Linear Regression 구현 (using placeholder)

import tensorflow as tf
 
 
X = tf.placeholder(tf.float32, shape=[None]) //shape이 None인 placeholder
Y = tf.placeholder(tf.float32, shape=[None])
W = tf.Variable(tf.random_normal([1]), name = 'weight')
b = tf.Variable(tf.random_normal([1]), name = 'bias')
 
 
Hypothesis = x_train * W +b //define Hypothesis
 
 
cost = tf.reduce_mean(tf.square(Hypothesis - y_train))
 
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01) //경사 하강법 , 학습비율 0.1
train = optimizer.minimize(cost) // optimizer의 minimize 함수 호출 하여 cost를 minimize함                                      
sess = tf.Session()
 
sess.run(tf.global_variables_initializer()) //W,b 와 같은 Variable을 initialize 함 
 
for step in range(2001):
    
    cost_val, W_val, b_val, _ = sess.run([cost, W, b, train],
        feed_dict = {X: [1,2,3,4,5], Y: [2.1,3.1,4.1,5.1,6.1]})
 
    if step %20 ==0:
        print(step ,cost_val ,W_val ,b_val)