Have you tried saving / restoring via the keras API? Is that sufficient for your needs? It's quite a bit simpler, if less flexible.
Sometimes I still come across people who tell me: "TensorFlow is difficult to use". If that's what you think, you're living in the past! It's not 2016-2017 anymore, and the TensorFlow user experience is really nice now. Brush up or get started: https://www.tensorflow.org/tutorials/
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I really, really enjoyed the paradigm shift that early TensorFlow offered -- it was somewhat weird to read up on the changes when I taught a small workshop on it in the spring.
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i think that the main reason is that people are not used to the way of thinking and how you should go about tackling a problem using machine learning techniques. I see it quite often when people talk to me about it.
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Do you have any easy tutorial for Deploying TensorFlow model in production ?
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I don't think the issue is about how easy
@TensorFlow is to use. The code itself is quite high-level and there's a lot of features that facilitate its use. The issue is that it is concept-heavy and understanding what is happening underneath the API is the challenging part. -
Agree, and as a not-unrelated issue it’s not always at the desired level of abstraction for any given problem. Which motivates higher-level paradigm-specific wrapper APIs like Keras and GPFlow
End of conversation
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I guess people mean different things by TensorFlow. It makes a difference if you talk about high level, or low level API. High level is easy, but there's many non-intuitive things about the low level one. Plus, it is rapidly changing, so there's lots of outdated resources.
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As someone who learned it this year, I agree :-) I felt empowered. (But there is still room for improvement.)
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