Linear Regression Ex1

H(x) = wx + b 의 Linear Regression example code


import tensorflow as tf

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

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))

-Reference-

https://github.com/hunkim/DeepLearningZeroToAll/