SESaMo provides an extension to Normalizing Flows that enforces symmetries to the output distribution.
Project description
SESaMo: Symmetry-Enforcing Stochastic Modulation for Normalizing Flows
Quick installation
Move into the repository's directory and create a new python environment:
$ cd SESaMo
$ python -m venv .venv
$ source .venv/bin/activate
Install the SESaMo module with pip:
$ pip install -e .
Run experiments
Run experiments with
cd scripts
python train.py -cp configs/<experiment> -cn <model>
Available <experiment>s are:
hubbard
gaussian-mixture
broken-gaussian-mixture
complex-phi4
broken-complex-phi4
broken-scalar-phi4
Available <model>s are:
realnvp
vmonf
canonicalization
sesamo
The checkpoint, tensorboard, config and stats files are stored in the SESaMo/scripts/runs folder.
After training is completed or interupted the distribution is plotted and saved as SESaMo/scripts/runs/.../samples.png
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