tf.variable_scope

Class variable_scope

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

A context manager for defining ops that creates variables (layers).

This context manager validates that the (optional) values are from the same graph, ensures that graph is the default graph, and pushes a name scope and a variable scope.

If name_or_scope is not None, it is used as is. If name_or_scope is None, then default_name is used. In that case, if the same name has been previously used in the same scope, it will be made unique by appending _N to it.

Variable scope allows you to create new variables and to share already created ones while providing checks to not create or share by accident. For details, see the Variable Scope How To, here we present only a few basic examples.

Simple example of how to create a new variable:

with tf.variable_scope("foo"):
    with tf.variable_scope("bar"):
        v = tf.get_variable("v", [1])
        assert v.name == "foo/bar/v:0"

Simple example of how to reenter a premade variable scope safely:

with tf.variable_scope("foo") as vs:
  pass

# Re-enter the variable scope.
with tf.variable_scope(vs,
                       auxiliary_name_scope=False) as vs1:
  # Restore the original name_scope.
  with tf.name_scope(vs1.original_name_scope):
      v = tf.get_variable("v", [1])
      assert v.name == "foo/v:0"
      c = tf.constant([1], name="c")
      assert c.name == "foo/c:0"

Basic example of sharing a variable AUTO_REUSE:

def foo():
  with tf.variable_scope("foo", reuse=tf.AUTO_REUSE):
    v = tf.get_variable("v", [1])
  return v

v1 = foo()  # Creates v.
v2 = foo()  # Gets the same, existing v.
assert v1 == v2

Basic example of sharing a variable with reuse=True:

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True):
    v1 = tf.get_variable("v", [1])
assert v1 == v

Sharing a variable by capturing a scope and setting reuse:

with tf.variable_scope("foo") as scope:
    v = tf.get_variable("v", [1])
    scope.reuse_variables()
    v1 = tf.get_variable("v", [1])
assert v1 == v

To prevent accidental sharing of variables, we raise an exception when getting an existing variable in a non-reusing scope.

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1])
    v1 = tf.get_variable("v", [1])
    #  Raises ValueError("... v already exists ...").

Similarly, we raise an exception when trying to get a variable that does not exist in reuse mode.

with tf.variable_scope("foo", reuse=True):
    v = tf.get_variable("v", [1])
    #  Raises ValueError("... v does not exists ...").

Note that the reuse flag is inherited: if we open a reusing scope, then all its sub-scopes become reusing as well.

A note about name scoping: Setting reuse does not impact the naming of other ops such as mult. See related discussion on github#6189

Note that up to and including version 1.0, it was allowed (though explicitly discouraged) to pass False to the reuse argument, yielding undocumented behaviour slightly different from None. Starting at 1.1.0 passing None and False as reuse has exactly the same effect.

A note about using variable scopes in multi-threaded environment: Variable scopes are thread local, so one thread will not see another thread's current scope. Also, when using default_name, unique scopes names are also generated only on a per thread basis. If the same name was used within a different thread, that doesn't prevent a new thread from creating the same scope. However, the underlying variable store is shared across threads (within the same graph). As such, if another thread tries to create a new variable with the same name as a variable created by a previous thread, it will fail unless reuse is True.

Further, each thread starts with an empty variable scope. So if you wish to preserve name prefixes from a scope from the main thread, you should capture the main thread's scope and re-enter it in each thread. For e.g.

main_thread_scope = variable_scope.get_variable_scope()

# Thread's target function:
def thread_target_fn(captured_scope):
  with variable_scope.variable_scope(captured_scope):
    # .... regular code for this thread


thread = threading.Thread(target=thread_target_fn, args=(main_thread_scope,))

__init__

__init__(
    name_or_scope,
    default_name=None,
    values=None,
    initializer=None,
    regularizer=None,
    caching_device=None,
    partitioner=None,
    custom_getter=None,
    reuse=None,
    dtype=None,
    use_resource=None,
    constraint=None,
    auxiliary_name_scope=True
)

Initialize the context manager.

Args:

  • name_or_scope: string or VariableScope: the scope to open.
  • default_name: The default name to use if the name_or_scope argument is None, this name will be uniquified. If name_or_scope is provided it won't be used and therefore it is not required and can be None.
  • values: The list of Tensor arguments that are passed to the op function.
  • initializer: default initializer for variables within this scope.
  • regularizer: default regularizer for variables within this scope.
  • caching_device: default caching device for variables within this scope.
  • partitioner: default partitioner for variables within this scope.
  • custom_getter: default custom getter for variables within this scope.
  • reuse: True, None, or tf.AUTO_REUSE; if True, we go into reuse mode for this scope as well as all sub-scopes; if tf.AUTO_REUSE, we create variables if they do not exist, and return them otherwise; if None, we inherit the parent scope's reuse flag. When eager execution is enabled, new variables are always created unless an EagerVariableStore or template is currently active.
  • dtype: type of variables created in this scope (defaults to the type in the passed scope, or inherited from parent scope).
  • use_resource: If False, all variables will be regular Variables. If True, experimental ResourceVariables with well-defined semantics will be used instead. 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.
  • auxiliary_name_scope: If True, we create an auxiliary name scope with the scope. If False, we don't create it. Note that the argument is not inherited, and it only takes effect for once when creating. You should only use it for re-entering a premade variable scope.

Returns:

A scope that can be captured and reused.

Raises:

  • ValueError: when trying to reuse within a create scope, or create within a reuse scope.
  • TypeError: when the types of some arguments are not appropriate.

Methods

__enter__

__enter__()

__exit__

__exit__(
    type_arg,
    value_arg,
    traceback_arg
)