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.
Other Methods
tf.RegisterGradient.__call__(f)
{:#RegisterGradient.call}
Registers the function f
as gradient function for op_type
.
tf.NotDifferentiable(op_type)
Specifies that ops of type op_type
is not differentiable.
This function should not be used for operations that have a welldefined gradient that is not yet implemented.
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.NotDifferentiable("Size")
The gradient computed for 'op_type' will then propagate zeros.
For ops that have a welldefined gradient but are not yet implemented, no declaration should be made, and an error must be thrown if an attempt to request its gradient is made.
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.
tf.NoGradient(op_type)
Specifies that ops of type op_type
is not differentiable.
This function should not be used for operations that have a welldefined gradient that is not yet implemented.
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.NotDifferentiable("Size")
The gradient computed for 'op_type' will then propagate zeros.
For ops that have a welldefined gradient but are not yet implemented, no declaration should be made, and an error must be thrown if an attempt to request its gradient is made.
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.
Soon to be removed. Shape functions should be registered via the SetShapeFn on the original Op specification in C++.
tf.RegisterShape.__call__(f)
{:#RegisterShape.call}
Registers "f" as the shape function for "op_type".
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 Shape functions in
C++
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.
Raises:
ValueError
: Ifself
is an unknown shape with an unknown rank.
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.__bool__()
{:#TensorShape.bool}
Returns True if this shape contains nonzero information.
tf.TensorShape.__eq__(other)
{:#TensorShape.eq}
Returns True if self
is equivalent to other
.
tf.TensorShape.__getitem__(key)
{:#TensorShape.getitem}
Returns the value of a dimension or a shape, depending on the key.
Args:
key
: Ifkey
is an integer, returns the dimension at that index; otherwise ifkey
is a slice, returns a TensorShape whose dimensions are those selected by the slice fromself
.
Returns:
A dimension if key
is an integer, or a TensorShape
if key
is a
slice.
Raises:
ValueError
: Ifkey
is a slice, and any of its elements are negative, or ifself
is completely unknown and the step is set.
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.__iter__()
{:#TensorShape.iter}
Returns self.dims
if the rank is known, otherwise raises ValueError.
tf.TensorShape.__len__()
{:#TensorShape.len}
Returns the rank of this shape, or raises ValueError if unspecified.
tf.TensorShape.__ne__(other)
{:#TensorShape.ne}
Returns True if self
is known to be different from other
.
tf.TensorShape.__nonzero__()
{:#TensorShape.nonzero}
Returns True if this shape contains nonzero information.
tf.TensorShape.__repr__()
{:#TensorShape.repr}
tf.TensorShape.__str__()
{:#TensorShape.str}
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.__add__(other)
{:#Dimension.add}
Returns the sum of self
and other
.
Dimensions are summed as follows:
Dimension(m) + Dimension(n) == Dimension(m + n) Dimension(m) + Dimension(None) == Dimension(None) Dimension(None) + Dimension(n) == Dimension(None) Dimension(None) + Dimension(None) == Dimension(None)
Args:
other
: Another Dimension.
Returns:
A Dimension whose value is the sum of self
and other
.
tf.Dimension.__div__(other)
{:#Dimension.div}
DEPRECATED: Use __floordiv__
via x // y
instead.
This function exists only for backwards compatibility purposes; new code
should use __floordiv__
via the syntax x // y
. Using x // y
communicates clearly that the result rounds down, and is forward compatible
to Python 3.
Args:
other
: AnotherDimension
.
Returns:
A Dimension
whose value is the integer quotient of self
and other
.
tf.Dimension.__eq__(other)
{:#Dimension.eq}
Returns true if other
has the same known value as this Dimension.
tf.Dimension.__floordiv__(other)
{:#Dimension.floordiv}
Returns the quotient of self
and other
rounded down.
Dimensions are divided as follows:
Dimension(m) // Dimension(n) == Dimension(m // n) Dimension(m) // Dimension(None) == Dimension(None) Dimension(None) // Dimension(n) == Dimension(None) Dimension(None) // Dimension(None) == Dimension(None)
Args:
other
: AnotherDimension
.
