")

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)

# TODO(josh11b): Switch everyone from "Tensor" to "Output" to match C++ API.

Output = Tensor

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)]}

register_dense_tensor_like_type(Tensor)

def convert_to_tensor(value,

dtype=None,

name=None,

as_ref=False,

preferred_dtype=None):

"""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

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. Only used if a new

`Tensor` is created.

preferred_dtype: Optional element type for the returned tensor,

used when dtype is None. In some cases, a caller may not have a

dtype in mind when converting to a tensor, so preferred_dtype

can be used as a soft preference. If the conversion to

`preferred_dtype` is not possible, this argument has no effect.

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):

# If dtype is None but preferred_dtype is not None, we try to

# cast to preferred_dtype first.

ret = None

if dtype is None and preferred_dtype is not None:

try:

ret = conversion_func(

value, dtype=preferred_dtype, name=name, as_ref=as_ref)

except (TypeError, ValueError):

# Could not coerce the conversion to use the preferred dtype.

ret = None

if ret is not None and ret is not NotImplemented:

if (ret.dtype.base_dtype !=

dtypes.as_dtype(preferred_dtype).base_dtype):

raise TypeError("convert_to_tensor did not convert to "

"the preferred dtype: %s vs %s " %

(ret.dtype.base_dtype,

dtypes.as_dtype(preferred_dtype).base_dtype))

if ret is None:

ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)

if ret is NotImplemented:

continue

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,

preferred_dtype=None):

"""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.

preferred_dtype: Optional element type for the returned tensors,

used when dtype is None. In some cases, a caller may not have a

dtype in mind when converting to a tensor, so preferred_dtype

can be used as a soft preference. If the conversion to

`preferred_dtype` is not possible, this argument has no effect.

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,

preferred_dtype=preferred_dtype))

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` or `SparseTensor` it is returned

unmodified. Otherwise, it is converted to a `Tensor` using

`convert_to_tensor()`.

Args:

value: An `IndexedSlices`, `SparseTensor`, 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`, `IndexedSlices`, or `SparseTensor` based on `value`.

Raises:

ValueError: If `dtype` does not match the element type of `value`.

"""

if isinstance(value, (IndexedSlices, SparseTensor)):

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.

Any `IndexedSlices` or `SparseTensor` objects in `values` are returned

unmodified.

Args:

values: A list of `None`, `IndexedSlices`, `SparseTensor`, 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`, `IndexedSlices`, and/or `SparseTensor` 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.

The conversion function may return `NotImplemented` for some

inputs. In this case, the conversion process will continue to try

subsequent conversion functions.

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 is not None else "")

def __neg__(self):

return IndexedSlices(-self.values, self.indices, self.dense_shape)

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(indices, values, 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 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 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.

* `shape`: A 1-D int64 tensor of shape `[ndims]`, which specifies the shape

of the sparse tensor. Takes a list indicating the number of elements in

each dimension. For example, `shape=[3,6]` specifies a two-dimensional 3x6

tensor, `shape=[2,3,4]` specifies a three-dimensional 2x3x4 tensor, and

`shape=[9]` specifies a one-dimensional tensor with 9 elements.

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(indices=[[0, 0], [1, 2]], values=[1, 2], shape=[3, 4])

```

represents the dense tensor

```python