I’m in second year of my PhD in a environmental-science laboratory and there aren’t many experts on machine learning here.
In one part of my research I should use a multilayer perceptron to build a model from a series of inputs that I determined before. So for this part I suggested to use a deep learning framework, specifically Keras with Tensorflow or CNTK, but in the lab my supervisors told me that for a real application they don’t want to depend of a framework from Google or Microsoft and they prefer to develop they own tools that won’t change in the time if there’s for example an upgrade or something like that. One researcher created a neutral net and he obtained good results, but when we are interested in make some fine tuning, his code is more complex to update or to test new things like an dropout for example.
I started to work with those frameworks having in mind the reproducibility because I only have to use the functions from Keras and define the parameters. I think other researchers who want to test/use my model should use the configuration I suggest and don’t try to understand the code I provide. So I obtained some good results and am now in the phase of fine tuning, but the work of the other researcher has a slightly better results but after a regularisation step that I didn’t try yet.
So this researcher told me that I should abandon my work with Keras and start using his code. He also told me that Keras is just some fancy tool and in research is not required to have a very precise model, just good results and use the code that works.
So my question is this: Is it right what this researcher told me? Is Keras actually used in research? If there are some frameworks that are developed, why not use them instead of rediscovering the wheel, am I right?