tf.get_variable( name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, use_resource=None, custom_getter=None, constraint=None )
See the guide: Variables > Sharing Variables
Gets an existing variable with these parameters or create a new one.
This function prefixes the name with the current variable scope and performs reuse checks. See the Variable Scope How To for an extensive description of how reusing works. Here is a basic example:
def foo(): with tf.variable_scope("foo", reuse=tf.AUTO_REUSE): v = tf.get_variable("v", ) return v v1 = foo() # Creates v. v2 = foo() # Gets the same, existing v. assert v1 == v2
If initializer is
None (the default), the default initializer passed in
the variable scope will be used. If that one is
None too, a
glorot_uniform_initializer will be used. The initializer can also be
a Tensor, in which case the variable is initialized to this value and shape.
Similarly, if the regularizer is
None (the default), the default regularizer
passed in the variable scope will be used (if that is
then by default no regularization is performed).
If a partitioner is provided, a
PartitionedVariable is returned.
Accessing this object as a
Tensor returns the shards concatenated along
the partition axis.
Some useful partitioners are available. See, e.g.,
name: The name of the new or existing variable.
shape: Shape of the new or existing variable.
dtype: Type of the new or existing variable (defaults to
initializer: Initializer for the variable if one is created. Can either be an initializer object or a Tensor. If it's a Tensor, its shape must be known unless validate_shape is False.
regularizer: A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable will be added to the collection
tf.GraphKeys.REGULARIZATION_LOSSESand can be used for regularization.
Truealso add the variable to the graph collection
collections: List of graph collections keys to add the Variable to. Defaults to
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
Switchand other conditional statements.
partitioner: Optional callable that accepts a fully defined
dtypeof the Variable to be created, and returns a list of partitions for each axis (currently only one axis can be partitioned).
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. For this to be used the initializer must be a Tensor and not an initializer object.
use_resource: If False, creates a regular Variable. If true, creates an experimental ResourceVariable instead 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.
custom_getter: Callable that takes as a first argument the true getter, and allows overwriting the internal get_variable method. The signature of
custom_gettershould match that of this method, but the most future-proof version will allow for changes:
def custom_getter(getter, *args, **kwargs). Direct access to all
get_variableparameters is also allowed:
def custom_getter(getter, name, *args, **kwargs). A simple identity custom getter that simply creates variables with modified names is:
def custom_getter(getter, name, *args, **kwargs): return getter(name + '_suffix', *args, **kwargs)
The created or existing
PartitionedVariable, if a
partitioner was used).
ValueError: when creating a new variable and shape is not declared, when violating reuse during variable creation, or when
dtypedon't match. Reuse is set inside