Yeah…Interesting project. In a lot of ways fastai
also adopts the keras
and sklearn API of fit
, predict
. But sticks with the PyTorch way of DataLoaders and Transformers. One key difference is, fastai
adds functionality to PyTorch (lr_find, cycles etc), whereas this repo, provides a wrapper to make it easier with fewer lines of code. But definitely great to see so many projects inspired by Scikit-learn. Infact, Keras has a scikit-learn wrapper - https://keras.io/scikit-learn-api/
One thing missing in fastai is GridSearch of Model Parameters that this wrapper and keras-scikit-learn has, would be good to have a function also available in fastai
.
That would be an easy addition I think, if anyone feels like having a go at it…
One thing missing in fastai is GridSearch of Model Parameters that this wrapper and keras-scikit-learn has, would be good to have a function also available in fastai.
That would be an easy addition I think, if anyone feels like having a go at it…
I wonder if it’s worth adding a scikit-learn wrapper for fastai?
I did this walkthrough on a parameter tuning / stacking tool called Xcessiv yesterday and thought it was really cool. I feel its organized visual interface would save me time down the road and help eke out better results.
The only catch is you need to give it a scikit-learn compatible wrapper to your model, which I don’t think fastai has yet. So, I added an item to the “fastai features wishlist”
I suppose it’s possible that people don’t do much parameter tuning for deep learning due to the time needed for training, and so stacking/tuning tools aren’t considered critical processes. But, we always do some, and any amount of this in my opinion is tricky to keep track of. I like the way Xcessiv handles this visually.