Tensorflow学习率的learning rate decay

本文主要是介绍Tensorflow学习率的learning rate decay，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

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)

输出 1,2,2注意其x会变的

import tensorflow as tfglobal_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)

参考：
http://blog.csdn.net/u012436149/article/details/62058318

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