View source on GitHub 
See the variable guide.
tf.Variable(
initial_value=None,
trainable=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None,
import_scope=None,
constraint=None,
synchronization=tf.VariableSynchronization.AUTO
,
aggregation=tf.compat.v1.VariableAggregation.NONE
,
shape=None,
experimental_enable_variable_lifting=True
)
A variable maintains shared, persistent state manipulated by a program.
The Variable()
constructor requires an initial value for the variable, which
can be a Tensor
of any type and shape. This initial value defines the type
and shape of the variable. After construction, the type and shape of the
variable are fixed. The value can be changed using one of the assign methods.
v = tf.Variable(1.)
v.assign(2.)
<tf.Variable ... shape=() dtype=float32, numpy=2.0>
v.assign_add(0.5)
<tf.Variable ... shape=() dtype=float32, numpy=2.5>
The shape
argument to Variable
's constructor allows you to construct a
variable with a less defined shape than its initial_value
:
v = tf.Variable(1., shape=tf.TensorShape(None))
v.assign([[1.]])
<tf.Variable ... shape=<unknown> dtype=float32, numpy=array([[1.]], ...)>
Just like any Tensor
, variables created with Variable()
can be used as
inputs to operations. Additionally, all the operators overloaded for the
Tensor
class are carried over to variables.
w = tf.Variable([[1.], [2.]])
x = tf.constant([[3., 4.]])
tf.matmul(w, x)
<tf.Tensor:... shape=(2, 2), ... numpy=
array([[3., 4.],
[6., 8.]], dtype=float32)>
tf.sigmoid(w + x)
<tf.Tensor:... shape=(2, 2), ...>
When building a machine learning model it is often convenient to distinguish
between variables holding trainable model parameters and other variables such
as a step
variable used to count training steps. To make this easier, the
variable constructor supports a trainable=<bool>
parameter. tf.GradientTape
watches trainable variables by default:
with tf.GradientTape(persistent=True) as tape:
trainable = tf.Variable(1.)
non_trainable = tf.Variable(2., trainable=False)
x1 = trainable * 2.
x2 = non_trainable * 3.
tape.gradient(x1, trainable)
<tf.Tensor:... shape=(), dtype=float32, numpy=2.0>
assert tape.gradient(x2, non_trainable) is None # Unwatched
Variables are automatically tracked when assigned to attributes of types
inheriting from tf.Module
.
m = tf.Module()
m.v = tf.Variable([1.])
m.trainable_variables
(<tf.Variable ... shape=(1,) ... numpy=array([1.], dtype=float32)>,)
This tracking then allows saving variable values to training checkpoints, or to SavedModels which include serialized TensorFlow graphs.
Variables are often captured and manipulated by tf.function
s. This works the
same way the undecorated function would have:
v = tf.Variable(0.)
read_and_decrement = tf.function(lambda: v.assign_sub(0.1))
read_and_decrement()
<tf.Tensor: shape=(), dtype=float32, numpy=0.1>
read_and_decrement()
<tf.Tensor: shape=(), dtype=float32, numpy=0.2>
Variables created inside a tf.function
must be owned outside the function
and be created only once:
class M(tf.Module):
@tf.function
def __call__(self, x):
if not hasattr(self, "v"): # Or set self.v to None in __init__
self.v = tf.Variable(x)
return self.v * x
m = M()
m(2.)
<tf.Tensor: shape=(), dtype=float32, numpy=4.0>
m(3.)
<tf.Tensor: shape=(), dtype=float32, numpy=6.0>
m.v
<tf.Variable ... shape=() dtype=float32, numpy=2.0>
See the tf.function
documentation for details.
Args  

initial_value

A Tensor , or Python object convertible to a Tensor ,
which is the initial value for the Variable. The initial value must have
a shape specified unless validate_shape is set to False. Can also be a
callable with no argument that returns the initial value when called. In
that case, dtype must be specified. (Note that initializer functions
from init_ops.py must first be bound to a shape before being used here.)

trainable

If True , GradientTapes automatically watch uses of this
variable. Defaults to True , unless synchronization is set to
ON_READ , in which case it defaults to False .

validate_shape

If False , allows the variable to be initialized with a
value of unknown shape. If True , the default, the shape of
initial_value must be known.

caching_device

Note: This argument is only valid when using a v1style
Session . Optional device string describing where the Variable should
be cached for reading. Defaults to the Variable's device. If not None ,
caches on another device. Typical use is to cache on the device where
the Ops using the Variable reside, to deduplicate copying through
Switch and other conditional statements.

name

Optional name for the variable. Defaults to 'Variable' and gets
uniquified automatically.

variable_def

VariableDef protocol buffer. If not None , recreates the
Variable object with its contents, referencing the variable's nodes in
the graph, which must already exist. The graph is not changed.
variable_def and the other arguments are mutually exclusive.

dtype

If set, initial_value will be converted to the given type. If
None , either the datatype will be kept (if initial_value is a
Tensor), or convert_to_tensor will decide.

import_scope

Optional string . Name scope to add to the Variable. Only
used when initializing from protocol buffer.

