指数衰减学习率

#coding:utf-8
#设损失函数 loss=(w+1)^2,令w初值是常数10.反向传播就是求最优w，即求最小loss对应的w值
#使用指数衰减学习率，在迭代初期得到较高的下降速度，可以在较小的训练轮数下取得更有效收敛度

import tensorflow as tf

LEARNING_RATE_BASE = 0.1 #最初学习率
LEARNING_RATE_DECAY = 0.99 #学习率衰减率
LEARNING_RATE_STEP = 1 #喂入多少轮BATCH_SIZE后，更新一次学习率，一般设为：总样本数/BATCH_SIZE

#运行了几轮BATCH_SIZE的计数器，初值给0，设为不被训练
global_step = tf.Variable(0, trainable=False)
#定义指数下降学习率
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,LEARNING_RATE_STEP, LEARNING_RATE_DECAY, staircase=True)
#定义待优化参数，初值给10S
w = tf.Variable(tf.constant(5,dtype=tf.float32))
#定义损失函数loss
loss = tf.square(w+1)
train_step = tf.train.GradientDescentOptimizer(learing_rate).minimize(loss, global_step=global_step)
#生成会话，训练40轮
with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    for i in range(40):
        sess.run(train_step)
        learing_rate_val = sess.run(learning_rate)
        global_step_val = sess.run(global_step)
        w_val = sess.run(w)
        loss_val = sess.run(loss)
        print "After %s steps: global_step is %f, w is %f, learning_rate is %f, loss is %f." % (i,global_step_val,w_val,learing_rate_val,loss_val)