[Tensorflow]회귀 모델

import tensorflow as tf

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_initalizer()

sess = tf.Session()

sess.run(init)

for i in ragne(10000):

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]}))