Welcome! Today, we will explore the art of fine-tuning Autoencoders. Previously, we learned about Autoencoders and their usefulness in dimensionality reduction. Now, we turn our attention to Hyperparameters — adjustable settings that can optimize model performance. We will experiment with different network architectures (by changing layers and activation functions) and training parameters (such as learning rates and batch sizes) of an Autoencoder using R and the keras3 package. Ready to begin this hands-on journey? Let’s get started!

Hyperparameters are adjustable settings that influence how a machine learning model learns. They are generally divided into two types: architectural and learning hyperparameters. Architectural hyperparameters include elements like the number of hidden layers and units in a neural network. Learning hyperparameters include the learning rate, number of epochs, and batch size. Tuning these hyperparameters is essential for managing model complexity and improving performance.

Architectural hyperparameters define the structure of a neural network, such as the number of layers and units. Layers are computational components that transform input data, and units (neurons) produce activations. Let’s modify our Autoencoder and experiment with different activation functions using R and keras3:

Learning hyperparameters, such as learning rate and batch size, have a significant impact on training. Let’s see how changing these values affects our Autoencoder.

Now, let’s train the same architecture with a slower learning rate and compare the results.

Let’s compare the model performances by calculating the reconstruction errors. Lower errors indicate better performance.

These results demonstrate the influence of the learning rate and provide a pathway for deeper exploration of hyperparameter tuning. By comparing the reconstruction errors, you can observe the impact of different learning rates on the performance of the autoencoder. Typically, a slower learning rate may result in a lower reconstruction error, indicating better performance. Note that output values may vary due to randomness and library versions.

Well done! Today, you explored how to fine-tune Autoencoders by adjusting hyperparameters. Your next step is to experiment further: try varying the settings and observe how they affect model performance. In the next lesson, we will dive into Loss Functions and Optimizers for Autoencoders. Keep experimenting and learning!