tf.Variable

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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.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.

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 v1-style 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.

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.

aggregation

constraint 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

Child Classes

class SaveSliceInfo

Methods

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).

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.

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).

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.

assign_sub

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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).

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.

batch_scatter_update

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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.

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.

TypeError if sparse_delta is not an IndexedSlices.

count_up_to

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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).

limit value at which incrementing the variable raises an error.

eval

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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())

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

experimental_ref

View source

experimental_ref()

DEPRECATED FUNCTION

from_proto

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@staticmethod
from_proto(
    variable_def, import_scope=None
)

Returns a Variable object created from variable_def.

gather_nd

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gather_nd(
    indices, name=None
)

Gather slices from params into a Tensor with shape specified by indices.

See tf.gather_nd for details.

indices A Tensor. Must be one of the following types: int32, int64. Index tensor. name A name for the operation (optional).

get_shape

View source

get_shape()

Alias of Variable.shape.

initialized_value

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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)

load

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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]

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

ValueError Session is not passed and no default session

read_value

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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.

ref

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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

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scatter_add(
    sparse_delta, use_locking=False, name=None
)

Adds tf.IndexedSlices to this variable.

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.

TypeError if sparse_delta is not an IndexedSlices.

scatter_div

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scatter_div(
    sparse_delta, use_locking=False, name=None
)

Divide this variable by tf.IndexedSlices.

sparse_delta tf.IndexedSlices to divide this variable by. use_locking If True, use locking during the operation. name the name of the operation.

TypeError if sparse_delta is not an IndexedSlices.

scatter_max

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scatter_max(
    sparse_delta, use_locking=False, name=None
)

Updates this variable with the max of tf.IndexedSlices and itself.

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.

TypeError if sparse_delta is not an IndexedSlices.

scatter_min

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scatter_min(
    sparse_delta, use_locking=False, name=None
)

Updates this variable with the min of tf.IndexedSlices and itself.

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.

TypeError if sparse_delta is not an IndexedSlices.

scatter_mul

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scatter_mul(
    sparse_delta, use_locking=False, name=None
)

Multiply this variable by tf.IndexedSlices.

sparse_delta tf.IndexedSlices to multiply this variable by. use_locking If True, use locking during the operation. name the name of the operation.

TypeError if sparse_delta is not an IndexedSlices.

scatter_nd_add

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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])
    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.

indices The indices to be used in the operation. updates The values to be used in the operation. name the name of the operation.

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])
    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.

indices The indices to be used in the operation. updates The values to be used in the operation. name the name of the operation.

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])
    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.

indices The indices to be used in the operation. updates The values to be used in the operation. name the name of the operation.

scatter_sub

View source

scatter_sub(
    sparse_delta, use_locking=False, name=None
)

Subtracts tf.IndexedSlices from this variable.

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.

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.

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.

TypeError if sparse_delta is not an IndexedSlices.

set_shape

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set_shape(
    shape
)

Overrides the shape for this variable.

shape the TensorShape representing the overridden shape.

sparse_read

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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.

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).

to_proto

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to_proto(
    export_scope=None
)

Converts a Variable to a VariableDef protocol buffer.

export_scope Optional string. Name scope to remove.

value

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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.

__abs__

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__abs__(
    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. 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]])>

x A Tensor or SparseTensor of type float16, float32, float64, int32, int64, complex64 or complex128. name A name for the operation (optional).

__add__

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__add__(
    y
)

The operation invoked by the Tensor.add operator.

The API symbol is not intended to be called by users directly and does appear in TensorFlow's generated documentation.

x The left-hand side of the + operator. y The right-hand side of the + operator. name an optional name for the operation.

__and__

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__and__(
    y
)

__div__

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__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.

x Tensor numerator of real numeric type. y Tensor denominator of real numeric type. name A name for the operation (optional).

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.