tensorflow - TensorFlow Eager 模块有什么用？

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Github 链接到 tensorflow 中新引入的 Eager 模块:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/c/eager
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/eager

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根据 article由 Google 前实习生撰写，解释 tensorflow eager mode .

基本上这是 tensorflow 中的一种模式，允许编写命令式编码风格，例如 numpy .所以应该没有明确的graph , session , session.run()了。当代码像 Chainer 那样运行时，图形是隐式构建的。/PyTorch .

它仍在积极开发中，性能仍然不如预期。您可以通过 pip 上的每晚构建进行尝试同时期待一些有趣的新功能，如 graph function ，它允许将子图作为函数调用。随着框架的发展，这应该是 tensorflow 的默认模式。 .

更新: Tensorflow团队已正式talked about it .

Eager execution is an imperative, define-by-run interface where operations are executed immediately as they are called from Python. This makes it easier to get started with TensorFlow, and can make research and development more intuitive.

A vast majority of the TensorFlow API remains the same whether eager execution is enabled or not. As a result, the exact same code that constructs TensorFlow graphs (e.g. using the layers API) can be executed imperatively by using eager execution. Conversely, most models written with Eager enabled can be converted to a graph that can be further optimized and/or extracted for deployment in production without changing code.

You can read out more about it in the blog post or the README. This is still a preview release, so you may hit some rough edges.

Looking forward to your feedback as you try it out.

更新:渴望模式现已正式发布于TF 1.7 .有一个不错的tutorial在他们的网站上。我想现在每个人都知道它是什么，所有主要框架都在融合，动态神经网络。

更新: Eager 模式将是 TF 2.0 中的默认模式，下一个主要版本 planned for release 2018 年晚些时候。