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Let me tell you about my favorite upcoming feature of TensorFlow/Keras 2.7: a better debugging experience. We've worked on 3 things that significantly cut the time it takes to analyze and fix issues in your Keras code.pic.twitter.com/9BmrWtJGi5
First of all, we're filtering tracebacks to eliminate TF-internal frames. (There's an option to display them, if you need to debug TF or Keras themselves.) Only frames related to your own code will show up, greatly reducing the amount of info you have to sift through.
Second, we're automatically displaying context information related to Keras layer calls. What were the arguments passed? This is essential, since most errors have to do with the shape or dtype of the input tensors, or with the mask or training arguments, which are often implicit.pic.twitter.com/rl6xBknM1q
Lastly, we've audited every error message in the Keras and TensorFlow codebases (thousands of error locations!) and improved them to make sure they follow UX best practices. https://blog.keras.io/user-experience-design-for-apis.html …pic.twitter.com/9jq8csvBnc
The end result is a much improved debugging experience, already available in tf-nightly, and upcoming in TF/Keras 2.7. Lots of other cool things are coming up, but right now this is what I'm most excited about.
Oh that's so great! I love it
Thank you for your great work. 
That new trace back is choice. The old one won't go to waste. I can tell war stories to the undergrads about "back in my day"
That's awesome! How did you implement that traceback filtering in practice? I've wanted to do something similar in fastai for a while but haven't found a good way to handle it in Python
This would have been virtually impossible before Python 3.7. I implemented it using the TracebackType API, which is a new addition in 3.7. You can check out the code in Keras & TF
much better!
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