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Use TF Numpy instead of NumSharp #658
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Impressive |
For the rest of the world: |
It's good to be in one framework, and it should be more comfortable to develop deep learning code. |
This approach is supported. Constantly between modules in order to glue is enough annoying. Another implementation of numpy is not out of the way. It is recommended to act as a module alone. |
NumSharp has issues to be solved. Leveraging TF Numpy could reduce unexpected behaviors. |
I agree with @Jiuyong, perhaps @Oceania2018, @henon we do need to discuss further about creating an abstract layer to numpy that'll be shared between NumSharp, Numpy.NET and potentially as a standalone module/nuget-package/project for Tensorflow.NET's numpy that will also use this interface. |
an interface in an independen package would work in theory. in practice, we are looking at ~500 api functions. that would be a huge effort of standardization between the three libs. that would be a year's work for an intern |
@henon @Oceania2018 After chatting with both of you privately, I decided to take lead on this project (Tensorflow Numpy aka tnp) and maintain it in the future.
In a nutshell, tnp will be a mini-NumSharp powered by Tensorflow. Edit: changes based on change requests below |
@Nucs Appreciate for your continuous contribution. Several things come out: |
I think that TNDArray (equivalent of tf.experimental.numpy.ndarray) is just a 'shell' to pipe calls onto Tensor methods. |
hi, |
@simonbuehler Check out update: https://twitter.com/ScisharpStack/status/1415652428830154752 |
Several benefits of using TF Numpy
@dsyme @henon @Nucs Please advise.
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