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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.
We need more of this kind of work in the software world.
I love this, good actionable error messages are truly a gift to every developer.
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