tf.VariableScope

Class VariableScope

Defined in tensorflow/python/ops/variable_scope.py.

Variable scope object to carry defaults to provide to get_variable.

Many of the arguments we need for get_variable in a variable store are most easily handled with a context. This object is used for the defaults.

Attributes:

• name: name of the current scope, used as prefix in get_variable.
• initializer: default initializer passed to get_variable.
• regularizer: default regularizer passed to get_variable.
• reuse: Boolean, None, or tf.AUTO_REUSE, setting the reuse in get_variable. When eager execution is enabled this argument is always forced to be False.
• caching_device: string, callable, or None: the caching device passed to get_variable.
• partitioner: callable or None: the partitioner passed to get_variable.
• custom_getter: default custom getter passed to get_variable.
• name_scope: The name passed to tf.name_scope.
• dtype: default type passed to get_variable (defaults to DT_FLOAT).
• use_resource: if False, create a normal Variable; if True create an experimental ResourceVariable with well-defined semantics. Defaults to False (will later change to True). When eager execution is enabled this argument is always forced to be True.
• 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.

__init__

__init__(
    reuse,
    name='',
    initializer=None,
    regularizer=None,
    caching_device=None,
    partitioner=None,
    custom_getter=None,
    name_scope='',
    dtype=tf.dtypes.float32,
    use_resource=None,
    constraint=None
)

Creates a new VariableScope with the given properties.

Properties

caching_device

constraint

custom_getter

dtype

initializer

name

original_name_scope

partitioner

regularizer

reuse

use_resource

Methods

tf.VariableScope.get_collection

get_collection(name)

Get this scope's variables.

tf.VariableScope.get_variable

get_variable(
    var_store,
    name,
    shape=None,
    dtype=None,
    initializer=None,
    regularizer=None,
    reuse=None,
    trainable=None,
    collections=None,
    caching_device=None,
    partitioner=None,
    validate_shape=True,
    use_resource=None,
    custom_getter=None,
    constraint=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.VariableAggregation.NONE
)

Gets an existing variable with this name or create a new one.

tf.VariableScope.global_variables

global_variables()

Get this scope's global variables.

tf.VariableScope.local_variables

local_variables()

Get this scope's local variables.

tf.VariableScope.reuse_variables

reuse_variables()

Reuse variables in this scope.

tf.VariableScope.set_caching_device

set_caching_device(caching_device)

Set caching_device for this scope.

tf.VariableScope.set_custom_getter

set_custom_getter(custom_getter)

Set custom getter for this scope.

tf.VariableScope.set_dtype

set_dtype(dtype)

Set data type for this scope.

tf.VariableScope.set_initializer

set_initializer(initializer)

Set initializer for this scope.

tf.VariableScope.set_partitioner

set_partitioner(partitioner)

Set partitioner for this scope.

tf.VariableScope.set_regularizer

set_regularizer(regularizer)

Set regularizer for this scope.

tf.VariableScope.set_use_resource

set_use_resource(use_resource)

Sets whether to use ResourceVariables for this scope.

tf.VariableScope.trainable_variables

trainable_variables()

Get this scope's trainable variables.