tf.contrib.eager.Variable

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Variable based on resource handles.

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()

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.

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.
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

class SaveSliceInfo

Methods

assign

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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

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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

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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

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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

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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

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Evaluates and returns the value of this variable.

experimental_ref

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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

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Returns a Variable object created from variable_def.

gather_nd

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Reads the value of this variable sparsely, using gather_nd.

get_shape

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Alias of Variable.shape.

initialized_value

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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

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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

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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

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read_value

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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

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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

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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

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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

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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

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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

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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 Kth 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

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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 Kth 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

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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 Kth 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

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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

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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

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Unsupported.

sparse_read

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Reads the value of this variable sparsely, using gather.

to_proto

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Converts a ResourceVariable to a VariableDef protocol buffer.

Args
export_scope Optional string. Name scope to remo