View source on GitHub |
Variable based on resource handles.
tf.contrib.eager.Variable(
initial_value=None, trainable=None, collections=None, validate_shape=True,
caching_device=None, name=None, dtype=None, variable_def=None,
import_scope=None, constraint=None, distribute_strategy=None,
synchronization=None, aggregation=None, shape=None
)
See the Variables How To for a high level overview.
A ResourceVariable
allows you to maintain state across subsequent calls to
session.run.
The ResourceVariable
constructor requires an initial value for the variable,
which can be a Tensor
of any type and shape. The 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.
Just like any Tensor
, variables created with
tf.Variable(use_resource=True)
can be used as inputs for other Ops in the
graph. Additionally, all the operators overloaded for the Tensor
class are
carried over to variables, so you can also add nodes to the graph by just
doing arithmetic on variables.
Unlike ref-based variable, a ResourceVariable has well-defined semantics. Each usage of a ResourceVariable in a TensorFlow graph adds a read_value operation to the graph. The Tensors returned by a read_value operation are guaranteed to see all modifications to the value of the variable which happen in any operation on which the read_value depends on (either directly, indirectly, or via a control dependency) and guaranteed to not see any modification to the value of the variable from operations that depend on the read_value operation. Updates from operations that have no dependency relationship to the read_value operation might or might not be visible to read_value.
For example, if there is more than one assignment to a ResourceVariable in a single session.run call there is a well-defined value for each operation which uses the variable's value if the assignments and the read are connected by edges in the graph. Consider the following example, in which two writes can cause tf.Variable and tf.ResourceVariable to behave differently:
a = tf.Variable(1.0, use_resource=True)
a.initializer.run()
assign = a.assign(2.0)
with tf.control_dependencies([assign]):
b = a.read_value()
with tf.control_dependencies([b]):
other_assign = a.assign(3.0)
with tf.control_dependencies([other_assign]):
# Will print 2.0 because the value was read before other_assign ran. If
# `a` was a tf.Variable instead, 2.0 or 3.0 could be printed.
tf.compat.v1.Print(b, [b]).eval()
Args | |
---|---|
initial_value
|
A Tensor , or Python object convertible to a Tensor ,
which is the initial value for the Variable. Can also be a
callable with no argument that returns the initial value when called.
(Note that initializer functions from init_ops.py must first be bound
to a shape before being used here.)
|
trainable
|
If True , the default, also adds the variable to the graph
collection GraphKeys.TRAINABLE_VARIABLES . This collection is used as
the default list of variables to use by the Optimizer classes.
Defaults to True , unless synchronization is set to ON_READ , in
which case it defaults to False .
|
collections
|
List of graph collections keys. The new variable is added to
these collections. Defaults to [GraphKeys.GLOBAL_VARIABLES] .
|
validate_shape
|
Ignored. Provided for compatibility with tf.Variable. |
caching_device
|
Optional device string or function 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.
|
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 float32 will be used (if it is a Python object convertible to a Tensor). |
variable_def
|
VariableDef protocol buffer. If not None, recreates the
ResourceVariable object with its contents. variable_def and other
arguments (except for import_scope) are mutually exclusive.
|
import_scope
|
Optional string . Name scope to add to the
ResourceVariable. Only used when variable_def is provided.
|
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.
|
distribute_strategy
|
The tf.distribute.Strategy this variable is being created inside of. |
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.
|
Raises | |
---|---|
ValueError
|
If the initial value is not specified, or does not have a
shape and validate_shape is True .
|
Attributes | |
---|---|
aggregation
|
|
constraint
|
Returns the constraint function associated with this variable. |
create
|
The op responsible for initializing this variable. |
device
|
The device this variable is on. |
dtype
|
The dtype of this variable. |
graph
|
The Graph of this variable.
|
handle
|
The handle by which this variable can be accessed. |
initial_value
|
Returns the Tensor used as the initial value for the variable. |
initializer
|
The op responsible for initializing this variable. |
name
|
The name of the handle for this variable. |
op
|
The op for this variable. |
shape
|
The shape of this variable. |
synchronization
|
|
trainable
|
Child Classes
Methods
assign
assign(
value, use_locking=None, name=None, read_value=True
)
Assigns a new value to this variable.
Args | |
---|---|
value
|
A Tensor . The new value for this variable.
|
use_locking
|
If True , use locking during the assignment.
|
name
|
The name to use for the assignment. |
read_value
|
A bool . Whether to read and return the new value of the
variable or not.
|
Returns | |
---|---|
If read_value is True , this method will return the new value of the
variable after the assignment has completed. Otherwise, when in graph mode
it will return the Operation that does the assignment, and when in eager
mode it will return None .
|
assign_add
assign_add(
delta, use_locking=None, name=None, read_value=True
)
Adds a value to this variable.
