tf.train.exponential_decay的用法

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

例如：

import tensorflow as tf;import numpy as np;import matplotlib.pyplot as plt;learning_rate = 0.1decay_rate = 0.96global_steps = 1000decay_steps = 100global_ = 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。


结果：