tf.py_function 和 tf.function 在目的上有什么区别? [英] What is the difference in purpose between tf.py_function and tf.function?

问题描述

两者之间的区别在我的脑海中模糊不清，尽管有什么是渴望的，什么不是.据我所知，@tf.function 装饰器有两个好处

The difference between the two is muddled in my head, notwithstanding the nuances of what is eager and what isn't. From what I gather, the @tf.function decorator has two benefits in that

1. 它将函数转换为 TensorFlow 图以提高性能，并且
2. 通过将许多(但不是全部)常见的 Python 操作解释为张量操作，例如if 变成 tf.cond 等

1. it converts functions into TensorFlow graphs for performance, and
2. allows for a more Pythonic style of coding by interpreting many (but not all) common-place Python operations into tensor operations, e.g. if into tf.cond, etc.

从 tf.py_function 的定义来看，它似乎只是上面的 #2.因此，当 tf.function 能够提高启动性能并且没有前者无法序列化时，为什么还要使用 tf.py_function 呢?

From the definition of tf.py_function, it seems that it does just #2 above. Hence, why bother with tf.py_function when tf.function does the job with a performance improvement to boot and without the inability of the former to serialize?

推荐答案

随着改进，它们确实开始彼此相似，因此了解它们的来源很有用.最初，不同之处在于:

They do indeed start to resemble each other as they are improved, so it is useful to see where they come from. Initially, the difference was that:

• @tf.function 将 python 代码转换为一系列 TensorFlow 图节点.
• tf.py_function 将现有的 Python 函数包装到单个图形节点中.

• @tf.function turns python code into a series of TensorFlow graph nodes.
• tf.py_function wraps an existing python function into a single graph node.

这意味着tf.function要求你的代码相对简单，而tf.py_function可以处理任何python代码，无论多么复杂.

This means that tf.function requires your code to be relatively simple while tf.py_function can handle any python code, no matter how complex.

虽然这条线确实很模糊，tf.py_function 做了更多的解释，tf.function 接受了很多复杂的 python 命令，但一般规则保持不变:

While this line is indeed blurring, with tf.py_function doing more interpretation and tf.function accepting lot's of complex python commands, the general rule stays the same:

• 如果您的 Python 代码逻辑相对简单，请使用 tf.function.
• 当您使用复杂的代码时，例如大型外部库(例如连接到数据库或加载大型外部 NLP 包)，请使用 tf.py_function.

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