CODEWITHSUNDEEP
pythonscopetensorflow
Veech
Veech

Reputation: 1463

Tensorflow VariableScope: original_name_scope vs name

In TensorFlow, the VariableScope class has both a original_name_scope and name attribute. What are their differences and when should I use one over the other? I can't seem to find much documentation on them.

Use case: I'm using the tf.get_collection(key, scope) method. Its second argument expects a string, but my variable my_scope has type VariableScope. I'm trying both

tf.get_collection(key, my_scope.name)

and

tf.get_collection(key, my_scope.original_scope_name)

. Both seem to work, but I'm not sure which is "right" and won't give me problems later down the road.

Upvotes: 0

Views: 838

Answers (1)

Veech
Veech

Reputation: 1463

foo.name returns the name (String) of the scope. On the other hand, foo.original_name_scope returns the same string as foo.name, except when the scope is recreated. In that case, all sub-scopes are appended with a _# as needed to make all calls to foo.original_name_scope return something unique for each instance of a scope.

For example, in this code:

with tf.variable_scope('a') as a:
    print(a.name)
    print(a.original_name_scope)
    print(a.original_name_scope)

with tf.variable_scope('a') as b:
    print(b.name)
    print(b.original_name_scope)

Returns

a
a/
a/
a
a_1/

Note that the calls to original_name_scope corresponding to different scope instances a return different values.

Presumably, this lets you distinguish between different scope instances with the same name.

Upvotes: 2

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