paxjaxlib 0.1.1

A simple neural network implementation using JAX

paxjaxlib

A simple and functional neural network library built on JAX.

---

paxjaxlib is a lightweight and modular deep learning library that embraces JAX's functional programming paradigm. Just a simple, yet powerful, API for building and training neural networks.

Installation

You can install paxjaxlib directly from PyPI:

pip install paxjaxlib

Or, for development, clone the repository and install in editable mode:

git clone https://github.com/paxamans/paxjaxlib.git
cd paxjaxlib
pip install -e .[dev]

Quick Start

Here's a quick example of how to define, train, and evaluate a simple NeuralNetwork on dummy data.

import jax
import jax.numpy as jnp
import optax
from paxjaxlib.layers import Dense
from paxjaxlib.models import NeuralNetwork
from paxjaxlib.training import Trainer

# 1. Generate dummy data
key = jax.random.PRNGKey(0)
X = jax.random.normal(key, (100, 10))
y = jax.random.normal(key, (100, 1))

# 2. Define the model
# The model is a pytree, so its parameters can be manipulated functionally.
model = NeuralNetwork([
    Dense(10, 64),
    jax.nn.relu,
    Dense(64, 1)
])

# 3. Initialize the trainer with an Optax optimizer
# The trainer manages the training loop and state.
trainer = Trainer(model, optimizer=optax.adam(1e-3))

# 4. Train the model
# The `train` method returns a history of metrics.
history = trainer.train(X, y, epochs=10, batch_size=32)
print("Training loss:", history['loss'][-1])

# 5. Make predictions
# The trained model is available at `trainer.model`.
predictions = trainer.model(X)

Architecture

The core of paxjaxlib is the paxjaxlib.core.Module, which serves as the base for all layers and models. By inheriting from Module, classes are automatically registered as JAX pytrees.

• Models are data: A NeuralNetwork or Dense layer is a simple data structure. All parameters are stored as attributes.
• Stateless updates: Training updates produce a new, updated model object instead of modifying an existing one in place.
• Gradients with respect to models: You can compute gradients directly with respect to the entire model object, for example: grads = jax.grad(loss_fn)(model, X, y).

Wiki & Documentation

Coming soon

License

This project is licensed under the GNU GENERAL PUBLIC LICENSE.