constraint

An optional projection function to be applied to the variable
after being updated by an Optimizer (e.g. used to implement norm
constraints or value constraints for layer weights). The function must
take as input the unprojected Tensor representing the value of the
variable and return the Tensor for the projected value (which must have
the same shape). Constraints are not safe to use when doing asynchronous
distributed training.

synchronization

Indicates when a distributed a variable will be
aggregated. Accepted values are constants defined in the class
tf.VariableSynchronization . By default the synchronization is set to
AUTO and the current DistributionStrategy chooses when to
synchronize.

aggregation

Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
tf.VariableAggregation .

shape

(optional) The shape of this variable. If None, the shape of
initial_value will be used. When setting this argument to
tf.TensorShape(None) (representing an unspecified shape), the variable
can be assigned with values of different shapes.

experimental_enable_variable_lifting

Whether to lift the variable out if
it's in a tf.function . Default is True . When this argument
is True , variable creation will follow the behavior and
restrictions described
here.
If this argument is False , that description doesn't apply,
and you can freely create and use the variable in the
tf.function , as if it's a "mutable tf.Tensor ". You can't
return the variable though.

Raises  

ValueError

If both variable_def and initial_value are specified.

ValueError

If the initial value is not specified, or does not have a
shape and validate_shape is True .

Child Classes
Methods
assign
assign(
value, use_locking=False, name=None, read_value=True
)
Assigns a new value to the variable.
This is essentially a shortcut for assign(self, value)
.
Args  

value

A Tensor . The new value for this variable.

use_locking

If True , use locking during the assignment.

name

The name of the operation to be created 
read_value

if True, will return something which evaluates to the new value of the variable; if False will return the assign op. 
Returns  

The updated variable. If read_value is false, instead returns None in
Eager mode and the assign op in graph mode.

assign_add
assign_add(
delta, use_locking=False, name=None, read_value=True
)
Adds a value to this variable.
This is essentially a shortcut for assign_add(self, delta)
.
Args  

delta

A Tensor . The value to add to this variable.

use_locking

If True , use locking during the operation.

name

The name of the operation to be created 
read_value

if True, will return something which evaluates to the new value of the variable; if False will return the assign op. 
Returns  

The updated variable. If read_value is false, instead returns None in
Eager mode and the assign op in graph mode.

assign_sub
assign_sub(
delta, use_locking=False, name=None, read_value=True
)
Subtracts a value from this variable.
This is essentially a shortcut for assign_sub(self, delta)
.
Args  

delta

A Tensor . The value to subtract from this variable.

use_locking

If True , use locking during the operation.

name

The name of the operation to be created 
read_value

if True, will return something which evaluates to the new value of the variable; if False will return the assign op. 
Returns  

The updated variable. If read_value is false, instead returns None in
Eager mode and the assign op in graph mode.

batch_scatter_update
batch_scatter_update(
sparse_delta, use_locking=False, name=None
)
Assigns tf.IndexedSlices
to this variable batchwise.
Analogous to batch_gather
. This assumes that this variable and the
sparse_delta IndexedSlices have a series of leading dimensions that are the
same for all of them, and the updates are performed on the last dimension of
indices. In other words, the dimensions should be the following:
num_prefix_dims = sparse_delta.indices.ndims  1
batch_dim = num_prefix_dims + 1
sparse_delta.updates.shape = sparse_delta.indices.shape + var.shape[
batch_dim:]
where
sparse_delta.updates.shape[:num_prefix_dims]
== sparse_delta.indices.shape[:num_prefix_dims]
== var.shape[:num_prefix_dims]
And the operation performed can be expressed as:
var[i_1, ..., i_n,
sparse_delta.indices[i_1, ..., i_n, j]] = sparse_delta.updates[
i_1, ..., i_n, j]
When sparse_delta.indices is a 1D tensor, this operation is equivalent to
scatter_update
.
To avoid this operation one can looping over the first ndims
of the
variable and using scatter_update
on the subtensors that result of slicing
the first dimension. This is a valid option for ndims = 1
, but less
efficient than this implementation.
Args  

sparse_delta

tf.IndexedSlices to be assigned to this variable.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

count_up_to
count_up_to(
limit
)
Increments this variable until it reaches limit
. (deprecated)
When that Op is run it tries to increment the variable by 1
. If
incrementing the variable would bring it above limit
then the Op raises
the exception OutOfRangeError
.
If no error is raised, the Op outputs the value of the variable before the increment.
This is essentially a shortcut for count_up_to(self, limit)
.
Args  

limit

value at which incrementing the variable raises an error. 
Returns  

A Tensor that will hold the variable value before the increment. If no
other Op modifies this variable, the values produced will all be
distinct.

eval
eval(
session=None
)
In a session, computes and returns the value of this variable.
This is not a graph construction method, it does not add ops to the graph.
This convenience method requires a session where the graph
containing this variable has been launched. If no session is
passed, the default session is used. See tf.compat.v1.Session
for more
information on launching a graph and on sessions.
v = tf.Variable([1, 2])
init = tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as sess:
sess.run(init)
# Usage passing the session explicitly.
print(v.eval(sess))
# Usage with the default session. The 'with' block
# above makes 'sess' the default session.
print(v.eval())
Args  

session

The session to use to evaluate this variable. If none, the default session is used. 
Returns  

A numpy ndarray with a copy of the value of this variable.

experimental_ref
experimental_ref()
DEPRECATED FUNCTION
from_proto
@staticmethod
from_proto( variable_def, import_scope=None )
Returns a Variable
object created from variable_def
.
gather_nd
gather_nd(
indices, name=None
)
Gather slices from params
into a Tensor with shape specified by indices
.
See tf.gather_nd for details.
Args  

indices

A Tensor . Must be one of the following types: int32 , int64 .
Index tensor.

