About names of variable scope in tensorflow

Question

Recently I have been trying to learn to use TensorFlow, and I do not understand how variable scopes work exactly. In particular, I have the following problem:

import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn

inputs = [tf.placeholder(tf.float32,shape=[10,10]) for _ in range(5)]
cell = rnn_cell.BasicLSTMCell(10)
outpts, states = rnn.rnn(cell, inputs, dtype=tf.float32)

print outpts[2].name
# ==> u'RNN/BasicLSTMCell_2/mul_2:0'

Where does the '_2' in 'BasicLSTMCell_2' come from? How does it work when later using tf.get_variable(reuse=True) to get the same variable again?

edit: I think I find a related problem:

def creating(s):
    with tf.variable_scope('test'):
        with tf.variable_scope('inner'):
            a=tf.get_variable(s,[1])
    return a

def creating_mod(s):
    with tf.variable_scope('test'):
        with tf.variable_scope('inner'):
            a=tf.Variable(0.0, name=s)
    return a

tf.ops.reset_default_graph()
a=creating('a')
b=creating_mod('b')
c=creating('c')
d=creating_mod('d')

print a.name, '\n', b.name,'\n', c.name,'\n', d.name

The output is

test/inner/a:0 
test_1/inner/b:0 
test/inner/c:0 
test_3/inner/d:0

I'm confused...

Answer 1

The answer above is somehow misguiding.

Let me answer why you got two different scope names, even though it looks like that you defined two identical functions: creating and creating_mod.

This is simply because you used tf.Variable(0.0, name=s) to create the variable in the function creating_mod.

ALWAYS use tf.get_variable, if you want your variable to be recognized by scope!

Check out this issue for more details.

Thanks!