This article shows how to use sharing variables in Tensroflow. But I still have a question: dose sharing variables have the same value? To answer this question, I write these code below:
import tensorflow as tf
#initial = tf.constant(0.1, shape=[1])
initial = tf.truncated_normal(shape=[3], stddev=1, mean=1)
a = [None] * 3
b = [None] * 3
c = [None] * 3
with tf.variable_scope(tf.get_variable_scope()):
for i in xrange(3):
with tf.name_scope("my_%d" % i):
a[i] = tf.Variable(initial, [3])
b[i] = tf.Variable(initial, [3])
c[i] = a[i] + b[i]
tf.get_variable_scope().reuse_variables()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
curr_a, curr_b, curr_a1, curr_b1, curr_a2, curr_b2, curr_c = sess.run([a[0], b[0], a[1], b[1], a[2], b[2], c[0]], feed_dict={a[0]:[0.1, 0.1, 0.1], b[0]:[0.2,0.2,0.2]})
print(curr_a, curr_b, curr_a1, curr_b1, curr_a2, curr_b2, curr_c)
The result of running these python code is:
(array([ 0.1, 0.1, 0.1], dtype=float32), array([ 0.2, 0.2, 0.2], dtype=float32), array([ 0.90568691, 1.30992699, 1.49500561], dtype=float32), array([ 0.90568691, 1.30992699, 1.49500561], dtype=float32), array([ 0.90568691, 1.30992699, 1.49500561], dtype=float32), array([ 0.90568691, 1.30992699, 1.49500561], dtype=float32), array([ 0.30000001, 0.30000001, 0.30000001], dtype=float32))
Therefore, the "sharing variables" mechanism is made only for convenience of writing short code to create multi-models. For sharing same value for different variables, we still need 'assign' operation.