
TensorFlow Feature Columns - mrry
https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html
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minimaxir
For context, usually you need to use something like scikit-learn for feature
engineering; looks like Google want to manage some of that in-house.

There is also some overlap with Keras’s preprocessing tools (now native to
TensorFlow), so there’s some nice synergy.

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nicolewhite
I'm not sure I'd choose the word synergy. Every time they move something from
contrib into the main package, it seems to create a lot of redundancy. Now we
have tf.keras.utils.to_categorical and
tf.feature_column.categorical_column_with_vocabulary_list.

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olalonde
I also don't quite understand how Keras is integrated, it looks like a
somewhat outdated copy/paste of files from the Keras repo.

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fchollet
To clarify: `tf.keras` is an implementation of the entire Keras API written
from the ground-up in pure TensorFlow. The first benefit of that is a greater
level of blending between non-Keras-TF workflows and TF-Keras workflows: for
instance, layers from `tf.layers` and `tf.keras` are interchangeable in all
use cases.

Additionally, this enables us to add TensorFlow-specific features that would
be difficult to add to the multi-backend version of Keras, and to do
performance optimizations that would otherwise be impossible.

Such features include support for Eager mode (dynamic graphs), support for
TensorFlow Estimators, which enables distributed training and training on
TPUs, and more to come.

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mark_l_watson
Thanks for clarifying that. BTW, I love your new book. I bought the MEAP last
month and I really enjoyed it. Looking forward to the final version.

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anentropic
What is the book? It sounds like something I might want to read!

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anentropic
I believe it must be [https://www.manning.com/books/deep-learning-with-
python](https://www.manning.com/books/deep-learning-with-python)