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as params .

get_shape
get_shape() > tf.TensorShape
Alias of Variable.shape
.
initialized_value
initialized_value()
Returns the value of the initialized variable. (deprecated)
You should use this instead of the variable itself to initialize another variable with a value that depends on the value of this variable.
# Initialize 'v' with a random tensor.
v = tf.Variable(tf.random.truncated_normal([10, 40]))
# Use `initialized_value` to guarantee that `v` has been
# initialized before its value is used to initialize `w`.
# The random values are picked only once.
w = tf.Variable(v.initialized_value() * 2.0)
Returns  

A Tensor holding the value of this variable after its initializer
has run.

load
load(
value, session=None
)
Load new value into this variable. (deprecated)
Writes new value to variable's memory. Doesn't add ops to the graph.
This convenience method requires a session where the graph
containing this variable has been launched. If no session is
passed, the default session is used. See tf.compat.v1.Session
for more
information on launching a graph and on sessions.
v = tf.Variable([1, 2])
init = tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as sess:
sess.run(init)
# Usage passing the session explicitly.
v.load([2, 3], sess)
print(v.eval(sess)) # prints [2 3]
# Usage with the default session. The 'with' block
# above makes 'sess' the default session.
v.load([3, 4], sess)
print(v.eval()) # prints [3 4]
Args  

value

New variable value 
session

The session to use to evaluate this variable. If none, the default session is used. 
Raises  

ValueError

Session is not passed and no default session 
read_value
read_value()
Returns the value of this variable, read in the current context.
Can be different from value() if it's on another device, with control dependencies, etc.
Returns  

A Tensor containing the value of the variable.

ref
ref()
Returns a hashable reference object to this Variable.
The primary use case for this API is to put variables in a set/dictionary.
We can't put variables in a set/dictionary as variable.__hash__()
is no
longer available starting Tensorflow 2.0.
The following will raise an exception starting 2.0
x = tf.Variable(5)
y = tf.Variable(10)
z = tf.Variable(10)
variable_set = {x, y, z}
Traceback (most recent call last):
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
variable_dict = {x: 'five', y: 'ten'}
Traceback (most recent call last):
TypeError: Variable is unhashable. Instead, use tensor.ref() as the key.
Instead, we can use variable.ref()
.
variable_set = {x.ref(), y.ref(), z.ref()}
x.ref() in variable_set
True
variable_dict = {x.ref(): 'five', y.ref(): 'ten', z.ref(): 'ten'}
variable_dict[y.ref()]
'ten'
Also, the reference object provides .deref()
function that returns the
original Variable.
x = tf.Variable(5)
x.ref().deref()
<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
scatter_add
scatter_add(
sparse_delta, use_locking=False, name=None
)
Adds tf.IndexedSlices
to this variable.
Args  

sparse_delta

tf.IndexedSlices to be added to this variable.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

scatter_div
scatter_div(
sparse_delta, use_locking=False, name=None
)
Divide this variable by tf.IndexedSlices
.
Args  

sparse_delta

tf.IndexedSlices to divide this variable by.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

scatter_max
scatter_max(
sparse_delta, use_locking=False, name=None
)
Updates this variable with the max of tf.IndexedSlices
and itself.
Args  

sparse_delta

tf.IndexedSlices to use as an argument of max with this
variable.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

scatter_min
scatter_min(
sparse_delta, use_locking=False, name=None
)
Updates this variable with the min of tf.IndexedSlices
and itself.
Args  

sparse_delta

tf.IndexedSlices to use as an argument of min with this
variable.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

scatter_mul
scatter_mul(
sparse_delta, use_locking=False, name=None
)
Multiply this variable by tf.IndexedSlices
.
Args  

sparse_delta

tf.IndexedSlices to multiply this variable by.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

scatter_nd_add
scatter_nd_add(
indices, updates, name=None
)
Applies sparse addition to individual values or slices in a Variable.
The Variable has rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into self.
It must be shape [d_0, ..., d_{Q2}, K]
where 0 < K <= P
.
The innermost dimension of indices
(with length K
) corresponds to
indices into elements (if K = P
) or slices (if K < P
) along the K
th
dimension of self.
updates
is Tensor
of rank Q1+PK
with shape:
[d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]].
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
v.scatter_nd_add(indices, updates)
print(v)
The resulting update to v would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See tf.scatter_nd
for more details about how to make updates to
slices.
Args  

