Tesorflow 简单案例 直线的拟合

梯度下降法

import tensorflow as tfimport numpy as np#生成随机点x_data = np.random.rand(100)     #产生随机点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# 定义lossloss = tf.reduce_mean(tf.square(y_data-y))#定义优化方法optimizar = tf.train.GradientDescentOptimizer(0.2)#最小化losstrain = optimizar.minimize(loss)#变量初始化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 sess.run([k, b])[0.055063572, 0.10062776][0.10450891, 0.19752166][0.1027341, 0.19849722][0.10165788, 0.19908877][0.10100529, 0.19944745][0.10060959, 0.19966495][0.10036964, 0.19979683][0.10022413, 0.1998768][0.1001359, 0.1999253][0.10008241, 0.1999547][0.10004997, 0.19997254]