Approximating stochastic functions

A generic approach for training probabilistic machine learning models

Start by making making a local clone of the repo.

git clone https://github.com/narroyo1/sffnn.git

Create the environment.

conda env create -f environment.yml

Then activate it.

conda activate sffn

Open main.py and run.

On the same directory as main.py run:

tensorboard --logdir=runs

Then open http://localhost:6006/ on any browser.

The default experiment is the first one shown in the article, to reproduce any other change the following line on main.py

experiment = experiments.EXPERIMENT_<experiment number>

A paper throughly explaining the method is located at docs/sffnn.md.

Name		Name	Last commit message	Last commit date
Latest commit History 67 Commits
datasets		datasets
docs		docs
LICENSE		LICENSE
README.md		README.md
datasets.py		datasets.py
emd_test.py		emd_test.py
environment.yml		environment.yml
experiments.py		experiments.py
functions.py		functions.py
goal1_test.py		goal1_test.py
goal2_test.py		goal2_test.py
main.py		main.py
model.py		model.py
plotter.py		plotter.py
tester.py		tester.py
trainer.py		trainer.py
utils.py		utils.py
writer.py		writer.py
zsamples.py		zsamples.py