set default settings with tf.variable_scope and ways to reuse variables.

tf_variable_scope.py

import tensorflow as tf

#with default settings
def scope_defsetting():
    with tf.variable_scope('qa', initializer=tf.zeros_initializer()):
        #tf.get_variable_scope().reuse_variables()
        # if without setting initializer in get_varaible, use the default one in the scope.
        a = tf.get_variable('a', [2,4], tf.float32)
        #tf.get_variable_scope().reuse == True
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            print(sess.run(a))
            
# reuse variables 1
def scope_reuse():
    with tf.variable_scope('qa'):
        tf.get_variable_scope().reuse_variables()
        #codes ....
    with tf.variable_scope('qa', reuse = True):
        #codes ....
        pass
    with tf.variable_scope('qa') as scope:
        scope.reuse_variables()
        #codes ....

if __name == '__main__':
    # 1
    scope_defsetting()
    '''out:
    [[ 0.  0.  0.  0.]
     [ 0.  0.  0.  0.]]
    '''