class tf.RegisterGradient
A decorator for registering the gradient function for an op type.
This decorator is only used when defining a new op type. For an op
with m
inputs and n
outputs, the gradient function is a function
that takes the original Operation
and n
Tensor
objects
(representing the gradients with respect to each output of the op),
and returns m
Tensor
objects (representing the partial gradients
with respect to each input of the op).
For example, assuming that operations of type "Sub"
take two
inputs x
and y
, and return a single output x  y
, the
following gradient function would be registered:
@tf.RegisterGradient("Sub")
def _sub_grad(unused_op, grad):
return grad, tf.neg(grad)
The decorator argument op_type
is the string type of an
operation. This corresponds to the OpDef.name
field for the proto
that defines the operation.
tf.RegisterGradient.__init__(op_type)
{:#RegisterGradient.init}
Creates a new decorator with op_type
as the Operation type.
Args:
op_type
: The string type of an operation. This corresponds to theOpDef.name
field for the proto that defines the operation.
tf.NoGradient(op_type)
Specifies that ops of type op_type
do not have a defined gradient.
This function is only used when defining a new op type. It may be
used for ops such as tf.size()
that are not differentiable. For
example:
tf.NoGradient("Size")
Args:
op_type
: The string type of an operation. This corresponds to theOpDef.name
field for the proto that defines the operation.
Raises:
TypeError
: Ifop_type
is not a string.
class tf.RegisterShape
A decorator for registering the shape function for an op type.
This decorator is only used when defining a new op type. A shape
function is a function from an Operation
object to a list of
TensorShape
objects, with one TensorShape
for each output of the
operation.
For example, assuming that operations of type "Sub"
take two
inputs x
and y
, and return a single output x  y
, all with the
same shape, the following shape function would be registered:
@tf.RegisterShape("Sub")
def _sub_shape(op):
return [op.inputs[0].get_shape().merge_with(op.inputs[1].get_shape())]
The decorator argument op_type
is the string type of an
operation. This corresponds to the OpDef.name
field for the proto
that defines the operation.
tf.RegisterShape.__init__(op_type)
{:#RegisterShape.init}
Saves the op_type
as the Operation
type.
class tf.TensorShape
Represents the shape of a Tensor
.
A TensorShape
represents a possiblypartial shape specification for a
Tensor
. It may be one of the following:
 Fullyknown shape: has a known number of dimensions and a known size for each dimension.
 Partiallyknown shape: has a known number of dimensions, and an unknown size for one or more dimension.
 Unknown shape: has an unknown number of dimensions, and an unknown size in all dimensions.
If a tensor is produced by an operation of type "Foo"
, its shape
may be inferred if there is a registered shape function for
"Foo"
. See tf.RegisterShape()
for details of shape
functions and how to register them. Alternatively, the shape may be set
explicitly using Tensor.set_shape()
.
tf.TensorShape.merge_with(other)
Returns a TensorShape
combining the information in self
and other
.
The dimensions in self
and other
are merged elementwise,
according to the rules defined for Dimension.merge_with()
.
Args:
other
: AnotherTensorShape
.
Returns:
A TensorShape
containing the combined information of self
and
other
.
Raises:
ValueError
: Ifself
andother
are not compatible.
tf.TensorShape.concatenate(other)
Returns the concatenation of the dimension in self
and other
.
N.B. If either self
or other
is completely unknown,
concatenation will discard information about the other shape. In
future, we might support concatenation that preserves this
information for use with slicing.
Args:
other
: AnotherTensorShape
.
Returns:
A TensorShape
whose dimensions are the concatenation of the
dimensions in self
and other
.
tf.TensorShape.ndims
Returns the rank of this shape, or None if it is unspecified.
tf.TensorShape.dims
Returns a list of Dimensions, or None if the shape is unspecified.
tf.TensorShape.as_list()
Returns a list of integers or None for each dimension.
Returns:
A list of integers or None for each dimension.
tf.TensorShape.as_proto()
Returns this shape as a TensorShapeProto
.
tf.TensorShape.is_compatible_with(other)
Returns True iff self
is compatible with other
.
Two possiblypartiallydefined shapes are compatible if there exists a fullydefined shape that both shapes can represent. Thus, compatibility allows the shape inference code to reason about partiallydefined shapes. For example:

TensorShape(None) is compatible with all shapes.

TensorShape([None, None]) is compatible with all twodimensional shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is not compatible with, for example, TensorShape([None]) or TensorShape([None, None, None]).

TensorShape([32, None]) is compatible with all twodimensional shapes with size 32 in the 0th dimension, and also TensorShape([None, None]) and TensorShape(None). It is not compatible with, for example, TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]).

