Tensorflow get all variables in scope

I think you want tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='my_scope'). This will get all variables in a scope.

To pass to an optimizer you do not want all variables you would just want the trainable variables. Those are also kept in a default collection, which is tf.GraphKeys.TRAINABLE_VARIABLES.

User correctly pointed out that you need tf.get_collection(). I will just give a simple example how to do this:

import tensorflow as tf

with tf.name_scope('some_scope1'):
    a = tf.Variable(1, 'a')
    b = tf.Variable(2, 'b')
    c = tf.Variable(3, 'c')

with tf.name_scope('some_scope2'):
    d = tf.Variable(4, 'd')
    e = tf.Variable(5, 'e')
    f = tf.Variable(6, 'f')

h = tf.Variable(8, 'h')

for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='some_scope'):
    print i   # i.name if you want just a name

Notice that you can provide any of the graphKeys and scope is a regular expression:

scope: (Optional.) If supplied, the resulting list is filtered to include only items whose name attribute matches using re.match. Items without a name attribute are never returned if a scope is supplied and the choice or re.match means that a scope without special tokens filters by prefix.

So if you will pass 'some_scope' you will get 6 variables.