tf.Variable
See the variable guide.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v2.Variable
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
)
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.functions. This works the
same way the un-decorated 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: 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.
Attributes:
aggregationconstraint: Returns the constraint function associated with this variable.
device: The device of this variable.
dtype: The DType of this variable.
graph: The Graph of this variable.
initial_value: Returns the Tensor used as the initial value for the variable.
Note that this is different from initialized_value() which runs
the op that initializes the variable before returning its value.
This method returns the tensor that is used by the op that initializes
the variable.
initializer: The initializer operation for this variable.
name: The name of this variable.
op: The Operation of this variable.
shape: The TensorShape of this variable.
synchronization
trainable
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
class SaveSliceInfo
Methods
__abs__
View source
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 \( \sqrt{a2 + b2}\). For example:
python
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__
View source
__add__(
a, *args, **kwargs
)
Dispatches to add for strings and add_v2 for all other types.
__and__
View source
__and__(
a, *args, **kwargs
)
Returns the truth value of x AND y element-wise.
NOTE: math.logical_and supports broadcasting. More about broadcasting
here
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.
__div__
View source
__div__(
a, *args, **kwargs
)
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__
View source
Compares two variables element-wise for equality.
__floordiv__
View source
__floordiv__(
a, *args, **kwargs
)
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__
View source
__ge__(
a, *args, **kwargs
)
Returns the truth value of (x >= y) element-wise.
NOTE: math.greater_equal supports broadcasting. More about broadcasting
here
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__
View source
__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:
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__
View source
__gt__(
a, *args, **kwargs
)
Returns the truth value of (x > y) element-wise.
NOTE: math.greater supports broadcasting. More about broadcasting
here
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__
View source
__invert__(
a, *args, **kwargs
)
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__
View source
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:
__le__
View source
__le__(
a, *args, **kwargs
)
Returns the truth value of (x <= y) element-wise.
NOTE: math.less_equal supports broadcasting. More about broadcasting
here
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__
View source
__lt__(
a, *args, **kwargs
)
Returns the truth value of (x < y) element-wise.
NOTE: math.less supports broadcasting. More about broadcasting
here
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__
View source
__matmul__(
a, *args, **kwargs
)
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:
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.
A simple 2-D tensor matrix multiplication:
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
a # 2-D tensor
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
b # 2-D tensor
c = tf.matmul(a, b)
c # a * b
A batch matrix multiplication with batch shape [2]
a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
a # 3-D tensor
b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
b # 3-D tensor
c = tf.matmul(a, b)
c # 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: 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.b_is_sparse: If True, b is treated as a sparse matrix.name: Name for the operation (optional).
Returns:
A tf.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:
output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]),
for all indices i, j.
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__
View source
__mod__(
a, *args, **kwargs
)
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.
NOTE: math.floormod supports broadcasting. More about broadcasting
here
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__
View source
__mul__(
a, *args, **kwargs
)
Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".
__ne__
View source
Compares two variables element-wise for equality.
__neg__
View source
__neg__(
a, *args, **kwargs
)
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.
__or__
View source
__or__(
a, *args, **kwargs
)
Returns the truth value of x OR y element-wise.
NOTE: math.logical_or supports broadcasting. More about broadcasting
here
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__
View source
__pow__(
a, *args, **kwargs
)
Computes the power of one value to another.
Given a tensor x and a tensor y, this operation computes \(xy\) 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__
View source
__radd__(
a, *args, **kwargs
)
Dispatches to add for strings and add_v2 for all other types.
__rand__
View source
__rand__(
a, *args, **kwargs
)
Returns the truth value of x AND y element-wise.
NOTE: math.logical_and supports broadcasting. More about broadcasting
here
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__
View source
__rdiv__(
a, *args, **kwargs
)
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__
View source
__rfloordiv__(
a, *args, **kwargs
)
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__
View source
__rmatmul__(
a, *args, **kwargs
)
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:
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.
