It is actually possible to re-enter a closed variable scope, as in the example below.

with tf.variable_scope('test') as vscope: pass
with tf.variable_scope(vs) as vscope_:
     assert vscope.name == vscope_.name

However, the re-entered variable scope does not copy the reuse status of the original one.

@gaohuazuo hmmm that assert statement returns True (as long as you are consistent about vscope vs vs); however...

with tf.variable_scope('foo') as vs:
    x = tf.Variable([1])
with tf.variable_scope(vs) as vs_:
    assert vs.name == vs_.name
    y = tf.Variable([1])
x.name
>>> u'foo/Variable:0'
y.name
>>> u'foo_1/Variable:0'

with corresponding non-overlapping name-scopes in tensorboard. This is not changed by passing reuse = True.

Anyway, if possible I think it would be useful to be able to access name-scope of a given variable....without writing a (simple but) hacky regex. The current approach I am using to build layers in a more functional manner does not lend itself to the workflow described above.

@ebrevdo: @meereeum's example with y.name == 'foo_1/Variable:0' looks like a bug. Could you take a look?

I'll assign to lukasz, he may have code fixing this.

I'm working on a CL/PR that at least adds the name scope to variable scope. Will report back here once that's in.

A recent CL correcting this is now live on master. It includes a unit test that re-enters a variable scope and the original name scope this variable scope was created in (by using the new .original_name_scope property). See here: variable_scope_test. Closing this now, please re-open or make a new issue if you find more problems!