Glad to hear :) We're now working hard on extensions ranging from hyperparameter tuning, autoML, preprocessing layers, easier distribution, easier deployment, and more. Work is never over.https://twitter.com/A_K_Nain/status/1144615884855500800 …
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Thanks François for your great contribution in
#deeplearning domain. Do you have any idea of supporting#Tf2 in#Keras. I've know that tf.keras may be better option and I'm utilizing it in my projects and packages, but some of other package is only written in pure Keras.
Multi-backend Keras will support TF2, in fact you can already check out the branch `tf-2` which does just that. However if you want to use TF2 features I would strongly recommend switching to tf.keras.
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tf.keras implements the same Keras API you already know. If your workflows are TF-based, you should use it.
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