Minimal neural network demonstration built using JAX. This serves as a starting point to creating your own neural network. The network is trained using Mini-batch SGD and is taught to predict the forward integration step of the Lorenz Attractor ODE.

You need a Python interpreter with JAX and matplotlib. Example:

python3 -m venv .venv  # Create a new virtual environment in the '.venv' directory
source .venv/bin/activate  # Activate the virtual environment
pip install jax jaxlib matplotlib  # Install packages using pip

A logical next step would be to make the Python class more portable by working around JAX's limitations using PyTrees: https://jax.readthedocs.io/en/latest/pytrees.html

One could also implement more adaptive Gradient Descent methods like AdaGrad, RMSProp and Adam.

JAX:

@software{jax2018github,
  author = {James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake Vander{P}las and Skye Wanderman-{M}ilne and Qiao Zhang},
  title = {{JAX}: composable transformations of {P}ython+{N}um{P}y programs},
  url = {http://github.com/google/jax},
  version = {0.3.13},
  year = {2018},
}

JAX tutorials, specifically: https://jax.readthedocs.io/en/latest/notebooks/Neural_Network_and_Data_Loading.html