indices

The indices to be used in the operation. 
updates

The values to be used in the operation. 
name

the name of the operation. 
Returns  

The updated variable. 
scatter_nd_sub
scatter_nd_sub(
indices, updates, name=None
)
Applies sparse subtraction to individual values or slices in a Variable.
Assuming the variable has rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into self.
It must be shape [d_0, ..., d_{Q2}, K]
where 0 < K <= P
.
The innermost dimension of indices
(with length K
) corresponds to
indices into elements (if K = P
) or slices (if K < P
) along the K
th
dimension of self.
updates
is Tensor
of rank Q1+PK
with shape:
[d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]].
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
v.scatter_nd_sub(indices, updates)
print(v)
After the update v
would look like this:
[1, 9, 3, 6, 4, 6, 7, 4]
See tf.scatter_nd
for more details about how to make updates to
slices.
Args  

indices

The indices to be used in the operation. 
updates

The values to be used in the operation. 
name

the name of the operation. 
Returns  

The updated variable. 
scatter_nd_update
scatter_nd_update(
indices, updates, name=None
)
Applies sparse assignment to individual values or slices in a Variable.
The Variable has rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into self.
It must be shape [d_0, ..., d_{Q2}, K]
where 0 < K <= P
.
The innermost dimension of indices
(with length K
) corresponds to
indices into elements (if K = P
) or slices (if K < P
) along the K
th
dimension of self.
updates
is Tensor
of rank Q1+PK
with shape:
[d_0, ..., d_{Q2}, self.shape[K], ..., self.shape[P1]].
For example, say we want to add 4 scattered elements to a rank1 tensor to 8 elements. In Python, that update would look like this:
v = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
v.scatter_nd_update(indices, updates)
print(v)
The resulting update to v would look like this:
[1, 11, 3, 10, 9, 6, 7, 12]
See tf.scatter_nd
for more details about how to make updates to
slices.
Args  

indices

The indices to be used in the operation. 
updates

The values to be used in the operation. 
name

the name of the operation. 
Returns  

The updated variable. 
scatter_sub
scatter_sub(
sparse_delta, use_locking=False, name=None
)
Subtracts tf.IndexedSlices
from this variable.
Args  

sparse_delta

tf.IndexedSlices to be subtracted from this variable.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

scatter_update
scatter_update(
sparse_delta, use_locking=False, name=None
)
Assigns tf.IndexedSlices
to this variable.
Args  

sparse_delta

tf.IndexedSlices to be assigned to this variable.

use_locking

If True , use locking during the operation.

name

the name of the operation. 
Returns  

The updated variable. 
Raises  

TypeError

if sparse_delta is not an IndexedSlices .

set_shape
set_shape(
shape
)
Overrides the shape for this variable.
Args  

shape

the TensorShape representing the overridden shape.

sparse_read
sparse_read(
indices, name=None
)
Gather slices from params axis axis according to indices.
This function supports a subset of tf.gather, see tf.gather for details on usage.
Args  

indices

The index Tensor . Must be one of the following types: int32 ,
int64 . Must be in range [0, params.shape[axis]) .

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as params .

to_proto
to_proto(
export_scope=None
)
Converts a Variable
to a VariableDef
protocol buffer.
Args  

export_scope

Optional string . Name scope to remove.

Returns  

A VariableDef protocol buffer, or None if the Variable is not
in the specified name scope.

value
value()
Returns the last snapshot of this variable.
You usually do not need to call this method as all ops that need the value
of the variable call it automatically through a convert_to_tensor()
call.
Returns a Tensor
which holds the value of the variable. You can not
assign a new value to this tensor as it is not a reference to the variable.
To avoid copies, if the consumer of the returned value is on the same device as the variable, this actually returns the live value of the variable, not a copy. Updates to the variable are seen by the consumer. If the consumer is on a different device it will get a copy of the variable.
Returns  

A Tensor containing the value of the variable.

__abs__
__abs__(
name=None
)
Computes the absolute value of a tensor.
Given a tensor of integer or floatingpoint values, this operation returns a tensor of the same type, where each element contains the absolute value of the corresponding element in the input.
Given a tensor x
of complex numbers, this operation returns a tensor of type
float32
or float64
that is the absolute value of each element in x
. For
a complex number \(a + bj\), its absolute value is computed as
\(\sqrt{a^2 + b^2}\).
For example:
# real number
x = tf.constant([2.25, 3.25])
tf.abs(x)
<tf.Tensor: shape=(2,), dtype=float32,
numpy=array([2.25, 3.25], dtype=float32)>
# complex number
x = tf.constant([[2.25 + 4.75j], [3.25 + 5.75j]])
tf.abs(x)
<tf.Tensor: shape=(2, 1), dtype=float64, numpy=
array([[5.25594901],
[6.60492241]])>
Args  

x

A Tensor or SparseTensor of type float16 , float32 , float64 ,
int32 , int64 , complex64 or complex128 .

name

A name for the operation (optional). 
Returns  

A Tensor or SparseTensor of the same size, type and sparsity as x ,
with absolute values. Note, for complex64 or complex128 input, the
returned Tensor will be of type float32 or float64 , respectively.

__add__
__add__(
y
)
The operation invoked by the Tensor.add
operator.
Purpose in the API  

This method is exposed in TensorFlow's API so that library developers
can register dispatching for Tensor.add to allow it to handle
custom composite tensors & other custom objects.
The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation. 
Args  

x

The lefthand side of the + operator.

y

The righthand side of the + operator.

name

an optional name for the operation. 
Returns  

The result of the elementwise + operation.

