선형 회귀(Linear Regression) 코드 분석

 

# Lab 2 Linear Regression
import tensorflow as tf #텐서플로우
tf.set_random_seed(777)  # for reproducibility 

# X and Y data
x_train = [1, 2, 3]
y_train = [1, 2, 3]

# Try to find values for W and b to compute y_data = x_data * W + b
# We know that W should be 1 and b should be 0
# But let TensorFlow figure it out
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))

# optimizer
train = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)

# Launch the graph in a session.
with tf.Session() as sess:
    # Initializes global variables in the graph.
    sess.run(tf.global_variables_initializer())

    # Fit the line
    for step in range(2001):
        _, cost_val, W_val, b_val = sess.run([train, cost, W, b])

        if step % 20 == 0:
            print(step, cost_val, W_val, b_val)