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A comment on Eager Execution: An imperative, define-by-run interface to TensorFlow. Parent comment

Re:Eager Execution: An imperative, define-by-run interface t... by brittohalloran on Tuesday October 31, 2017 @09:00PM (#15598147)
What are the strengths and weaknesses of each? I've been using keras but planning on diving into a real deal framework next. Tensorflow is appealing for the momentum it has in the community, but pytorch looks easier to learn.

Doing image classification, object localization, and homography (given an input image, which of my known template images is matches it and in what orientation).

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Re:Eager Execution: An imperative, define-by-run interface t... by alextp on Tuesday October 31, 2017 @10:22PM (#15598439)
I think Keras is a real deal framework. It provides a higher-level API than most other frameworks, but it has pretty sweet portability of models across frameworks and platforms and most research papers are implementable in Keras without too much trouble.
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Re:Eager Execution: An imperative, define-by-run interface t... by Narew on Friday November 03, 2017 @05:05AM (#15617684)
In my opinion, the real deal with Pytorch or Chainer, there are similar than numpy API. So the learning curve is flat. The NN construction part and gradiant part are specific but all the glue is regular python unlike Keras, tensorflow ...
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