__and__
__and__(
y
)
__div__
__div__(
y
)
Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)
This function divides x
and y
, forcing Python 2 semantics. That is, if x
and y
are both integers then the result will be an integer. This is in
contrast to Python 3, where division with /
is always a float while division
with //
is always an integer.
Args  

x

Tensor numerator of real numeric type.

y

Tensor denominator of real numeric type.

name

A name for the operation (optional). 
Returns  

x / y returns the quotient of x and y.

Migrate to TF2
This function is deprecated in TF2. Prefer using the Tensor division operator,
tf.divide
, or tf.math.divide
, which obey the Python 3 division operator
semantics.
__eq__
__eq__(
other
)
Compares two variables elementwise for equality.
__floordiv__
__floordiv__(
y
)
Divides x / y
elementwise, rounding toward the most negative integer.
Mathematically, this is equivalent to floor(x / y). For example: floor(8.4 / 4.0) = floor(2.1) = 2.0 floor(8.4 / 4.0) = floor(2.1) = 3.0 This is equivalent to the '//' operator in Python 3.0 and above.
Args  

x

Tensor numerator of real numeric type.

y

Tensor denominator of real numeric type.

name

A name for the operation (optional). 
Returns  

x / y rounded toward infinity.

Raises  

TypeError

If the inputs are complex. 
__ge__
__ge__(
y: Annotated[Any, tf.raw_ops.Any
],
name=None
) > Annotated[Any, tf.raw_ops.Any
]
Returns the truth value of (x >= y) elementwise.
Example:
x = tf.constant([5, 4, 6, 7])
y = tf.constant([5, 2, 5, 10])
tf.math.greater_equal(x, y) ==> [True, True, True, False]
x = tf.constant([5, 4, 6, 7])
y = tf.constant([5])
tf.math.greater_equal(x, y) ==> [True, False, True, True]
Args  

x

A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor of type bool .

__getitem__
__getitem__(
slice_spec
)
Creates a slice helper object given a variable.
This allows creating a subtensor from part of the current contents
of a variable. See tf.Tensor.getitem
for detailed examples
of slicing.
This function in addition also allows assignment to a sliced range.
This is similar to __setitem__
functionality in Python. However,
the syntax is different so that the user can capture the assignment
operation for grouping or passing to sess.run()
in TF1.
For example,
import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
print(A[:2, :2]) # => [[1,2], [4,5]]
A[:2,:2].assign(22. * tf.ones((2, 2))))
print(A) # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
Note that assignments currently do not support NumPy broadcasting semantics.
Args  

var

An ops.Variable object.

slice_spec

The arguments to Tensor.getitem .

Returns  

The appropriate slice of "tensor", based on "slice_spec".
As an operator. The operator also has a assign() method
that can be used to generate an assignment operator.

Raises  

ValueError

If a slice range is negative size. 
TypeError

TypeError: If the slice indices aren't int, slice, ellipsis, tf.newaxis or int32/int64 tensors. 
__gt__
__gt__(
y: Annotated[Any, tf.raw_ops.Any
],
name=None
) > Annotated[Any, tf.raw_ops.Any
]
Returns the truth value of (x > y) elementwise.
Example:
x = tf.constant([5, 4, 6])
y = tf.constant([5, 2, 5])
tf.math.greater(x, y) ==> [False, True, True]
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.greater(x, y) ==> [False, False, True]
Args  

x

A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor of type bool .

__invert__
__invert__(
name=None
)
__iter__
__iter__()
When executing eagerly, iterates over the value of the variable.
__le__
__le__(
y: Annotated[Any, tf.raw_ops.Any
],
name=None
) > Annotated[Any, tf.raw_ops.Any
]
Returns the truth value of (x <= y) elementwise.
Example:
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less_equal(x, y) ==> [True, True, False]
x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 6])
tf.math.less_equal(x, y) ==> [True, True, True]
Args  

x

A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor of type bool .

__lt__
__lt__(
y: Annotated[Any, tf.raw_ops.Any
],
name=None
) > Annotated[Any, tf.raw_ops.Any
]
Returns the truth value of (x < y) elementwise.
Example:
x = tf.constant([5, 4, 6])
y = tf.constant([5])
tf.math.less(x, y) ==> [False, True, False]
x = tf.constant([5, 4, 6])
y = tf.constant([5, 6, 7])
tf.math.less(x, y) ==> [False, True, True]
Args  

x

A Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , int64 , bfloat16 , uint16 , half , uint32 , uint64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor of type bool .

