A small experimental neural network library where neurons are represented as matrices.
Project description
MATNETS
MATNETS is a JAX library for matrix-neuron neural network experiments.
In a traditional neural network, a neuron carries one scalar activation. In
MATNETS, each neuron carries an n x n matrix. A layer maps a stack of input
matrix-neurons to a stack of output matrix-neurons.
Core Shape Contract
The core dense primitive uses:
params.W: (q, p, n, n)
params.B: (q, n, n)
x: (p, n, n)
output: (q, n, n)
p is the input neuron count. q is the output neuron count. n is the matrix
size for every neuron.
Read Next
- Getting Started: install MATNETS and run a first dense layer.
- Concepts: understand matrix-neuron shapes and JAX transforms.
- API Guide: see each public function and its expected shapes.
- Examples: run the included examples.
- Development: run tests and local checks.
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