Per the RFC, we need to move external optimizers from contrib to addons:
This will involve inheriting from base Keras optimizer, modifying the code to match those APIs, and modifying test cases to run in TF2.
This is partially related, but it might also be good to have a docstring or notebook somewhere that just showcases the most idiomatic way to minimize a tf.function in 2.0. In particular, now that we have a function-first API, it may make sense to showcase some simple "low-level" scipy.optimize use cases. Obviously this is somewhat orthogonal to implementing a Keras-compliant optimizer, but still useful I think. (and it's not clear where else to report this request).
@facaiy @WindQAQ This is a proposed contrib move that I've been questioning. Its not that I don't find it useful... but to me it seems like it could almost be a separate repository. I think its hard to say that this will fit into a well established API pattern, and it requires us to bring in a new dependancy... but I wanted to get your thoughts.
At least to me, I don't think it's useful. How about let's implement it in the future if requested by enough users?
I find some references in tensorflow probability. TFP has offered some widely used optimization methods like bfgs, lbfgs and differential evolution, which also lies in scipy.optimize. In this case, I do not think we still have to bridge the scipy interface.
https://www.tensorflow.org/probability/api_docs/python/tfp/optimizer
Closing this issue unless there is a convincing argument otherwise or a large request from the community
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I find some references in tensorflow probability. TFP has offered some widely used optimization methods like bfgs, lbfgs and differential evolution, which also lies in scipy.optimize. In this case, I do not think we still have to bridge the scipy interface.
https://www.tensorflow.org/probability/api_docs/python/tfp/optimizer