A simple 2-D tensor matrix multiplication:
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
a # 2-D tensor
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
b # 2-D tensor
c = tf.matmul(a, b)
c # a * b
A batch matrix multiplication with batch shape [2]
a = tf.constant(np.arange(1, 13, dtype=np.int32), shape=[2, 2, 3])
a # 3-D tensor
b = tf.constant(np.arange(13, 25, dtype=np.int32), shape=[2, 3, 2])
b # 3-D tensor
c = tf.matmul(a, b)
c # 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: 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.b_is_sparse: If True, b is treated as a sparse matrix.name: Name for the operation (optional).
Returns:
A tf.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:
output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]),
for all indices i, j.
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__
View source
__rmod__(
a, *args, **kwargs
)
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.
NOTE: math.floormod supports broadcasting. More about broadcasting
here
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__
View source
__rmul__(
a, *args, **kwargs
)
Dispatches cwise mul for "Dense*Dense" and "Dense*Sparse".
__ror__
View source
__ror__(
a, *args, **kwargs
)
Returns the truth value of x OR y element-wise.
NOTE: math.logical_or supports broadcasting. More about broadcasting
here
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__
View source
__rpow__(
a, *args, **kwargs
)
Computes the power of one value to another.
Given a tensor x and a tensor y, this operation computes \(xy\) 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__
View source
__rsub__(
a, *args, **kwargs
)
Returns x - y element-wise.
NOTE: Subtract supports broadcasting. More about broadcasting
here
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__
View source
__rtruediv__(
a, *args, **kwargs
)
__rxor__
View source
__rxor__(
a, *args, **kwargs
)
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__
View source
__sub__(
a, *args, **kwargs
)
Returns x - y element-wise.
NOTE: Subtract supports broadcasting. More about broadcasting
here
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__
View source
__truediv__(
a, *args, **kwargs
)
__xor__
View source
__xor__(
a, *args, **kwargs
)
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.
assign
View source
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 createdread_value: if True, will return something which evaluates to the new
value of the variable; if False will return the assign op.
Returns:
A Tensor that will hold the new value of this variable after
the assignment has completed.
assign_add
View source
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 createdread_value: if True, will return something which evaluates to the new
value of the variable; if False will return the assign op.
Returns:
A Tensor that will hold the new value of this variable after
the addition has completed.
assign_sub
View source
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 createdread_value: if True, will return something which evaluates to the new
value of the variable; if False will return the assign op.
Returns:
A Tensor that will hold the new value of this variable after
the subtraction has completed.
batch_scatter_update
View source
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 assignment has completed.
Raises:
TypeError: if sparse_delta is not an IndexedSlices.
count_up_to
View source
Increments this variable until it reaches limit. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Prefer Dataset.range instead.
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
View source
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
View source
Returns a hashable reference object to this Variable.
Warning: Experimental API that could be changed or removed.
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
View source
@staticmethod
from_proto(
variable_def, import_scope=None
)
Returns a Variable object created from variable_def.
gather_nd
View source
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
View source
Alias of Variable.shape.
initialized_value
View source
Returns the value of the initialized variable. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
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
View source
load(
value, session=None
)
Load new value into this variable. (deprecated)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Prefer Variable.assign which has equivalent behavior in 2.X.
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 valuesession: 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
View source
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.
scatter_add
View source
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
View source
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
View source
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
View source
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
View source
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
View source
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_{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 Kth
dimension of self.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, self.shape[K], ..., self.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:
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])
add = v.scatter_nd_add(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(add)
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:
A Tensor that will hold the new value of this variable after
the scattered addition has completed.
scatter_nd_sub
View source
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_{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 Kth
dimension of self.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, self.shape[K], ..., self.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:
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])
op = v.scatter_nd_sub(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
The resulting update to v 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
View source
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_{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 Kth
dimension of self.
updates is Tensor of rank Q-1+P-K with shape:
[d_0, ..., d_{Q-2}, self.shape[K], ..., self.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:
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])
op = v.scatter_nd_assign(indices, updates)
with tf.compat.v1.Session() as sess:
print sess.run(op)
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:
A Tensor that will hold the new value of this variable after
the scattered assignment has completed.
scatter_sub
View source
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
View source
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 assignment has completed.
Raises:
TypeError: if sparse_delta is not an IndexedSlices.
set_shape
View source
Overrides the shape for this variable.
Args:
shape: the TensorShape representing the overridden shape.
sparse_read
View source
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
View source
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
View source
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.
View source on GitHub