04-2. Multi-variable linear regression with tf.matmul

■ matmul 함수 사용.

x_data 

- 4x3 배열 생성

y_data 

- 4x1 배열 생성

hypothesis = tf.matmul(X,W)+b

- X : 4x3 , Y:3x1 matrix multiplier 수행 후 b와 더함.

# Lab04-2 Multi-variable linear regression
 
import tensorflow as tf
 
tf.set_random_seed(777)  # for reproducibility
 
 
x_data = [[73., 80., 75.],
 
          [93., 88., 93.],
 
          [89., 91., 90.],
 
          [96., 98., 100.],
 
          [73., 66., 70.]]
 
y_data = [[152.],
 
          [185.],
 
          [180.],
 
          [196.],
 
          [142.]]
 
 
 
# placeholders for a tensor that will be always fed.
 
X = tf.placeholder(tf.float32, shape=[None, 3])
 
Y = tf.placeholder(tf.float32, shape=[None, 1])
 
 
W = tf.Variable(tf.random_normal([3, 1]), name='weight')
 
b = tf.Variable(tf.random_normal([1]), name='bias')
 
 
# Hypothesis
 
hypothesis = tf.matmul(X, W) + b
 
 
# Simplified cost/loss function
 
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
 
# Minimize
 
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
 
train = optimizer.minimize(cost)
 
 
# Launch the graph in a session.
 
sess = tf.Session()
 
# Initializes global variables in the graph.
 
sess.run(tf.global_variables_initializer())
 
 
for step in range(2001):
 
    cost_val, hy_val, _ = sess.run(
 
        [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})
 
    if step % 10 == 0:
 
        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)