tenssor flow: linear regression

linear_regression_tf.py

import tensorflow as tf

x1_data = [1, 0, 3, 0, 5]
x2_data = [0, 2, 0, 4, 0]
y_data = [1, 2, 3, 4, 5]


W1 = tf.Variable(tf.random_uniform([1], -1.0, 1.0) )
W2 = tf.Variable(tf.random_uniform([1], -1.0, 1.0) )
b = tf.Variable(tf.random_uniform([1], -1.0, 1.0) )


hypothesis = W1*x1_data + W2*x2_data + b 


# Cost function
cost = tf.reduce_mean(tf.square(y_data - hypothesis))

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

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

for step in range(100):
    sess.run(train)
    if step % 10 == 0:
        print (step, sess.run(cost), sess.run(W1), sess.run(W2), sess.run(b))