Introducing Keras 2
blog.keras.io
Introducing Keras 2
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Re: Introducing Keras 2
#2Copying my rare product endorsement from the previous submission:
Keras is so good that it is effectively cheating in machine learning, where even Tensorflow tutorials can be replaced with a single line of code. (which is important for iteration; Keras layers are effectively Lego blocks). A simple read of the Keras examples (https://github.com/fchollet/keras/tree/master/examples) and documentation (https://keras.io/getting-started/functional-api-guide/) will let you reverse-engineer most the revolutionary Deep Learning clickbait thought pieces.
It's good to see that backward compatability is a priority in 2.0, since it sounds like a lot had changed.
Re: Introducing Keras 2
#3Highly recommend this course - http://course.fast.ai/ that utilizes Keras as the main programming tool
Re: Introducing Keras 2
#4I wonder why they decided to get rid of MaxoutDense. Is there something better or is it so trivial to implement they decided to drop it?
Re: Introducing Keras 2
#5Will Keras2 support PyTorch as backend, in the future?
Answer: [0] No, there are no plans to support PyTorch. There is nothing to be gained in supporting every novelty framework that crops up every quarter. Our goal is to make deep learning accessible and useful to as many people as possible, and that goal is completely opposite to building up deep learning hipster cred.
Re: Introducing Keras 2
#6Highly recommend this course - http://course.fast.ai/ that utilizes Keras as the main programming tool
I'm definitely going to give this a shot, thanks for the link. Approaching ML at a higher level is exactly what I need to develop a better interest in it. I realize that underpinnings are important, but waiting 30 minutes for mnist on to process on my localhost is just unbearably boring.
Re: Introducing Keras 2
#7Will Keras2 support PyTorch as backend, in the future? Answer: [0] No, there are no plans to support PyTorch. There is nothing to be gained in supporting every novelty framework that crops up every quarter. Our goal is to make deep learning accessible and useful to as many people as possible, and that goal is completely opposite to building up deep learning hipster cred. [0]: https://github.com/fchollet/keras/issues/…
To put this quote in context: this isn't specifically about PyTorch. Every couple of months since mid-2015, a new deep learning framework gets released. In the following week, someone inevitably asks "will X get added as a Keras backend?".
Supporting several backends is a strong positive. But chasing every new framework as a backend is a quick way to kill Keras, via bloat, support issues and general technical debt. We should only support a backend that is considered mature, and we should stay away from the hype surrounding the release of every new framework. There will be another hyped up framework next quarter anyway. And the one after.
It is in fact possible that Keras will eventually support PyTorch. But if it ever happens, it would be at least 1-2 years in the future. When PyTorch becomes "uncool", just like Keras :)
Re: Introducing Keras 2
#8The mathematician in me has kept me from jumping into deep learning before I understand the mathematical and statistical underpinnings of the algorithms involved. Looking forward to reading through the latest book out by mit press and giving things a whirl with Keras which I've heard so much about.
Re: Introducing Keras 2
#9Awesome.
Yet "codebases written in Keras 2 next month should still run many years from now" given that deep learning is no new, how can they that confident that this API will remain relevant years down the line?
Re: Introducing Keras 2
#10Will Keras2 support PyTorch as backend, in the future? Answer: [0] No, there are no plans to support PyTorch. There is nothing to be gained in supporting every novelty framework that crops up every quarter. Our goal is to make deep learning accessible and useful to as many people as possible, and that goal is completely opposite to building up deep learning hipster cred. [0]: https://github.com/fchollet/keras/issues/…
To put this quote in context: this isn't specifically about PyTorch. Every couple of months since mid-2015, a new deep learning framework gets released. In the following week, someone inevitably asks "will X get added as a Keras backend?". Supporting several backends is a strong positive. But chasing every new framework as a backend is a quick way to kill Keras, via bloat, support issues and general technical debt. W…
Yay, making deep learning uncool again! ;) (http://www.fast.ai/about/)
But seriously - does it even make use to have a "define by run" dynamic framework as a backend? It seems to me that keras is particularly suited to wrapping frameworks that define and run a computation graph.