4Ward - Make your Graph a Feedforward Neural Network

With 4Ward, directed acyclic graphs (DAGs) characterized by complex topologies can be easily transformed into feedforward neural networks deployable as PyTorch Modules.

Get Started

Install 4Ward by typing:

pip install git+https://github.com/BoCtrl-C/forward.git

The main 4Ward class can be imported through:

from forward.models import ForWard

Finally, deploy your new PyTorch Module wherever you want! For instance:

from torch.nn import Sequential


dag = ...

ffnn = ForWard(dag)

model = Sequential(
    ...,
    ffnn,
    ...
)

Examples

A runnable Jupyter Notebook that makes use of 4Ward to classify MNIST images can be found inside the examples directory.

Citation

@misc{https://doi.org/10.48550/arxiv.2209.02037,
  doi = {10.48550/ARXIV.2209.02037},
  url = {https://arxiv.org/abs/2209.02037},
  author = {Boccato, Tommaso and Ferrante, Matteo and Duggento, Andrea and Toschi, Nicola},
  keywords = {Neural and Evolutionary Computing (cs.NE), Disordered Systems and Neural Networks (cond-mat.dis-nn), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences},
  title = {4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}