TensorShape([32, 784]) is compatible with itself, and also TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None, None]) and TensorShape(None). It is not compatible with, for example, TensorShape([32, 1, 784]) or TensorShape([None]).
The compatibility relation is reflexive and symmetric, but not transitive. For example, TensorShape([32, 784]) is compatible with TensorShape(None), and TensorShape(None) is compatible with TensorShape([4, 4]), but TensorShape([32, 784]) is not compatible with TensorShape([4, 4]).
Args:
other
: Another TensorShape.
Returns:
True iff self
is compatible with other
.
tf.TensorShape.is_fully_defined()
Returns True iff self
is fully defined in every dimension.
tf.TensorShape.with_rank(rank)
Returns a shape based on self
with the given rank.
This method promotes a completely unknown shape to one with a known rank.
Args:
rank
: An integer.
Returns:
A shape that is at least as specific as self
with the given rank.
Raises:
ValueError
: Ifself
does not represent a shape with the givenrank
.
tf.TensorShape.with_rank_at_least(rank)
Returns a shape based on self
with at least the given rank.
Args:
rank
: An integer.
Returns:
A shape that is at least as specific as self
with at least the given
rank.
Raises:
ValueError
: Ifself
does not represent a shape with at least the givenrank
.
tf.TensorShape.with_rank_at_most(rank)
Returns a shape based on self
with at most the given rank.
Args:
rank
: An integer.
Returns:
A shape that is at least as specific as self
with at most the given
rank.
Raises:
ValueError
: Ifself
does not represent a shape with at most the givenrank
.
tf.TensorShape.assert_has_rank(rank)
Raises an exception if self
is not compatible with the given rank
.
Args:
rank
: An integer.
Raises:
ValueError
: Ifself
does not represent a shape with the givenrank
.
tf.TensorShape.assert_same_rank(other)
Raises an exception if self
and other
do not have compatible ranks.
Args:
other
: AnotherTensorShape
.
Raises:
ValueError
: Ifself
andother
do not represent shapes with the same rank.
tf.TensorShape.assert_is_compatible_with(other)
Raises exception if self
and other
do not represent the same shape.
This method can be used to assert that there exists a shape that both
self
and other
represent.
Args:
other
: Another TensorShape.
Raises:
ValueError
: Ifself
andother
do not represent the same shape.
tf.TensorShape.assert_is_fully_defined()
Raises an exception if self
is not fully defined in every dimension.
Raises:
ValueError
: Ifself
does not have a known value for every dimension.
Other Methods
tf.TensorShape.__init__(dims)
{:#TensorShape.init}
Creates a new TensorShape with the given dimensions.
Args:
dims
: A list of Dimensions, or None if the shape is unspecified.DEPRECATED
: A single integer is treated as a singleton list.
Raises:
TypeError
: If dims cannot be converted to a list of dimensions.
tf.TensorShape.num_elements()
Returns the total number of elements, or none for incomplete shapes.
class tf.Dimension
Represents the value of one dimension in a TensorShape.
tf.Dimension.__init__(value)
{:#Dimension.init}
Creates a new Dimension with the given value.
tf.Dimension.assert_is_compatible_with(other)
Raises an exception if other
is not compatible with this Dimension.
Args:
other
: Another Dimension.
Raises:
ValueError
: Ifself
andother
are not compatible (see is_compatible_with).
tf.Dimension.is_compatible_with(other)
Returns true if other
is compatible with this Dimension.
Two known Dimensions are compatible if they have the same value. An unknown Dimension is compatible with all other Dimensions.
Args:
other
: Another Dimension.
Returns:
True if this Dimension and other
are compatible.
tf.Dimension.merge_with(other)
Returns a Dimension that combines the information in self
and other
.
Dimensions are combined as follows:
Dimension(n) .merge_with(Dimension(n)) == Dimension(n)
Dimension(n) .merge_with(Dimension(None)) == Dimension(n)
Dimension(None).merge_with(Dimension(n)) == Dimension(n)
Dimension(None).merge_with(Dimension(None)) == Dimension(None)
Dimension(n) .merge_with(Dimension(m)) raises ValueError for n != m
Args:
other
: Another Dimension.
Returns:
A Dimension containing the combined information of self
and
other
.
Raises:
ValueError
: Ifself
andother
are not compatible (see is_compatible_with).
tf.Dimension.value
The value of this dimension, or None if it is unknown.
tf.op_scope(values, name, default_name=None)
Returns a context manager for use when defining a Python op.
This context manager validates that the given values
are from the
same graph, ensures that graph is the default graph, and pushes a
name scope.
For example, to define a new Python op called my_op
:
def my_op(a, b, c, name=None):
with tf.op_scope([a, b, c], name, "MyOp") as scope:
a = tf.convert_to_tensor(a, name="a")
b = tf.convert_to_tensor(b, name="b")
c = tf.convert_to_tensor(c, name="c")
# Define some computation that uses `a`, `b`, and `c`.
return foo_op(..., name=scope)
Args:
values
: The list ofTensor
arguments that are passed to the op function.name
: The name argument that is passed to the op function.default_name
: The default name to use if thename
argument isNone
.
Returns:
A context manager for use in defining Python ops. Yields the name scope.
Raises:
ValueError
: if neithername
nordefault_name
is provided.
tf.get_seed(op_seed)
Returns the local seeds an operation should use given an opspecific seed.
Given operationspecific seed, op_seed
, this helper function returns two
seeds derived from graphlevel and oplevel seeds. Many random operations
internally use the two seeds to allow user to change the seed globally for a
graph, or for only specific operations.
For details on how the graphlevel seed interacts with op seeds, see
set_random_seed
.
Args:
op_seed
: integer.
Returns:
A tuple of two integers that should be used for the local seed of this operation.