tf.variable_creator_scope(variable_creator)
Defined in tensorflow/python/ops/variable_scope.py.
Scope which defines a variable creation function to be used by variable().
variable_creator is expected to be a function with the following signature:
def variable_creator(next_creator, **kwargs)
The creator is supposed to eventually call the next_creator to create a variable if it does want to create a variable and not call Variable or ResourceVariable directly. This helps make creators composable. A creator may choose to create multiple variables, return already existing variables, or simply register that a variable was created and defer to the next creators in line. Creators can also modify the keyword arguments seen by the next creators.
Custom getters in the variable scope will eventually resolve down to these custom creators when they do create variables.
The valid keyword arguments in kwds are:
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, 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.
trainable defaults to True unless synchronization is
set to ON_READ.
collections: List of graph collections keys. The new variable is added to
these collections. Defaults to [GraphKeys.GLOBAL_VARIABLES].
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.
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.
constraint: A constraint function to be applied to the variable after
updates by some algorithms.
use_resource: if True, a ResourceVariable is always created.
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. If synchronization is set to ON_READ,
trainable must not be set to True.
aggregation: Indicates how a distributed variable will be aggregated.
Accepted values are constants defined in the class
tf.VariableAggregation.
This set may grow over time, so it's important the signature of creators is as mentioned above.
Args:
variable_creator: the passed creator
Yields:
A scope in which the creator is active