VortexNet: Neural Computing through Fluid Dynamics
This repository contains toy implementations of the concepts introduced in the research paper VortexNet: Neural Computing through Fluid Dynamics. These examples demonstrate how PDE-based vortex layers and fluid-inspired mechanisms can be integrated into neural architectures, such as autoencoders for different datasets.
Note: These are toy prototypes for educational purposes and are not intended as fully optimized or physically precise fluid solvers.
Contents
vortexnet_mnist.py :
A demonstration script for building and training a VortexNet Autoencoder on the MNIST dataset.
: A demonstration script for building and training a VortexNet Autoencoder on the MNIST dataset. vortexnext_image.py :
An advanced script for building and training a VortexNet Autoencoder on custom image datasets with enhanced features like data augmentation and latent space interpolation.
Getting Started
1. Clone the Repository
git clone https://github.com/samim23/vortexnet.git cd vortexnet
2. Install Dependencies
Ensure you have Python 3.8+ installed. Install the required Python packages using pip :
pip install torch torchvision matplotlib pyyaml scikit-learn seaborn tensorboard
3. Prepare the Data
MNIST Dataset :
The MNIST dataset will be automatically downloaded by vortexnet_mnist.py if not already present.
Custom Image Dataset:
For vortexnext_image.py , place your images (JPEG, PNG, or JPEG formats) inside the my_data/ directory.
4. Run the Scripts
a. VortexNet MNIST Autoencoder ( vortexnet_mnist.py )
This script builds and trains a VortexNet Autoencoder on the MNIST dataset.
Usage:
python3.11 vortexnet_mnist.py
b. VortexNet Image Autoencoder ( vortexnext_image.py )
This advanced script builds and trains a VortexNet Autoencoder on custom image datasets with enhanced features.
Usage:
python3.11 vortexnext_image.py --config config_image.yaml
Notes