__matmul__
__matmul__(
y
)
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication dimensions, and any further outer dimensions specify matching batch size.
Both matrices must be of the same type. The supported types are:
bfloat16
, float16
, float32
, float64
, int32
, int64
,
complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
a_is_sparse
or b_is_sparse
flag to True
. These are False
by default.
This optimization is only available for plain matrices (rank2 tensors) with
datatypes bfloat16
or float32
.
A simple 2D tensor matrix multiplication:
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
a # 2D tensor
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
b # 2D tensor
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[ 7, 8],
[ 9, 10],
[11, 12]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 58, 64],
[139, 154]], dtype=int32)>
A batch matrix multiplication with batch shape [2]:
a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
a # 3D tensor
<tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]], dtype=int32)>
b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
b # 3D tensor
<tf.Tensor: shape=(2, 3, 2), dtype=int32, numpy=
array([[[13, 14],
[15, 16],
[17, 18]],
[[19, 20],
[21, 22],
[23, 24]]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2, 2), dtype=int32, numpy=
array([[[ 94, 100],
[229, 244]],
[[508, 532],
[697, 730]]], dtype=int32)>
Since python >= 3.5 the @ operator is supported
(see PEP 465). In TensorFlow,
it simply calls the tf.matmul()
function, so the following lines are
equivalent:
d = a @ b @ [[10], [11]]
d = tf.matmul(tf.matmul(a, b), [[10], [11]])
Args  

a

tf.Tensor of type float16 , float32 , float64 , int32 ,
complex64 , complex128 and rank > 1.

b

tf.Tensor with same type and rank as a .

transpose_a

If True , a is transposed before multiplication.

transpose_b

If True , b is transposed before multiplication.

adjoint_a

If True , a is conjugated and transposed before
multiplication.

adjoint_b

If True , b is conjugated and transposed before
multiplication.

a_is_sparse

If True , a is treated as a sparse matrix. Notice, this
does not support tf.sparse.SparseTensor , it just makes optimizations
that assume most values in a are zero.
See tf.sparse.sparse_dense_matmul
for some support for tf.sparse.SparseTensor multiplication.

b_is_sparse

If True , b is treated as a sparse matrix. Notice, this
does not support tf.sparse.SparseTensor , it just makes optimizations
that assume most values in b are zero.
See tf.sparse.sparse_dense_matmul
for some support for tf.sparse.SparseTensor multiplication.

output_type

The output datatype if needed. Defaults to None in which case the output_type is the same as input type. Currently only works when input tensors are type (u)int8 and output_type can be int32. 
name

Name for the operation (optional). 
Returns  

A tf.Tensor of the same type as a and b where each innermost matrix
is the product of the corresponding matrices in a and b , e.g. if all
transpose or adjoint attributes are False :


Note

This is matrix product, not elementwise product. 
Raises  

ValueError

If transpose_a and adjoint_a , or transpose_b and
adjoint_b are both set to True .

TypeError

If output_type is specified but the types of a , b and
output_type is not (u)int8, (u)int8 and int32.

__mod__
__mod__(
y
)
Returns elementwise remainder of division.
This follows Python semantics in that the
result here is consistent with a flooring divide. E.g.
floor(x / y) * y + floormod(x, y) = x
, regardless of the signs of x and y.
Args  

x

A Tensor . Must be one of the following types: int8 , int16 , int32 ,
int64 , uint8 , uint16 , uint32 , uint64 , bfloat16 , half ,
float32 , float64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as x .

__mul__
__mul__(
y
)
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ne__
__ne__(
other
)
Compares two variables elementwise for equality.
__neg__
__neg__(
name=None
) > Annotated[Any, tf.raw_ops.Any
]
Computes numerical negative value elementwise.
I.e., \(y = x\).
Args  

x

A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , int8 , int16 , int32 , int64 , complex64 , complex128 .

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as x .

__or__
__or__(
y
)
__pow__
__pow__(
y
)
Computes the power of one value to another.
Given a tensor x
and a tensor y
, this operation computes \(x^y\) for
corresponding elements in x
and y
. For example:
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
Args  

x

A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .

y

A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .

name

A name for the operation (optional). 
Returns  

A Tensor .

__radd__
__radd__(
x
)
The operation invoked by the Tensor.add
operator.
Purpose in the API  

This method is exposed in TensorFlow's API so that library developers
can register dispatching for Tensor.add to allow it to handle
custom composite tensors & other custom objects.
The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation. 
Args  

x

The lefthand side of the + operator.

y

The righthand side of the + operator.

name

an optional name for the operation. 
Returns  

The result of the elementwise + operation.

__rand__
__rand__(
x
)
__rdiv__
__rdiv__(
x
)
Divides x / y elementwise (using Python 2 division operator semantics). (deprecated)
This function divides x
and y
, forcing Python 2 semantics. That is, if x
and y
are both integers then the result will be an integer. This is in
contrast to Python 3, where division with /
is always a float while division
with //
is always an integer.
Args  

x

Tensor numerator of real numeric type.

y

Tensor denominator of real numeric type.

name

A name for the operation (optional). 
Returns  

x / y returns the quotient of x and y.

Migrate to TF2
This function is deprecated in TF2. Prefer using the Tensor division operator,
tf.divide
, or tf.math.divide
, which obey the Python 3 division operator
semantics.
__rfloordiv__
__rfloordiv__(
x
)
Divides x / y
elementwise, rounding toward the most negative integer.
Mathematically, this is equivalent to floor(x / y). For example: floor(8.4 / 4.0) = floor(2.1) = 2.0 floor(8.4 / 4.0) = floor(2.1) = 3.0 This is equivalent to the '//' operator in Python 3.0 and above.
Args  

x

Tensor numerator of real numeric type.

y

Tensor denominator of real numeric type.

name

A name for the operation (optional). 
Returns  

x / y rounded toward infinity.

