This somehow also reminds me of the `__array_module__` (NEP37) protocol. I'm not sure if TF would ever implement it, but it would be really nice if the NEP37 proposal would move forward and libraries would implement it.
On Mon, Jun 1, 2020 at 8:22 PM Iordanis Fostiropoulos < danny.fostiropou...@gmail.com> wrote: > In regard to Feature Request: https://github.com/numpy/numpy/issues/16469 > > It was suggested to sent to the mailing list. I think I can make a strong > point as to why the support for this naming convention would make sense. > Such as it would follow other frameworks that often work alongside numpy > such as tensorflow. For backward compatibility, it can simply be an alias > to np.concatenate > > I often convert portions of code from tf to np, it is as simple as > changing the base module from tf to np. e.g. np.expand_dims -> > tf.expand_dims. This is done either in debugging (e.g. converting tf to np > without eager execution to debug portion of the code), or during > prototyping, e.g. develop in numpy and convert in tf. > > I find myself more than at one occasion to getting syntax errors because > of this particular function np.concatenate. It is unnecessarily long. I > imagine there are more people that also run into the same problems. Pandas > uses concat (torch on the other extreme uses simply cat, which I don't > think is as descriptive). > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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