import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

xData = [1,2,3,4,5,6,7]
yData = [25000,55000,75000,110000,128000,155000,180000]
W = tf.Variable(tf.random_uniform([1], -100,100) )
b = tf.Variable(tf.random_uniform([1], -100,100) )
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
H = W * X + b
cost = tf.reduce_mean(tf.square(H-Y))
a = tf.Variable(0.01)
optimizer = tf.train.GradientDescentOptimizer(a)
train = optimizer.minimize(cost)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(5001) :
    sess.run(train , feed_dict={X : xData , Y : yData})
    if i % 500 == 0 :
        print(i , sess.run(cost , feed_dict={ X : xData , Y : yData}) , sess.run(W) , sess.run(b))
print(sess.run(H,feed_dict={X : [8]}))