Model Orchestration in C++

Welcome back to the third lesson of "Building and Applying Your Neural Network Library"! You've made tremendous progress in this course. In our first lesson, we successfully modularized our core neural network components — dense layers and activation functions. Then, in our previous lesson, we organized our training components by creating dedicated modules for loss functions and optimizers. We now have a well-structured foundation with a clean separation of concerns.

However, as you may have noticed from our previous training examples, we're still writing quite a bit of boilerplate code for each training session. We manually create layers, set up optimizers, write training loops, handle forward and backward passes, and coordinate all these components ourselves. While this gives us complete control, it also means we're repeating the same orchestration logic every time we want to train a model.

In this lesson, we're going to orchestrate all these components into a unified, high-level interface. We'll build a powerful Model abstract class that acts as the conductor of our neural network orchestra, coordinating layers, optimizers, and loss functions through clean, intuitive methods like compile(), fit(), and predict(), as well as a SequentialModel concrete subclass that can be seen as a better and improved replacement for the manual training approach we developed previously, providing a much more elegant and maintainable API for building and training neural networks. Let's get started!

Think of a symphony orchestra — while each musician is skilled at playing their individual instrument, the magic happens when a conductor coordinates all these talents toward a unified performance. Similarly, we've built excellent individual components (layers, optimizers, losses), but we need a conductor to orchestrate them into a seamless training experience.

Currently, our training process requires us to manually coordinate several moving parts:

• Instantiate layers and build our network architecture.
• Create an optimizer with specific parameters.
• Define our loss function.
• Implement the training loop with forward passes, loss calculations, backward passes, and weight updates.

This manual orchestration is error-prone and repetitive — exactly the kind of work that should be automated. What we need is a model class that serves as this conductor, providing a high-level API that handles the complexities of training coordination while still giving us the flexibility to customize our network architecture, choose different optimizers and loss functions, and control training parameters.

// Current goal: elegant and extensible training API
SequentialModel model;
model.add(std::make_unique<DenseLayer>(2, 4, "relu", 0.1));
model.add(std::make_unique<DenseLayer>(4, 1, "sigmoid", 0.1));
model.compile("sgd", 0.5, "mse");
model.fit(X_train, y_train, 1000);