I am new to edward and checking the tutorial about linear mixed effects (jupyter notebook can be found here) trying to understand the code. This question might be more related to me not understanding something ternsorflow though.
the code in the link does:
sigma_s = tf.sqrt(tf.exp(tf.get_variable("sigma_s", [])))
sigma_d = tf.sqrt(tf.exp(tf.get_variable("sigma_d", [])))
sigma_dept = tf.sqrt(tf.exp(tf.get_variable("sigma_dept", [])))
I would guess what happens here is we define sigma_xxx to be the sigma square,a and then needs to do tf.sqrt to get the sigma. However, I really do not understand this tf.exp... What is it doing here and what is raised to which exponent???