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https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo...

> Eager execution is an experimental interface to TensorFlow that provides an imperative programming style (à la NumPy). When you enable eager execution, TensorFlow operations execute immediately; you do not execute a pre-constructed graph with Session.run().

This sounds like it would make mucking about with TensorFlow in the REPL far easier.

And debuging. Usually with tensorflow project I found my self at debuging hell where I really dont have any clue where the buggy line is.

This is great! No more constant `eval` spam :)

Eager execution is an experimental interface to TensorFlow that provides an imperative programming style (à la NumPy). When you enable eager execution, TensorFlow operations execute immediately; you do not execute a pre-constructed graph with Session.run().

https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo...

I definitely recommend reading the documentation for that info: https://github.com/tensorflow/tensorflow/tree/r1.5/tensorflo...

It's actually pretty clear (to anyone that has used TensorFlow)

Here’s the announcement from October, with examples and links to more documentation. It feels like a response to PyTorch.

https://research.googleblog.com/2017/10/eager-execution-impe...