TensorFlow 1.1+中的Keras和tf.keras有什么区别? [英] What is the difference between Keras and tf.keras in TensorFlow 1.1+?

问题描述

现在TensorFlow 1.1在tf.contrib.keras下支持Keras API，如果我打算将Keras与TF后端一起使用，应该使用哪一个?

Now that TensorFlow 1.1 supports the Keras API under tf.contrib.keras, which one should I use if I intend to use Keras with a TF backend?

tf.contrib.keras版本是否与常规Keras发行版有所不同? (想到内部数据结构的TF特定优化).如果我同时使用Keras和TensorFlow Core，是否有任何好处?

Is the tf.contrib.keras version different in any way than a regular Keras distribution? (TF specific optimizations of internal data structures come to mind). Is there any benefit in terms of using Keras and TensorFlow Core together if I use one or the other?

还是tf.contrib.keras只是与Keras相同的代码库的副本，但是在不同的命名空间下?

Or is tf.contrib.keras simply a copy of the same codebase as Keras but under a different namespace?

推荐答案

如果将有两个github存储库，您将如何同步拉 对tf.keras和此存储库的请求?会不会有人 将一个存储库中的更改应用于另一个存储库?

If there will be two github repositories, how would you sync pull requests to tf.keras and this repository? Will there be someone applying changes in one repositority to another?

代码库将有所不同，因此无需复制 拉取请求.对于API更改，您需要将PR发送到API规范 本身，对API规范的更改将复制到所有 代码库.

The codebases will be different, so there will be no need to replicate pull requests. For API changes, you would send a PR to the API spec itself, and changes to the API spec would be replicated across all codebases.

https://github.com/fchollet/keras/issues/5299 #issuecomment-278166426

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