Raises  

TypeError

If the inputs are complex. 
__rmatmul__
__rmatmul__(
x
)
Multiplies matrix a
by matrix b
, producing a
* b
.
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication dimensions, and any further outer dimensions specify matching batch size.
Both matrices must be of the same type. The supported types are:
bfloat16
, float16
, float32
, float64
, int32
, int64
,
complex64
, complex128
.
Either matrix can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the matrices contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
a_is_sparse
or b_is_sparse
flag to True
. These are False
by default.
This optimization is only available for plain matrices (rank2 tensors) with
datatypes bfloat16
or float32
.
A simple 2D tensor matrix multiplication:
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
a # 2D tensor
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
b # 2D tensor
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[ 7, 8],
[ 9, 10],
[11, 12]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 58, 64],
[139, 154]], dtype=int32)>
A batch matrix multiplication with batch shape [2]:
a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
a # 3D tensor
<tf.Tensor: shape=(2, 2, 3), dtype=int32, numpy=
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]], dtype=int32)>
b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
b # 3D tensor
<tf.Tensor: shape=(2, 3, 2), dtype=int32, numpy=
array([[[13, 14],
[15, 16],
[17, 18]],
[[19, 20],
[21, 22],
[23, 24]]], dtype=int32)>
c = tf.matmul(a, b)
c # `a` * `b`
<tf.Tensor: shape=(2, 2, 2), dtype=int32, numpy=
array([[[ 94, 100],
[229, 244]],
[[508, 532],
[697, 730]]], dtype=int32)>
Since python >= 3.5 the @ operator is supported
(see PEP 465). In TensorFlow,
it simply calls the tf.matmul()
function, so the following lines are
equivalent:
d = a @ b @ [[10], [11]]
d = tf.matmul(tf.matmul(a, b), [[10], [11]])
Args  

a

tf.Tensor of type float16 , float32 , float64 , int32 ,
complex64 , complex128 and rank > 1.

b

tf.Tensor with same type and rank as a .

transpose_a

If True , a is transposed before multiplication.

transpose_b

If True , b is transposed before multiplication.

adjoint_a

If True , a is conjugated and transposed before
multiplication.

adjoint_b

If True , b is conjugated and transposed before
multiplication.

a_is_sparse

If True , a is treated as a sparse matrix. Notice, this
does not support tf.sparse.SparseTensor , it just makes optimizations
that assume most values in a are zero.
See tf.sparse.sparse_dense_matmul
for some support for tf.sparse.SparseTensor multiplication.

b_is_sparse

If True , b is treated as a sparse matrix. Notice, this
does not support tf.sparse.SparseTensor , it just makes optimizations
that assume most values in b are zero.
See tf.sparse.sparse_dense_matmul
for some support for tf.sparse.SparseTensor multiplication.

output_type

The output datatype if needed. Defaults to None in which case the output_type is the same as input type. Currently only works when input tensors are type (u)int8 and output_type can be int32. 
name

Name for the operation (optional). 
Returns  

A tf.Tensor of the same type as a and b where each innermost matrix
is the product of the corresponding matrices in a and b , e.g. if all
transpose or adjoint attributes are False :


Note

This is matrix product, not elementwise product. 
Raises  

ValueError

If transpose_a and adjoint_a , or transpose_b and
adjoint_b are both set to True .

TypeError

If output_type is specified but the types of a , b and
output_type is not (u)int8, (u)int8 and int32.

__rmod__
__rmod__(
x
)
Returns elementwise remainder of division.
This follows Python semantics in that the
result here is consistent with a flooring divide. E.g.
floor(x / y) * y + floormod(x, y) = x
, regardless of the signs of x and y.
Args  

x

A Tensor . Must be one of the following types: int8 , int16 , int32 ,
int64 , uint8 , uint16 , uint32 , uint64 , bfloat16 , half ,
float32 , float64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as x .

__rmul__
__rmul__(
x
)
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ror__
__ror__(
x
)
__rpow__
__rpow__(
x
)
Computes the power of one value to another.
Given a tensor x
and a tensor y
, this operation computes \(x^y\) for
corresponding elements in x
and y
. For example:
x = tf.constant([[2, 2], [3, 3]])
y = tf.constant([[8, 16], [2, 3]])
tf.pow(x, y) # [[256, 65536], [9, 27]]
Args  

x

A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .

y

A Tensor of type float16 , float32 , float64 , int32 , int64 ,
complex64 , or complex128 .

name

A name for the operation (optional). 
Returns  

A Tensor .

