tf.train.GradientDescentOptimizer Optimizer

tf.train.GradientDescentOptimizer(learning_rate, use_locking=False,name=’GradientDescent’)

 参数：
learning_rate: A Tensor or a floating point value. 要使用的学习率
use_locking: 要是True的话，就对于更新操作（update operations.）使用锁
name: 名字，可选，默认是”GradientDescent”

minimize() 函数处理了梯度计算和参数更新两个操作
compute_gradients() 函数用于获取梯度
apply_gradients() 用于更新参数

sample

import tensorflow as tf

x = tf.Variable(2, name='x', dtype=tf.float32)
log_x = tf.log(x)
log_x_squared = tf.square(log_x)

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(log_x_squared)

init = tf.initialize_all_variables()

def optimize():
  with tf.Session() as session:
    session.run(init)
    print("starting at", "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))
    for step in range(10):  
      session.run(train)
      print("step", step, "x:", session.run(x), "log(x)^2:", session.run(log_x_squared))
        

optimize()