Tensorflow Practice 2-4

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)

'''初始化变量'''
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]))