tf.contrib.eager.Variable

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

class SaveSliceInfo

Methods

`assign`

View source

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`

View source

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`

View source

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`

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 subtraction has completed.

Raises
`TypeError`	if `sparse_delta` is not an `IndexedSlices`.

`count_up_to`

View source

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`

View source

eval(
    session=None
)

Evaluates and returns the value of this variable.

`experimental_ref`

View source

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`

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
)

Reads the value of this variable sparsely, using gather_nd.

`get_shape`

View source

get_shape()

Alias of Variable.shape.

`initialized_value`

View source

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`

View source

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`

View source

load(
    value, session=None
)

Load n

tf.contrib.eager.Variable

Args

Raises

Attributes

Child Classes

Methods

assign

assign_add

assign_sub

batch_scatter_update

count_up_to

eval

experimental_ref

from_proto

gather_nd

get_shape

initialized_value

is_initialized

load

`assign`

`assign_add`

`assign_sub`

`batch_scatter_update`

`count_up_to`

`eval`

`experimental_ref`

`from_proto`

`gather_nd`

`get_shape`

`initialized_value`

`is_initialized`

`load`