一个小的实例

import tensorflow as tf
import numpy as np
#使用numpy生成100个随机点
x_data = np.random.rand(100)
y_data = x_data*0.1+0.2

#构造一个线性模型
b =tf.Variable(0.)
k = tf.Variable(0.)
y =k*x_data+b

#二次代价函数
loss = tf.reduce_mean(tf.square(y_data-y))

#定义一个梯度下降法来进行训练的优化器
optimizer = tf.train.GradientDescentOptimizer(0.2)

#最小化代价函数
train =optimizer.minimize(loss)
init =tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    for step in range(201):
        sess.run(train)
        if step%20 ==0:
            print(step,sess.run([k,b]))