学习率周期性变化，能后解决陷入鞍点的问题，更多的方式请参考https://github.com/bckenstler/CLR 

一，学习率周期性变化
global_steps=tf.placeholder(shape=[1],dtype=tf.int64)
cycle = tf.cast(tf.floor(1. + tf.cast(global_steps, dtype=tf.float32) /(2 * 1000.)), dtype=tf.float32)

x = tf.cast(tf.abs(tf.cast(global_steps, dtype=tf.float32) / 1000. - 2. * cycle + 1.), dtype=tf.float32)

learning_rate = 1e-6 + (1e-3 - 1e-6) * tf.maximum(0., (1 - x))
with tf.Session() as sess:
    lr_list = []
    cycle_list=[]
    for i in range(8000):
        lr=sess.run(learning_rate,feed_dict={global_steps:[i]})
        lr_list.append(lr)

        cl = sess.run(cycle, feed_dict={global_steps: [i]})
        cycle_list.append(cl)

    plt.plot(lr_list)
    plt.show()

    print(cycle_list)

二，学习率周期性衰减

global_steps=tf.placeholder(shape=[1],dtype=tf.int64)
cycle = tf.cast(tf.floor(1. + tf.cast(global_steps, dtype=tf.float32) /(2 * 1000.)), dtype=tf.float32)

x = tf.cast(tf.abs(tf.cast(global_steps, dtype=tf.float32) / 1000. - 2. * cycle + 1.), dtype=tf.float32)

learning_rate = 1e-6 + (1e-3 - 1e-6) * tf.maximum(0., (1 - x))/tf.cast(2**(cycle-1),dtype=tf.float32)
with tf.Session() as sess:
    lr_list = []
    cycle_list=[]
    for i in range(8000):
        lr=sess.run(learning_rate,feed_dict={global_steps:[i]})
        lr_list.append(lr)

        cl = sess.run(cycle, feed_dict={global_steps: [i]})
        cycle_list.append(cl)

    plt.plot(lr_list)
    plt.show()

    print(cycle_list)