Args | |
---|---|
delta
|
A Tensor . The value to add to this variable.
|
use_locking
|
If True , use locking during the operation.
|
name
|
The name to use for the operation. |
read_value
|
A bool . Whether to read and return the new value of the
variable or not.
|
Returns | |
---|---|
If read_value is True , this method will return the new value of the
variable after the assignment has completed. Otherwise, when in graph mode
it will return the Operation that does the assignment, and when in eager
mode it will return None .
|
assign_sub
assign_sub(
delta, use_locking=None, name=None, read_value=True
)
Subtracts a value from this variable.
Args | |
---|---|
delta
|
A Tensor . The value to subtract from this variable.
|
use_locking
|
If True , use locking during the operation.
|
name
|
The name to use for the operation. |
read_value
|
A bool . Whether to read and return the new value of the
variable or not.
|
Returns | |
---|---|
If read_value is True , this method will return the new value of the
variable after the assignment has completed. Otherwise, when in graph mode
it will return the Operation that does the assignment, and when in eager
mode it will return None .
|
batch_scatter_update
batch_scatter_update(
sparse_delta, use_locking=False, name=None
)
Assigns tf.IndexedSlices
to this variable batch-wise.
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
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
)
Evaluates and returns the value of this variable.
experimental_ref
experimental_ref()
Returns a hashable reference object to this Variable.
The primary usecase 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.
import tensorflow as tf
x = tf.Variable(5)
y = tf.Variable(10)
z = tf.Variable(10)
# The followings will raise an exception starting 2.0
# TypeError: Variable is unhashable if Variable equality is enabled.
variable_set = {x, y, z}
variable_dict = {x: 'five', y: 'ten'}
Instead, we can use variable.experimental_ref()
.
variable_set = {x.experimental_ref(),
y.experimental_ref(),
z.experimental_ref()}
print(x.experimental_ref() in variable_set)
==> True
variable_dict = {x.experimental_ref(): 'five',
y.experimental_ref(): 'ten',
z.experimental_ref(): 'ten'}
print(variable_dict[y.experimental_ref()])
==> ten
Also, the reference object provides .deref()
function that returns the
original Variable.
x = tf.Variable(5)
print(x.experimental_ref().deref())
==> <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=5>
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
)
Reads the value of this variable sparsely, using gather_nd
.
get_shape
get_shape()
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.
|
is_initialized
is_initialized(
name=None
)
Checks whether a resource variable has been initialized.
Outputs boolean scalar indicating whether the tensor has been initialized.
Args | |
---|---|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
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 |
numpy
numpy()
read_value
read_value()
Constructs an op which reads the value of this variable.
Should be used when there are multiple reads, or when it is desirable to read the value only after some condition is true.
Returns | |
---|---|
the read operation. |
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered addition has completed.
|
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered division has completed.
|
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered maximization has completed.
|
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered minimization has completed.
|
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered multiplication has completed.
|
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.
ref
is a Tensor
with rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into ref
.
It must be shape [d_0, ..., d_{Q-2}, 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 ref
.
updates
is Tensor
of rank Q-1+P-K
with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
add = ref.scatter_nd_add(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(add)
The resulting update to ref 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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
scatter_nd_sub
scatter_nd_sub(
indices, updates, name=None
)
Applies sparse subtraction to individual values or slices in a Variable.
ref
is a Tensor
with rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into ref
.
It must be shape [d_0, ..., d_{Q-2}, 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 ref
.
updates
is Tensor
of rank Q-1+P-K
with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_sub(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref would look like this:
[1, -9, 3, -6, -6, 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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
scatter_nd_update
scatter_nd_update(
indices, updates, name=None
)
Applies sparse assignment to individual values or slices in a Variable.
ref
is a Tensor
with rank P
and indices
is a Tensor
of rank Q
.
indices
must be integer tensor, containing indices into ref
.
It must be shape [d_0, ..., d_{Q-2}, 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 ref
.
updates
is Tensor
of rank Q-1+P-K
with shape:
[d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]].
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this:
ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1] ,[7]])
updates = tf.constant([9, 10, 11, 12])
op = ref.scatter_nd_update(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to ref 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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
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 | |
---|---|
A Tensor that will hold the new value of this variable after
the scattered subtraction has completed.
|
Raises | |
---|---|
TypeError
|
if sparse_delta is not an IndexedSlices .
|
set_shape
set_shape(
shape
)
Unsupported.
sparse_read
sparse_read(
indices, name=None
)
Reads the value of this variable sparsely, using gather
.
to_proto
to_proto(
export_scope=None
)
Converts a ResourceVariable
to a VariableDef
protocol buffer.
