This is a PyTorch implementation of the Bayesian Structure Adaptation for Continual Learning.

• Keras 2.2.5
• PyTorch 1.3.1
• torchvision 0.4.2
• scipy 1.3.1
• sklearn 0.21.3
• numpy 1.17.2
• matplotlib 3.1.1
• gzip, pickle, tarfile, urllib, PIL, math, copy
• A cuda enabled 12 GB GPU (GeForce GTX 1080 Ti) / Equivalent Devices / Google Colab

To run all experiments together (might take a while) use : sh run_all.sh

Individual experiments can be run using pyhton3 experiment_name.py

• Experiment names starts with : npbcl_xxx.py
• Copy and save the stored experiment models using : echo -e "saves\ncache/destination" | python3 save.py
• Copy and save the generative experiment images : echo -e "Gens\ncache/destination" | python3 save.py

• Create a new notebook on colab and clone this repo : ! git clone https://github.com/npbcl/NPBCL.git
• Change working directory to icml20 folder : os.chdir('NPBCL')
• Run all experiments : sh run_all.sh

If you ran all experiments using sh file. You can see all experiment results in cache folder. After individual experiment the results are stored in saves folder and Gens folder.