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> 6) Its development is extremely rapid, new releases every couple of months with a lot of improvements and new features every time. How stable is the api then
by digitalzombie 9y ago
> 6) Its development is extremely rapid, new releases every couple of months with a lot of improvements and new features every time.
How stable is the api then?
I think google is a bit notorious for this (e.g. Angular vs Angular 2).
- deleted 9y ago[deleted]
- fnl 9y agoI am also interested in that - People seemingly are complaining a lot about broken RNN(Cell) functionality/insufficient regression testing. https://news.ycombinator.com/item?id=14576912 https://news.ycombinator.com/item?id=14576912
- sirfz 9y agoAll changes are very well documented for each release [1]. The biggest changes I've seen were, as expected, upgrading from 0.x to 1.x which were mostly function attribute names changes that were made to comply with numpy's attribute names (a welcome change). They did provide tools that automatically convert 0.x code to 1.x so that was helpful. It is not necessary to upgrade if you're satisfied with the current version and I sure won't deploy a different version to production than training. Upgrading is mainly worth it if there are performance improvements or new features/tools that make a big impact (and those are the changes we're mostly interested in and look forward to every release). [1] https://github.com/tensorflow/tensorflow/releases https://github.com/tensorflow/tensorflow/releases