指数衰减学习率的staircase

staircase:楼梯

如果为true,即楼梯为真,说明学习率要向楼梯一样下降;

看代码说话:

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-')
opo=tf.train.RMSPropOptimizer(0.01)

plt.show()


猜你喜欢

转载自blog.csdn.net/ningyanggege/article/details/80996003