在 TensorFlow 中有什么方法可以只初始化未初始化的变量? [英] In TensorFlow is there any way to just initialize uninitialised variables?

问题描述

TensorFlow 中初始化变量的标准方法是

The standard way of initializing variables in TensorFlow is

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

经过一段时间的学习后，我创建了一组新变量，但是一旦我初始化它们，它就会重置我所有现有的变量.目前我解决这个问题的方法是保存我需要的所有变量，然后在 tf.initalize_all_variables 调用后重新应用它们.这有效，但有点丑陋和笨重.我在文档中找不到这样的东西...

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...

有谁知道初始化未初始化变量的任何好方法?

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

推荐答案

没有优雅*的方法来枚举图中未初始化的变量.但是，如果您可以访问新的变量对象—让我们称它们为 v_6、v_7 和 v_8—您可以有选择地使用tf.initialize_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)

<小时>

* 试错过程可用于识别未初始化的变量，如下所示:

---

* 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)
# ...

...但是，我不会容忍这种行为:-).

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

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