Args | |
---|---|
export_scope
|
Optional string . Name scope to remove.
|
Raises | |
---|---|
RuntimeError
|
If run in EAGER mode. |
Returns | |
---|---|
A VariableDef protocol buffer, or None if the Variable is not
in the specified name scope.
|
value
value()
A cached operation which reads the value of this variable.
__abs__
__abs__(
x, name=None
)
Computes the absolute value of a tensor.
Given a tensor of integer or floating-point 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
. All
elements in x
must be complex numbers of the form \(a + bj\). The
absolute value is computed as \( \sqrt{a^2 + b^2}\). For example:
x = tf.constant([[-2.25 + 4.75j], [-3.25 + 5.75j]])
tf.abs(x) # [5.25594902, 6.60492229]
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 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__(
x, y
)
Dispatches to add for strings and add_v2 for all other types.
__and__
__and__(
x, y
)
Returns the truth value of x AND y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__bool__
__bool__()
__div__
__div__(
x, y
)
Divide two values using Python 2 semantics.
Used for Tensor.div.
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.
|
__eq__
__eq__(
other
)
Compares two variables element-wise for equality.
__floordiv__
__floordiv__(
x, y
)
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.compat.v1.div(x,y)
for integers, but uses
tf.floor(tf.compat.v1.div(x,y))
for
floating point arguments so that the result is always an integer (though
possibly an integer represented as floating point). This op is generated by
x // y
floor division in Python 3 and in Python 2.7 with
from __future__ import division
.
x
and y
must have the same type, and the result will have the same type
as well.
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 down.
|
Raises | |
---|---|
TypeError
|
If the inputs are complex. |
__ge__
__ge__(
x, y, name=None
)
Returns the truth value of (x >= y) element-wise.
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__(
var, slice_spec
)
Creates a slice helper object given a variable.
This allows creating a sub-tensor 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()
.
For example,
import tensorflow as tf
A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
with tf.compat.v1.Session() as sess:
sess.run(tf.compat.v1.global_variables_initializer())
print(sess.run(A[:2, :2])) # => [[1,2], [4,5]]
op = A[:2,:2].assign(22. * tf.ones((2, 2)))
print(sess.run(op)) # => [[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__(
x, y, name=None
)
Returns the truth value of (x > y) element-wise.
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__(
x, name=None
)
Returns the truth value of NOT x element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__iter__
__iter__()
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 variable's Tensor from 0 to infinity. Declaring this method prevents this unintended behavior.
Raises | |
---|---|
TypeError
|
when invoked. |
__le__
__le__(
x, y, name=None
)
Returns the truth value of (x <= y) element-wise.
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__(
x, y, name=None
)
Returns the truth value of (x < y) element-wise.
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__(
x, 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 arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are:
float16
, float32
, float64
, int32
, 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 (rank-2 tensors) with
datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a`
# [[1, 2, 3],
# [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
# 2-D tensor `b`
# [[ 7, 8],
# [ 9, 10],
# [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
# `a` * `b`
# [[ 58, 64],
# [139, 154]]
c = tf.matmul(a, b)
# 3-D tensor `a`
# [[[ 1, 2, 3],
# [ 4, 5, 6]],
# [[ 7, 8, 9],
# [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])
# 3-D tensor `b`
# [[[13, 14],
# [15, 16],
# [17, 18]],
# [[19, 20],
# [21, 22],
# [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])
# `a` * `b`
# [[[ 94, 100],
# [229, 244]],
# [[508, 532],
# [697, 730]]]
c = tf.matmul(a, b)
# 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
|
Tensor of type float16 , float32 , float64 , int32 , complex64 ,
complex128 and rank > 1.
|
b
|
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.
|
b_is_sparse
|
If True , b is treated as a sparse matrix.
|
name
|
Name for the operation (optional). |
Returns | |
---|---|
A Tensor of the same type as a and b where each inner-most 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 element-wise product. |
Raises | |
---|---|
ValueError
|
If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True. |
__mod__
__mod__(
x, y
)
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent
with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: int32 , int64 , 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__(
x, y
)
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ne__
__ne__(
other
)
Compares two variables element-wise for equality.
__neg__
__neg__(
x, name=None
)
Computes numerical negative value element-wise.
I.e., \(y = -x\).