Returns:
A Dimension
whose value is the integer quotient of self
and other
.
tf.Dimension.__ge__(other)
{:#Dimension.ge}
Returns True if self
is known to be greater than or equal to other
.
Dimensions are compared as follows:
Dimension(m) >= Dimension(n) == m >= n Dimension(m) >= Dimension(None) == None Dimension(None) >= Dimension(n) == None Dimension(None) >= Dimension(None) == None
Args:
other
: Another Dimension.
Returns:
The value of self.value >= other.value
if both are known, otherwise
None.
tf.Dimension.__gt__(other)
{:#Dimension.gt}
Returns True if self
is known to be greater than other
.
Dimensions are compared as follows:
Dimension(m) > Dimension(n) == m > n Dimension(m) > Dimension(None) == None Dimension(None) > Dimension(n) == None Dimension(None) > Dimension(None) == None
Args:
other
: Another Dimension.
Returns:
The value of self.value > other.value
if both are known, otherwise
None.
tf.Dimension.__index__()
{:#Dimension.index}
tf.Dimension.__init__(value)
{:#Dimension.init}
Creates a new Dimension with the given value.
tf.Dimension.__int__()
{:#Dimension.int}
tf.Dimension.__le__(other)
{:#Dimension.le}
Returns True if self
is known to be less than or equal to other
.
Dimensions are compared as follows:
Dimension(m) <= Dimension(n) == m <= n Dimension(m) <= Dimension(None) == None Dimension(None) <= Dimension(n) == None Dimension(None) <= Dimension(None) == None
Args:
other
: Another Dimension.
Returns:
The value of self.value <= other.value
if both are known, otherwise
None.
tf.Dimension.__lt__(other)
{:#Dimension.lt}
Returns True if self
is known to be less than other
.
Dimensions are compared as follows:
Dimension(m) < Dimension(n) == m < n Dimension(m) < Dimension(None) == None Dimension(None) < Dimension(n) == None Dimension(None) < Dimension(None) == None
Args:
other
: Another Dimension.
Returns:
The value of self.value < other.value
if both are known, otherwise
None.
tf.Dimension.__mod__(other)
{:#Dimension.mod}
Returns self
modulo `other.
Dimension moduli are computed as follows:
Dimension(m) % Dimension(n) == Dimension(m % n) Dimension(m) % Dimension(None) == Dimension(None) Dimension(None) % Dimension(n) == Dimension(None) Dimension(None) % Dimension(None) == Dimension(None)
Args:
other
: Another Dimension.
Returns:
A Dimension whose value is self
modulo other
.
tf.Dimension.__mul__(other)
{:#Dimension.mul}
Returns the product of self
and other
.
Dimensions are summed as follows:
Dimension(m) * Dimension(n) == Dimension(m * n)
Dimension(m) * Dimension(None) == Dimension(None)
Dimension(None) * Dimension(n) == Dimension(None)
Dimension(None) * Dimension(None) == Dimension(None)
Args:
other
: Another Dimension.
Returns:
A Dimension whose value is the product of self
and other
.
tf.Dimension.__ne__(other)
{:#Dimension.ne}
Returns true if other
has a different known value from self
.
tf.Dimension.__repr__()
{:#Dimension.repr}
tf.Dimension.__str__()
{:#Dimension.str}
tf.Dimension.__sub__(other)
{:#Dimension.sub}
Returns the subtraction of other
from self
.
Dimensions are subtracted as follows:
Dimension(m)  Dimension(n) == Dimension(m  n) Dimension(m)  Dimension(None) == Dimension(None) Dimension(None)  Dimension(n) == Dimension(None) Dimension(None)  Dimension(None) == Dimension(None)
Args:
other
: Another Dimension.
Returns:
A Dimension whose value is the subtraction of sum of other
from self
.
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)
DEPRECATED. Same as name_scope above, just different argument order.
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.