Release Keras 2.3.0 · Keras-team/Keras | Hacker News Reader

Release Keras 2.3.0 · Keras-team/Keras

github.com

51 pointsAnon8415 comments

Good move. I'd much rather it worked well for one backend then sucked mightily on all of them. Eager mode means that for the first time ever you can _easily_ debug programs using the TensorFlow backend. That will be music to the ears of anyone who's ever tried to debug a complex TF-backed model.

I don't think they have that in yet:

> However note that it does not support most TensorFlow 2.0 features, in particular eager execution.

They don't have that in _this_ standalone version. The one integrated into TF 2.0 itself does support eager execution IIRC.

Oh cool, thought you referred to this specific version.

Since TF included Keras in its codebase, I have been wondering if Keras would be acquired by Google. Is this the reality of the open source world we live in. TF vs Pytorch - Google vs FB. C

Seems indie frameworks in AI can't survive?

At least glad, Julia hasn't been in that way yet.

> Seems indie frameworks in AI can't survive?

AI frameworks are enormously complex pieces of software—a mixed bag of GPU acceleration, math utilities, low and high-level implementations of state-of-the-art components, and a (hopefully, eventually, but not usually) friendly interface to tie it all together.

While Keras is certainly useful, it was only possible because of the underlying libraries (i.e. Theano, TensorFlow, etc.) However, maintaining useful frameworks on a shifting landscape of underlying libraries often results in mismatches which leads to weird incompatibilities, performance regressions, numerical instabilities, etc. Even the big players deal with these issues internal to their own AI frameworks.

Moreover, due to performance and deployment requirements, and the rapid development of the field, these underlying libraries are often tightly coupled or have poorly defined API interfaces for third-parties outside of "model development".

It's not impossible to have well-defined abstractions but "AI" is too new, too varied and too ill-defined itself to have developed those abstractions sufficiently for _large_ indie frameworks to be successful to a broader community. It would be similar to trying to design a high-level framework for "software development". Again, not impossible—Keras did much better than most—but exceedingly difficult for independent developers. Therefore, we're left with only the biggest players.

It's not all bad news, though. There's certainly room for independent development, but outside of hobby projects or thought experiments, I don't think these contributions are effective at the broadest level of scope. The best way for independent developers to contribute and keep their sanity is on implementations of individual components, utilities, or abstractions for specific use cases which don't try to be everything to everyone.

Keras developer works for Google.

the original guy that wrote keras now works for google.

https://www.linkedin.com/in/fchollet/

last I checked, there's also amazon/microsoft pushing MX.net (which seems mature and actually seems to have tooling which is not presuming the user is Google-internal)

MX.net?

Microsoft is pushing ML.NET, which builds on top of DirectML and interoperates with TensorFlow.

No idea about Amazon.

Keras still has CNTK and Theano backends in addition to Tensorflow. Given that both frameworks are not being developed anymore, is there any point in maintaining those backends in the future?

From the release announcement:

> Development will focus on tf.keras going forward. We will keep maintaining multi-backend Keras over the next 6 months, but we will only be merging bug fixes.

So it's the end of Keras

and I thought there was discussion about including mx.net. Looks like if you want to build, train and reuse NNs (in an established framework) you finally have to choose between Google, Facebook or Amazon/Microsoft in every case.

There is support for Keras with MXNet[1], however you need to install a forked version of keras, `pip install keras-mxnet`.

[1] https://github.com/awslabs/keras-apache-mxnet

I guess it's going to be just TensorFlow in the near future. From the release notes:

> This is also the last major release of multi-backend Keras. Going forward, we recommend that users consider switching their Keras code to tf.keras in TensorFlow 2.0.

Reply on news.ycombinator.com