__rsub__
__rsub__(
x
)
Returns x  y elementwise.
Both input and output have a range (inf, inf)
.
Example usages below.
Subtract operation between an array and a scalar:
x = [1, 2, 3, 4, 5]
y = 1
tf.subtract(x, y)
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
tf.subtract(y, x)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([ 0, 1, 2, 3, 4], dtype=int32)>
Note that binary 
operator can be used instead:
x = tf.convert_to_tensor([1, 2, 3, 4, 5])
y = tf.convert_to_tensor(1)
x  y
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
Subtract operation between an array and a tensor of same shape:
x = [1, 2, 3, 4, 5]
y = tf.constant([5, 4, 3, 2, 1])
tf.subtract(y, x)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([ 4, 2, 0, 2, 4], dtype=int32)>
For example,
x = tf.constant([1, 2], dtype=tf.int8)
y = [2**8 + 1, 2**8 + 2]
tf.subtract(x, y)
<tf.Tensor: shape=(2,), dtype=int8, numpy=array([0, 0], dtype=int8)>
When subtracting two input values of different shapes, tf.subtract
follows the
general broadcasting rules
. The two input array shapes are compared elementwise. Starting with the
trailing dimensions, the two dimensions either have to be equal or one of them
needs to be 1
.
For example,
x = np.ones(6).reshape(2, 3, 1)
y = np.ones(6).reshape(2, 1, 3)
tf.subtract(x, y)
<tf.Tensor: shape=(2, 3, 3), dtype=float64, numpy=
array([[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]])>
Example with inputs of different dimensions:
x = np.ones(6).reshape(2, 3, 1)
y = np.ones(6).reshape(1, 6)
tf.subtract(x, y)
<tf.Tensor: shape=(2, 3, 6), dtype=float64, numpy=
array([[[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]]])>
Args  

x

A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 , uint32 , uint64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as x .

__rtruediv__
__rtruediv__(
x
)
Divides x / y elementwise (using Python 3 division operator semantics).
This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
x / y
division in Python 3 and in Python 2.7 with
from __future__ import division
. If you want integer division that rounds
down, use x // y
or tf.math.floordiv
.
x
and y
must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to float32
for int8
and int16
and float64
for int32
and int64
(matching the behavior of Numpy).
Args  

x

Tensor numerator of numeric type.

y

Tensor denominator of numeric type.

name

A name for the operation (optional). 
Returns  

x / y evaluated in floating point.

Raises  

TypeError

If x and y have different dtypes.

__rxor__
__rxor__(
x
)
__sub__
__sub__(
y
)
Returns x  y elementwise.
Both input and output have a range (inf, inf)
.
Example usages below.
Subtract operation between an array and a scalar:
x = [1, 2, 3, 4, 5]
y = 1
tf.subtract(x, y)
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
tf.subtract(y, x)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([ 0, 1, 2, 3, 4], dtype=int32)>
Note that binary 
operator can be used instead:
x = tf.convert_to_tensor([1, 2, 3, 4, 5])
y = tf.convert_to_tensor(1)
x  y
<tf.Tensor: shape=(5,), dtype=int32, numpy=array([0, 1, 2, 3, 4], dtype=int32)>
Subtract operation between an array and a tensor of same shape:
x = [1, 2, 3, 4, 5]
y = tf.constant([5, 4, 3, 2, 1])
tf.subtract(y, x)
<tf.Tensor: shape=(5,), dtype=int32,
numpy=array([ 4, 2, 0, 2, 4], dtype=int32)>
For example,
x = tf.constant([1, 2], dtype=tf.int8)
y = [2**8 + 1, 2**8 + 2]
tf.subtract(x, y)
<tf.Tensor: shape=(2,), dtype=int8, numpy=array([0, 0], dtype=int8)>
When subtracting two input values of different shapes, tf.subtract
follows the
general broadcasting rules
. The two input array shapes are compared elementwise. Starting with the
trailing dimensions, the two dimensions either have to be equal or one of them
needs to be 1
.
For example,
x = np.ones(6).reshape(2, 3, 1)
y = np.ones(6).reshape(2, 1, 3)
tf.subtract(x, y)
<tf.Tensor: shape=(2, 3, 3), dtype=float64, numpy=
array([[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]],
[[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]]])>
Example with inputs of different dimensions:
x = np.ones(6).reshape(2, 3, 1)
y = np.ones(6).reshape(1, 6)
tf.subtract(x, y)
<tf.Tensor: shape=(2, 3, 6), dtype=float64, numpy=
array([[[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]]])>
Args  

x

A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 , uint32 , uint64 .

y

A Tensor . Must have the same type as x .

name

A name for the operation (optional). 
Returns  

A Tensor . Has the same type as x .

__truediv__
__truediv__(
y
)
Divides x / y elementwise (using Python 3 division operator semantics).
This function forces Python 3 division operator semantics where all integer
arguments are cast to floating types first. This op is generated by normal
x / y
division in Python 3 and in Python 2.7 with
from __future__ import division
. If you want integer division that rounds
down, use x // y
or tf.math.floordiv
.
x
and y
must have the same numeric type. If the inputs are floating
point, the output will have the same type. If the inputs are integral, the
inputs are cast to float32
for int8
and int16
and float64
for int32
and int64
(matching the behavior of Numpy).
Args  

x

Tensor numerator of numeric type.

y

Tensor denominator of numeric type.

name

A name for the operation (optional). 
Returns  

x / y evaluated in floating point.

Raises  

TypeError

If x and y have different dtypes.

__xor__
__xor__(
y
)