Comments (1)
I have created the feature/bayesianDL
branch for this purpose. I think that the acquisition functions should be implemented in the modAL.bayesianDL
module.
I am not sure that we can implement these functions in a backend-agnostic way. If not, I propose to implement two versions for each acquisition function. Related to this, I have been thinking about the future of modAL a lot and I think it would be a good direction to rebuild modAL like the early Keras, where you could specify the backend (Theano, TensorFlow or CNTK), but the frontend interface was the same. With this architecture, the required sklearn API for the estimator can be dropped, which would be pretty awesome.
One more thing. Unfortunately, my Ubuntu development setup has just given up, so I need to set up my computer again. Since I have just moved to a new country and started a new job, I might not have time to do it in the following few days. In any case, I'll try to do it as soon as I can!
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Related Issues (20)
- keras image classification model using AL
- Error
- Multivariate Active regression
- How to extract the image names and labels in the training set after completing the active learning loop and write them to a CSV file
- decision_function instead of predict_proba HOT 5
- AttributeError: bootstrap_init HOT 3
- TypeError: cannot concatenate object of type '<class 'numpy.ndarray'>'; only Series and DataFrame objs are valid
- Can I use modAL with estimators from other libraries than scikit-learn like xgboost? HOT 1
- Which sampling method is best for very unbalanced data? HOT 1
- Encountering error with number of batches per epoch
- mmdetection integration with modAL
- Adding active learning regression implementations based on greedy sampling HOT 2
- modAL not installable via pypi anymore HOT 3
- the modAL package has been changed into modal in the pip repository HOT 7
- Data augmentation with `skorch`
- QBC approach for multi-class classification
- Suggestion on how to improve acquisition.UCB for active GP example HOT 1
- QBC stratified bootstrapping HOT 1
- Use modAL on BERT models HOT 1
- Spacy NER HOT 1
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