Tensorflow - simple example

Code:

import tensorflow as tf
import numpy as np


#Use numpy to generate 100 random points
#sample
x_data = np.random.rand(100)
y_data = x_data * 0.1 + 0.2

#Construct a linear model
b = tf.Variable(0.3)
k = tf.Variable(1.0)
y = k * x_data + b

#Secondary cost function
#The square of the error takes the average
loss = tf.reduce_mean(tf.square(y_data-y))
#define a gradient descent optimizer for training
optimizaer = tf.train.GradientDescentOptimizer(0.2)
#define a minimization cost function
train = optimizaer.minimize(loss)

#Initialize variables
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(201):
        sess.run (train)
        #Print the value of k and b every 20 times
        if step%20 == 0:
            print(step, sess.run([k, b]))

operation result:

0 [0.84287709, 0.061396986]
20 [0.49860233, -0.032348074]
40 [0.35844025, 0.049353316]
60 [0.26756388, 0.1023258]
80 [0.20864277, 0.13667135]
100 [0.1704403, 0.15893984]
120 [0.14567113, 0.17337798]
140 [0.12961161, 0.18273918]
160 [0.11919918, 0.18880866]
180 [0.1124481, 0.19274391]
200 [0.10807092, 0.19529541]