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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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