This is the code to reproduce Figure 5 and 6 of "The double descent risk curve" blog post on Medium.
This script recovers the double descent curve using random projections plus the RidgeClassifier from scikit-learn.
It is possible to choose between a synthetic optical processing unit (OPU) and the real OPU.
To request access to our cloud and try our optics-based hardware, contact us: https://www.lighton.ai/contact-us/
python ddc_ridgeclassifier.py # to use synthetic opu on mnist
python ddc_ridgeclassifier.py -dataset 'cifar10' # to use synthetic opu on cifar10
python ddc_ridgeclassifier.py -is_real_opu True # to use opu on mnist with threshold encoder
python ddc_ridgeclassifier.py -is_real_opu True -encoding_method 'autoencoder' # to use opu on mnist with autoencoder
python ddc_ridgeclassifier.py -is_real_opu True -dataset 'cifar10' # to use opu on cifar10 with threshold encoder
python ddc_ridgeclassifier.py -is_real_opu True -encoding_method 'autoencoder' -dataset 'cifaro10'# to use opu on cifar10 with autoencoder
Running ddc_ridgeclassifier.py outputs a .pkl file. To plot the results using this file look at the plot.ipynb example.