import tensorflow as tf
tf.set_random_seed(337)

#1. 데이터
x= tf.placeholder(tf.float32, shape=[None])
y= tf.placeholder(tf.float32, shape=[None])

w = tf.Variable(tf.random_normal([1]), dtype=tf.float32) #random_uniform 균등 분포(N빵), random_normal 정규 분포 [1]은 1개짜리
b = tf.Variable(tf.random_normal([1]), dtype=tf.float32) #random_uniform 균등 분포(N빵), random_normal 정규 분포 [1]은 1개짜리

#####[실습]#####
#2. 모델 구성
hypothesis = x * w + b

# #3-1 컴파일
loss = tf.reduce_mean(tf.square(hypothesis-y))

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
#w = w - 러닝레이트*(로스를 w로 편미분한 값)
#
train = optimizer.minimize(loss)

#3-2 훈련
with tf.compat.v1.Session() as sess :
# sess = tf.compat.v1.Session()
    sess.run(tf.global_variables_initializer())

    #model.fit()
    epochs = 5001
    for step in range(epochs) :
        # sess.run(train)
        _, loss_val, w_val, b_val = sess.run([train, loss, w, b], feed_dict = {x:[1,2,3,4,5], y:[2,4,6,8,10]})   
        #플레이스홀드로 공간만 만들어뒀기 때문에 feed_dic으로 값 지정
        #
        if step %20 == 0 :
            # print(step, 'loss :', sess.run(loss), 'w :', sess.run(w), 'b :', sess.run(b))            
            print(step, 'loss :', loss_val, 'w :', w_val, 'b :', b_val)

    
    x_data= tf.placeholder(tf.float32, shape=[None])
    # x_data = [6,7,8]
    y_pred = x_data*w_val+b_val
    y_predict = sess.run([y_pred], feed_dict={x_data:[6,7,8]})
    
    print('y_predict :', y_predict[0][0], y_predict[0][1], y_predict[0][2])
    
    
    # 쌤 코드
    # x_data = [6,7,8]
    # x_test = tf.compat.v1.placeholder(tf.float32, shape=[None])
    # y_predict = x_test * w_val + b_val
    # print(sess.run(y_predict,feed_dict={x_test:x_data}))
    
    

#실습

tf08_2_lr