TensorFlow北大公开课学习笔记-4.2学习率

#coding:utf-8
#设损失函数 loss=(w+1)^2，令w初值是常数5，反向传播就是求最优w,即最小loss对应的w值
import tensorflow as tf
#定义待优化参数w初值赋5
w=tf.Variable(tf.constant(5,dtype=tf.float32))
#定义损失函数loss
loss=tf.square(w+1)
#定义反向传播方法
train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#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(400):
        sess.run(train_step)
        w_val=sess.run(w)
        loss_val=sess.run(loss)
        print("After %s steps: w is %f, loss is %f."%(i,w_val,loss_val))

#coding:utf-8
#设损失函数 loss=(w+1)^2，令w初值是常数5，反向传播就是求最优w,即最小loss对应的w值
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w初值赋5
w=tf.Variable(tf.constant(5,dtype=tf.float32))
#定义损失函数loss
loss=tf.square(w+1)
#定义反向传播方法
train_step=tf.train.GradientDescentOptimizer(learning_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)
        learning_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,learning_rate_val,loss_val))