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , int32 , int64 , complex64 , complex128 .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor . Has the same type as x .
|
__nonzero__
__nonzero__()
__or__
__or__(
x, y
)
Returns the truth value of x OR y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__pow__
__pow__(
x, 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__(
y, x
)
Dispatches to add for strings and add_v2 for all other types.
__rand__
__rand__(
y, x
)
Returns the truth value of x AND y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__rdiv__
__rdiv__(
y, x
)
Divide two values using Python 2 semantics.
Used for Tensor.div.
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.
|
__rfloordiv__
__rfloordiv__(
y, x
)
Divides x / y
elementwise, rounding toward the most negative integer.
The same as tf.compat.v1.div(x,y)
for integers, but uses
tf.floor(tf.compat.v1.div(x,y))
for
floating point arguments so that the result is always an integer (though
possibly an integer represented as floating point). This op is generated by
x // y
floor division in Python 3 and in Python 2.7 with
from __future__ import division
.
x
and y
must have the same type, and the result will have the same type
as well.
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 down.
|
Raises | |
---|---|
TypeError
|
If the inputs are complex. |
__rmatmul__
__rmatmul__(
y, 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 arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are:
float16
, float32
, float64
, int32
, 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 (rank-2 tensors) with
datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a`
# [[1, 2, 3],
# [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
# 2-D tensor `b`
# [[ 7, 8],
# [ 9, 10],
# [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
# `a` * `b`
# [[ 58, 64],
# [139, 154]]
c = tf.matmul(a, b)
# 3-D tensor `a`
# [[[ 1, 2, 3],
# [ 4, 5, 6]],
# [[ 7, 8, 9],
# [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])
# 3-D tensor `b`
# [[[13, 14],
# [15, 16],
# [17, 18]],
# [[19, 20],
# [21, 22],
# [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])
# `a` * `b`
# [[[ 94, 100],
# [229, 244]],
# [[508, 532],
# [697, 730]]]
c = tf.matmul(a, b)
# 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
|
Tensor of type float16 , float32 , float64 , int32 , complex64 ,
complex128 and rank > 1.
|
b
|
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.
|
b_is_sparse
|
If True , b is treated as a sparse matrix.
|
name
|
Name for the operation (optional). |
Returns | |
---|---|
A Tensor of the same type as a and b where each inner-most 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 element-wise product. |
Raises | |
---|---|
ValueError
|
If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True. |
__rmod__
__rmod__(
y, x
)
Returns element-wise remainder of division. When x < 0
xor y < 0
is
true, this follows Python semantics in that the result here is consistent
with a flooring divide. E.g. floor(x / y) * y + mod(x, y) = x
.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: int32 , int64 , 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__(
y, x
)
Dispatches cwise mul for "DenseDense" and "DenseSparse".
__ror__
__ror__(
y, x
)
Returns the truth value of x OR y element-wise.
Args | |
---|---|
x
|
A Tensor of type bool .
|
y
|
A Tensor of type bool .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of type bool .
|
__rpow__
__rpow__(
y, 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__(
y, x
)
Returns x - y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 .
|
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__(
y, x
)
__rxor__
__rxor__(
y, x
)
Logical XOR function.
x ^ y = (x | y) & ~(x & y)
Inputs are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed.
Usage:
x = tf.constant([False, False, True, True], dtype = tf.bool)
y = tf.constant([False, True, False, True], dtype = tf.bool)
z = tf.logical_xor(x, y, name="LogicalXor")
# here z = [False True True False]
Args | |
---|---|
x
|
A Tensor type bool.
|
y
|
A Tensor of type bool.
|
Returns | |
---|---|
A Tensor of type bool with the same size as that of x or y.
|
__sub__
__sub__(
x, y
)
Returns x - y element-wise.
Args | |
---|---|
x
|
A Tensor . Must be one of the following types: bfloat16 , half , float32 , float64 , uint8 , int8 , uint16 , int16 , int32 , int64 , complex64 , complex128 .
|
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__(
x, y
)
__xor__
__xor__(
x, y
)
Logical XOR function.
x ^ y = (x | y) & ~(x & y)
Inputs are tensor and if the tensors contains more than one element, an element-wise logical XOR is computed.
Usage:
x = tf.constant([False, False, True, True], dtype = tf.bool)
y = tf.constant([False, True, False, True], dtype = tf.bool)
z = tf.logical_xor(x, y, name="LogicalXor")
# here z = [False True True False]
Args | |
---|---|
x
|
A Tensor type bool.
|
y
|
A Tensor of type bool.
|
Returns | |
---|---|
A Tensor of type bool with the same size as that of x or y.
|