问题描述
在 TensorFlow 2.0 APIs 中有一个模块 tf.experimental.这样的名字也出现在其他地方，比如 tf.data.experimental.我只想知道设计这些模块的动机是什么.
In TensorFlow 2.0 APIs, there is a module tf.experimental. Such a name also appears in other places like tf.data.experimental. I just would like to know what the motivate for designing these modules is.
推荐答案
tf.experimental 表示所述类/方法处于早期开发、不完整或不太常见、未达到-标准.它是用户贡献的集合，尚未与主 TensorFlow 集成，但仍可作为开源的一部分供用户测试和提供反馈.
tf.experimental indicates that the said class/method is in early development, incomplete, or less commonly, not up-to-standards. It's a collection of user contributions which weren't yet integrated w/ main TensorFlow, but are still available as a part of open-source for users to test and give feedback.
不完整"是最常见的，其中可能包括存在错误，或者未通过所需的一组平台或硬件(CPU/GPU)的测试.作为未达到标准"的示例，来自 2017 年 Google Devs tf.xla.experimental 上的 >blog:(更多详情请见 这个答案)
"Incomplete" is the most common, which can include having bugs, or not passing tests across a required set of platforms or hardware (CPU/GPU). As an example of not being "up to standards", from a 2017 Google Devs blog on tf.xla.experimental: (more details in this answer)
XLA 仍应视为实验性的，某些基准测试可能会变慢
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