tensorflow05 《TensorFlow实战Google深度学习框架》笔记-04-03学习率设置

# 《TensorFlow实战Google深度学习框架》04 深层神经网络# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts04.03.py 学习率设置# 假设我们要最小化函数 $y=x^2$, 选择初始点$x_0=5$# 1. 学习率为1的时候，x在5和-5之间震荡import tensorflow as tfTRAINING_STEPS = 10LEARNING_RATE = 1x = tf.Variable(tf.constant(5, dtype=tf.float32), name="x")y = tf.square(x)train_op = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(y)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for i in range(TRAINING_STEPS):        sess.run(train_op)        x_value = sess.run(x)        print("After %s iteration(s): x%s is %f."% (i+1, i+1, x_value))'''After 1 iteration(s): x1 is -5.000000.After 2 iteration(s): x2 is 5.000000.After 3 iteration(s): x3 is -5.000000.After 4 iteration(s): x4 is 5.000000.After 5 iteration(s): x5 is -5.000000.After 6 iteration(s): x6 is 5.000000.After 7 iteration(s): x7 is -5.000000.After 8 iteration(s): x8 is 5.000000.After 9 iteration(s): x9 is -5.000000.After 10 iteration(s): x10 is 5.000000.'''# 2. 学习率为0.001的时候，下降速度过慢，在901轮时才收敛到0.823355TRAINING_STEPS = 1000LEARNING_RATE = 0.001x = tf.Variable(tf.constant(5, dtype=tf.float32), name="x")y = tf.square(x)train_op = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(y)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for i in range(TRAINING_STEPS):        sess.run(train_op)        if i % 100 == 0:            x_value = sess.run(x)            print("After %s iteration(s): x%s is %f."% (i+1, i+1, x_value))'''After 1 iteration(s): x1 is 4.990000.After 101 iteration(s): x101 is 4.084646.After 201 iteration(s): x201 is 3.343555.After 301 iteration(s): x301 is 2.736923.After 401 iteration(s): x401 is 2.240355.After 501 iteration(s): x501 is 1.833880.After 601 iteration(s): x601 is 1.501153.After 701 iteration(s): x701 is 1.228794.After 801 iteration(s): x801 is 1.005850.After 901 iteration(s): x901 is 0.823355.'''# 3. 使用指数衰减的学习率，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不错的收敛程度TRAINING_STEPS = 100global_step = tf.Variable(0)LEARNING_RATE = tf.train.exponential_decay(0.1, global_step, 1, 0.96, staircase=True)x = tf.Variable(tf.constant(5, dtype=tf.float32), name="x")y = tf.square(x)train_op = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(y, global_step=global_step)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for i in range(TRAINING_STEPS):        sess.run(train_op)        if i % 10 == 0:            LEARNING_RATE_value = sess.run(LEARNING_RATE)            x_value = sess.run(x)            print("After %s iteration(s): x%s is %f, learning rate is %f."% (i+1, i+1, x_value, LEARNING_RATE_value))'''After 1 iteration(s): x1 is 4.000000, learning rate is 0.096000.After 11 iteration(s): x11 is 0.690561, learning rate is 0.063824.After 21 iteration(s): x21 is 0.222583, learning rate is 0.042432.After 31 iteration(s): x31 is 0.106405, learning rate is 0.028210.After 41 iteration(s): x41 is 0.065548, learning rate is 0.018755.After 51 iteration(s): x51 is 0.047625, learning rate is 0.012469.After 61 iteration(s): x61 is 0.038558, learning rate is 0.008290.After 71 iteration(s): x71 is 0.033523, learning rate is 0.005511.After 81 iteration(s): x81 is 0.030553, learning rate is 0.003664.After 91 iteration(s): x91 is 0.028727, learning rate is 0.002436.'''