renormalization

Requirements
- numpy, pytorch
For overview of code + some plots, check out main.ipynb
Generate Ising model data:
- run python generate_data.py
- Place correlated .npy file in correct temperature directory
Create uncorrelated samples in supervised_convnet/generate_uncorrelated_data.py
- Set data variable to be path of the Ising model data (of each temperature)
- Place uncorrelated .npy file in correct temperature directory
Train neural network to distinguish between correlated/uncorrelated samples in temperature directory e.g. supervised_convnet/t_1/train.py
- Make sure both correlated and uncorrelated .npy file in supervised_convnet/t_1
Neural network architecture in supervised_convnet/supervised_convnet.py
- Also contains IsingDataset class for pytorch data set loading

Name		Name	Last commit message	Last commit date
Latest commit History 144 Commits
adversary		adversary
analysis		analysis
configs		configs
mine		mine
mnist		mnist
openselfsup		openselfsup
supervised_convnet		supervised_convnet
tensornetworks		tensornetworks
tools		tools
.gitignore		.gitignore
Ising.ipynb.txt		Ising.ipynb.txt
README.md		README.md
__init__.py		__init__.py
della_jupyter_start.sh		della_jupyter_start.sh
environment.yml		environment.yml
generate_data.py		generate_data.py
generate_data_kiel.py		generate_data_kiel.py
generate_uncorrelated_matrices.ipynb		generate_uncorrelated_matrices.ipynb
jupyter_start.sh		jupyter_start.sh
main.ipynb		main.ipynb
setup.py		setup.py

anhhuyalex/renormalization