Welcome to the world of Axon, where you can harness the power of deep learning in Elixir! With Axon, you can easily create, train, and optimize your neural networks using a clean and functional approach. This blog post will guide you through the basics of creating a neural network using Axon, and we’ll tackle some common troubleshooting steps along the way.

Getting Started with Axon

Before diving into the details, make sure you have Elixir installed. If you’re new to Elixir, you can create a project using the following command:

$ mix new my_app

Next, you need to add Axon to your project dependencies. Open your mix.exs file and add the following lines:

def deps do
  [
    {:axon, "~> 0.6"},
    {:exla, "~> 0.6"}, # Include EXLA for deep learning tasks
  ]
end

Understanding the Components of Axon

Now that you have Axon set up, let’s discuss its core components:

• Functional API: This is the backbone of Axon where all neural network functionalities are defined. It includes modules for activations, initializers, layers, losses, and metrics.
• Model Creation API: Here, you initialize and apply your models, making it easy to manage various structures.
• Training API: This API enables streamlined training processes that draw inspiration from popular frameworks like PyTorch Ignite.

Creating Your First Model

Let’s create a simple neural network model. Imagine building a structure from blocks; each method in Axon represents a different type of block that adds specific functionality to your network. Here’s how you can stack your blocks:

model = 
  Axon.input(input, shape: nil, 784)
  Axon.dense(128)
  Axon.dense(10, activation: :softmax)

In this analogy, the input block is where you begin, and the denses function lets you add layers that transform the data as it passes through.

Building and Evaluating the Model

After defining your model, you need to build it. Think of this step as laying down the blueprints for your building. Use the following command:

init_fn, predict_fn = Axon.build(model, compiler: EXLA)

Then, initialize the model with your data and run predictions. It’s like turning the lights on in your newly built structure:

params = init_fn.(Nx.template(1, 784, :f32), %)
predict_fn.(params, input)

Training Your Model

Training your model involves repeating a specific process to gradually enhance its performance. Here’s a step-by-step guide:

1. Define your model as shown above.
2. Set up a training loop with a defined loss function and optimizer.
3. Run the loop over your data for the given number of epochs.

Here’s an example of what the code might look like:

Axon.Loop.trainer(:categorical_cross_entropy, Polaris.Optimizers.adamw(0.005))
Axon.Loop.metric(:accuracy)
Axon.Loop.run(data, %, epochs: 10, compiler: EXLA)

Troubleshooting Tips

Despite all the advantages, you might run into issues while working with Axon. Here are some common troubleshooting steps to help you out:

• Check Your Elixir Version: Ensure you’re using a compatible version of Elixir.
• Dependencies Not Installed: Make sure all dependencies are correctly added to your project.
• Compiler Issues: If your code isn’t compiling, re-check syntax and function calls.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Axon offers a powerful yet flexible way to build neural networks in Elixir, allowing for deep learning applications that integrate seamlessly with other Nx libraries. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Final Thoughts

Embarking on a journey with Axon can be exhilarating, and with the tips provided here, you’ll be well-equipped to create remarkable neural networks. Happy coding!