How to quantize the values of tf.Variables in Tensorflow

I have a training model like

Y = w * X + b

where Y and X are output and input placeholder, w and b are the vectors
I already know the value of w can only be 0 or 1, while b is still tf.float32.

How could I quantize the range of variable w when I define it?
or
Can I have two different learning rates? The rate for w is 1 or -1 and the rate for b is 0.0001 as usual.

Solution

There is no way to limit your variable during the activation. But what you can do is to limit it after each iteration. Here is one way to do this with tf.where():

import tensorflow as tf

a = tf.random_uniform(shape=(3, 3))

b = tf.where(
    tf.less(a, tf.zeros_like(a) + 0.5),
    tf.zeros_like(a),
    tf.ones_like(a)
)

with tf.Session() as sess:
    A, B = sess.run([a, b])
    print A, '\n'
    print B

Which will convert everything above 0.5 to 1 and everything else to 0:

[[ 0.2068541   0.12682056  0.73839438]
 [ 0.00512838  0.43465161  0.98486936]
 [ 0.32126224  0.29998791  0.31065524]] 

[[ 0.  0.  1.]
 [ 0.  0.  1.]
 [ 0.  0.  0.]]