[section_4_lab1] Multivariable linear regression using Matrix

Multivariable linear regression

• x의 데이타가 하나가 아닌 여러 데이터인 경우에 linear regression입니다.

import tensorflow as tf

# 입력 값을 넣어준다.
x1_data = [73., 93., 89., 96., 73.]
x2_data = [80., 88., 91., 98., 66.]
x3_data = [75., 93., 90., 100., 70.]
y_data = [152., 185., 180., 196., 142.]

# placeholders for a tensor : 입력 값을 저장할 공간.
x1 = tf.placeholder(tf.float32)
x2 = tf.placeholder(tf.float32)
x3 = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

# 구하고자 하는 값 w,b
w1 = tf.Variable(tf.random_normal([1]), name='weight1')
w2 = tf.Variable(tf.random_normal([1]), name='weight2')
w3 = tf.Variable(tf.random_normal([1]), name='weight3')
b = tf.Variable(tf.random_normal([1]), name='bias')
hypothesis = x1 * w1 + x2 * w2 + x3 * w3 + b

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
# Minimize. Need a very small learning rate for this data set
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())

# 2000번 학습중 1000번 마다 출력을 해준다.
# 현재의 step, cost, w와b를 통한 y를 출력해준다.
for step in range(2001):
   cost_val, hy_val, _ = sess.run([cost, hypothesis, train],
                        feed_dict={x1: x1_data, x2: x2_data, x3: x3_data, Y: y_data})
   if step % 1000 == 0:
       print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)

• 실행 결과
  

Multivariable linear regression using Matrix

• 이번에는 Matrix를 사용하여 동일한 작업을 하였습니다.

import tensorflow as tf

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 % 1000 == 0:
       print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)

• 실행 결과