# coding: utf-8# #### 假設(shè)我們要最小化函數(shù) $y=x^2$, 選擇初始點 $x_0=5$# #### 1. 學習率為1的時候，x在5和-5之間震蕩。# In[1]:import tensorflow as tf TRAINING_STEPS = 10 LEARNING_RATE = 1 x = 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)#result 學習率為1的時候，x在5和-5之間震蕩。 # 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. 學習率為0.001的時候，下降速度過慢，在901輪時才收斂到0.823355。# In[2]: TRAINING_STEPS = 1000 LEARNING_RATE = 0.001 x = 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. 使用指數(shù)衰減的學習率，在迭代初期得到較高的下降速度，可以在較小的訓練輪數(shù)下取得不錯的收斂程度。# In[3]: TRAINING_STEPS = 100 global_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.

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轉(zhuǎn)載于:https://www.cnblogs.com/adong7639/p/7554114.html

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