
print(__doc__)
import tensorflow as tf
import numpy as np
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1+0.3

"""try to find values for W and b that compute
y=W*x_data+b we know that W should be 0.1 and b = 0.3, but
the tensorflow will figure that out for us"""
W=tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b=tf.Variable(tf.zeros([1]))
y = W*x_data+b
loss = tf.reduce_mean(tf.square((y-y_data)))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for step in range(501):
    sess.run(train)
    if step%20 == 0:
        print(step,sess.run(W),sess.run(b))
