[ML] Linear Regression의 Hypothesis와 cost

tf.Variable : tensorflow가 자체적으로 변경시키는 variable, trainable 한 variable.

reduce_mean : 평균을 내줌

ex) t = [1, 2, 3, 4]

     tf.reduce_mean(t) ==> 2.5

linear_regression.py

# Lab 2 Linear Regression
 
import tensorflow as tf
 
# X and Y data
x_train = [1, 2, 3]
y_train = [1, 2, 3]
 
# H(x) = Wx + b
W = tf.Variable(tf.random_normal([1]), name = 'weight')
b = tf.Variable(tf.random_normal([1]), name = 'bias')
 
# Our hypothesis XW + b
hypothesis = x_train * W + b
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - y_train))
 
# Minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.01)
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())
 
# Fit the Line
for step in range(2001):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(cost), sess.run(W), sess.run(b))
 

linear_regression_feed.py

# Lab 2 Linear Regression
import tensorflow as tf
 
W = tf.Variable(tf.random_normal([1]), name = 'weight')
b = tf.Variable(tf.random_normal([1]), name = 'bias')
X = tf.placeholder(tf.float32, shape=[None])
Y = tf.placeholder(tf.float32, shape=[None])
 
# Our hypothesis XW+b
hypothesis = X * W + b
 
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
 
# Minimize
optimzier = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimzier.minimize(cost)
 
# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())
 
# Fit the line With new training data
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)