Tensorflow学习率的learning rate decay

x = tf.Variable(1.0)
y = x.assign_add(1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(x)
print sess.run(y)
print sess.run(x)

import tensorflow as tf

global_step = tf.Variable(0, trainable=False)

initial_learning_rate = 0.1 #初始学习率

learning_rate = tf.train.exponential_decay(initial_learning_rate,
global_step=global_step,
decay_steps=10,decay_rate=0.9)
opt = tf.train.GradientDescentOptimizer(learning_rate)

add_global = global_step.assign_add(1)
with tf.Session() as sess:
tf.global_variables_initializer().run()
print(sess.run(learning_rate))
for i in range(1):
_, rate = sess.run([add_global, learning_rate])
print(rate)