python - In TensorFlow is there any way to just initialize uninitialised variables?

Question

The standard way of initializing variables in TensorFlow is

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

After running some learning for a while I create a new set of variables but once I initialize them it resets all my existing variables. At the moment my way around this is to save all the variable I need and then reapply them after the tf.initalize_all_variables call. This works but is a bit ugly and clunky. I cannot find anything like this in the docs...

Does anyone know of any good way to just initialize the uninitialized variables?

Answer 1

There is no elegant* way to enumerate the uninitialized variables in a graph. However, if you have access to the new variable objects—let's call them v_6, v_7, and v_8—you can selectively initialize them using tf.initialize_variables():

init_new_vars_op = tf.initialize_variables([v_6, v_7, v_8])
sess.run(init_new_vars_op)

---

* A process of trial and error could be used to identify the uninitialized variables, as follows:

uninitialized_vars = []
for var in tf.all_variables():
    try:
        sess.run(var)
    except tf.errors.FailedPreconditionError:
        uninitialized_vars.append(var)

init_new_vars_op = tf.initialize_variables(uninitialized_vars)
# ...

...however, I would not condone such behavior :-).