常见面试题

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
k = tf.Variable(0.)
b = tf.Variable(0.)
y = k * x_data + b

#定义损失函数
loss = tf.reduce_mean(tf.square(y_data - y))

#用梯度下降法作为训练的优化器，学习率设为0.2
optimizer = tf.train.GradientDescentOptimizer(0.2)

#最小化损失函数
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]))