#!/usr/bin/pyton

import tensorflow as tf
import numpy as np 
import matplotlib.pyplot as plt 

x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases

loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

sess = tf.Session()
#initialize_local_variables no more support in new version
if int((tf.__version__).split(‘.‘)[1]) < 12 and int((tf.__version__).split(‘.‘)[0]) < 1:
    init = tf.initialize_all_variables()
else:
    init = tf.global_variables_initializer()

sess.run(init)
plt.figure()
for step in range(201):
    sess.run(train)

plt.scatter(x_data, sess.run(y))
plt.plot(x_data, sess.run(y), color = ‘red‘)
plt.show()
sess.close()