axon

Introduction to Axon: Neural Networks for Elixir

Axon is a powerful library designed to facilitate the development and training of neural networks within the Elixir programming ecosystem. Harnessing Elixir's Nx numerical computing library, Axon is ideal for creating efficient and highly performant deep learning models. The following sections provide an overview of Axon's key components and functionalities, making the sophisticated world of machine learning accessible to Elixir developers.

Key Components of Axon

Functional API

At its core, Axon offers a Functional API dedicated to the foundational operations in deep learning. This API includes:

• Activations: Functions for element-wise activation tasks crucial for introducing non-linearities.
• Initializers: Methods to set initial parameters of a model, ensuring a balanced start for training.
• Layers: Implementations of common neural network layers for building complex models.
• Losses: Loss functions such as categorical cross-entropy and mean squared error that guide training.
• Metrics: Tools for evaluating model performance, such as accuracy and precision.

These elements are formulated as numerical definitions (defn), ensuring compatibility with any Nx compiler. This setup allows models to be optimized using a variety of computational backends.

Model Creation API

Creating a neural network model in Axon is both intuitive and flexible. A typical model definition involves stacking input, dense layers, and defining activation functions in a pipeline:

model =
  Axon.input("input", shape: {nil, 784})
  |> Axon.dense(128)
  |> Axon.dense(10, activation: :softmax)

The resulting model is essentially an Elixir struct, easily serialized into different formats for deployment or further analysis. Using the Axon.Display module, users can generate detailed visual summaries of their model architectures.

Training API

To streamline the training process, Axon provides a Training API inspired by PyTorch Ignite. This API simplifies the training pipeline into four main steps:

1. Model Definition: Specify the neural network architecture.
2. Loop Definition: Create the training loop with tools like Axon.Loop.trainer.
3. Instrumentation: Add metrics and event handlers to monitor training.
4. Execution: Run the training loop over the dataset.

Here's an example of a complete training loop using Axon's high-level training interfaces:

model_state =
  model
  |> Axon.Loop.trainer(:categorical_cross_entropy, Polaris.Optimizers.adamw(0.005))
  |> Axon.Loop.metric(:accuracy)
  |> Axon.Loop.handle(:iteration_completed, &log_metrics/1, every: 50)
  |> Axon.Loop.run(data, %{}, epochs: 10, compiler: EXLA)

Axon employs Polaris for optimization, ensuring robust support for a diverse set of optimization tasks.

Getting Started with Axon

To integrate Axon into an Elixir project, you'll need to follow a few simple steps:

1. Create a New Project: Use the Mix build tool to initiate a new Elixir application.

   $ mix new my_app
   

2. Add Axon as a Dependency: Edit your mix.exs to include Axon and optionally an Nx compiler like EXLA:

   def deps do
     [
       {:axon, "~> 0.6"},
       {:exla, "~> 0.6"}
     ]
   end
   

3. Explore Further Integrations: Utilize projects like Ortex and AxonONNX for extended compatibility with ONNX models and other neural network formats.

Conclusion

Axon brings the power and flexibility of neural network development to Elixir developers. With its modular API design, Axon offers a comprehensive suite of tools for creating, training, and deploying sophisticated machine learning models, all within the robust Elixir ecosystem. Whether you are building simple models or complex learning systems, Axon is a valuable asset in bridging the gap between Elixir's functional programming paradigm and cutting-edge deep learning technologies.