tf.train.exponential_decay的用法

tf.train.exponential_decay(learning_rate, global_, decay_steps, decay_rate, staircase=True/False)

import tensorflow as tf;  import numpy as np;  import matplotlib.pyplot as plt;  learning_rate = 0.1  decay_rate = 0.96  global_steps = 1000  decay_steps = 100  global_ = tf.Variable(tf.constant(0))  c = tf.train.exponential_decay(learning_rate, global_, decay_steps, decay_rate, staircase=True)  d = tf.train.exponential_decay(learning_rate, global_, decay_steps, decay_rate, staircase=False)  T_C = []  F_D = []  with tf.Session() as sess:      for i in range(global_steps):          T_c = sess.run(c,feed_dict={global_: i})          T_C.append(T_c)          F_d = sess.run(d,feed_dict={global_: i})          F_D.append(F_d)  plt.figure(1)  plt.plot(range(global_steps), F_D, 'r-')  plt.plot(range(global_steps), T_C, 'b-')  plt.show()  

分析：
初始的学习速率是0.1，总的迭代次数是1000次，如果staircase=True，那就表明每decay_steps次计算学习速率变化，更新原始学习速率，如果是False，那就是每一步都更新学习速率。红色表示False，绿色表示True。

计算方式：decayed_lr = lr * decay_rate ^ (global_step/decay_steps)