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Diffstat (limited to 'tensorflow/python/ops/variable_scope.py')
-rw-r--r-- | tensorflow/python/ops/variable_scope.py | 21 |
1 files changed, 20 insertions, 1 deletions
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index f49e2d314d..47414c28af 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -1786,6 +1786,23 @@ class variable_scope(object): assert v.name == "foo/bar/v:0" ``` + Simple example of how to reenter a premade variable scope safely: + + ```python + 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: ```python @@ -1924,7 +1941,9 @@ class variable_scope(object): (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 touch name scope. + 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. |