Run sweep.py for all 32 taskids. Results are dumped to lastlayersims folder. To each taskid there are three files associated
- taskid*_args.pt: contains all the arguments used in lastlayerbayesian.py
- taskid*_results.pt: contains generalization error for each method and each sample size
- taskid*.png: preliminary graphic, not publication pretty Run visualize.py for all 32 taskids to produce pretty learning curves and latex tables summarizing learning coefficient and R2 fit.
The random seed was set to 43 in lastlayerbayesian.py.
To commit notebooks, first execute the following to strip all notebooks of output.
python3 -m nbconvert --ClearOutputPreprocessor.enabled=True --inplace .ipynb **/.ipynb
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user jupyter nbextension enable equation-numbering/main
MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: "AMS" } } });