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

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?

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 :-).

From: stackoverflow.com/q/35164529