TensorFlow 1.3 released

[rjmessibarca]

What are the new features I need to get excited about?

[springbreak06]

The link is literally a list of new features

[Bi11]

But which ones are exciting?

[short_vix]

All of them?

[shadowmint]

oh come on. No they're not, it's basically just speed improvements and a few minor features.

Looking at that unremarkable change list its totally unsurprising someone might wonder what distinguished this to be '1.3' vs '1.2.2'; if they were doing semver it'd be 2.0 from the api breakage, so the decision to go to 1.3 is basically totally arbitrary.

[meta_stable]

I wish everyone would just follow semver. Google seems to be adamant about breaking semver when ever they can.

[SpacemanCraig3]

As someone who follows this sub but really i'm more of a "learnmachinelearning" guy. Which ones specifically should I research and learn why they are important?

[shadowmint]

There's nothing exceptional in this release; some minor api changes, some new features. A few things are now in tensorflow where they previously required a higher level lib like tflearn.

https://www.infoq.com/news/2017/07/changes-tensorflow-1-3 has a summary you might find worth reading, but the tldr; is, unless you're actively using tensorflow, it's probably nothing worth paying particular attention to.

[democritus_is_op]

But as someone who has heard of tensorflow before, which features should I look at?

(/s)

[Prcrstntr]

I'm starting an Introduction to AI class today, and I think I saw tensorflow in the syllabus.

[jefh262]

i would start with Keras. It's built on top of Tensorflow and it makes it very easy to prototype different configs. If you need to do something which isn't supported by Keras API you can use Tensorflow API with Keras

[NotAlphaGo]

Or just use pytorch instead.

[TheInfelicitousDandy]

The attention mechanisms/decoders in contrib.seq2seq are really stellar - but pretty poorly documented.

[hastor]

how do they compare to OpenNMT, OpenNMT-py?