A hyperparameter in neural networks is a configuration setting that controls how the model learns during training. Unlike model parameters (e.g., weights and biases), which are learned automatically from data, hyperparameters are set manually before training begins. These settings influence the model’s architecture, optimization process, and training behavior. For example, the learning rate—a hyperparameter—determines how much the model adjusts its parameters during each update. Hyperparameters are critical because they directly impact training efficiency, model performance, and generalization to new data.
Common examples of hyperparameters include the learning rate, batch size, number of hidden layers, number of neurons per layer, and regularization strength. The learning rate controls the step size during gradient descent; too high a value may cause unstable training, while too low a value slows convergence. Batch size affects how many data samples are processed before updating parameters—larger batches use more memory but provide smoother gradient estimates. The number of hidden layers and neurons defines the model’s capacity to learn complex patterns. For instance, a shallow network might underfit simple data, while an overly deep one could overfit. Regularization hyperparameters like dropout rate or L2 penalty help prevent overfitting by adding constraints to the model. Activation functions (e.g., ReLU, sigmoid) are sometimes considered hyperparameters, though they’re often fixed once chosen.
Developers tune hyperparameters through experimentation or automated methods like grid search, random search, or Bayesian optimization. Tools like Keras Tuner or Optuna automate this process by testing combinations and selecting those that maximize validation accuracy. However, manual tuning is still common, especially when domain knowledge guides initial choices. For example, a smaller learning rate might be preferred for fine-tuning a pre-trained model, while a larger one could speed up training from scratch. Hyperparameters are often interdependent: a larger batch size might require adjusting the learning rate. Frameworks like TensorFlow and PyTorch provide defaults, but real-world problems typically require customization. The process is iterative and resource-intensive, as training multiple configurations can be computationally costly. Ultimately, understanding hyperparameters’ roles helps developers balance training speed, model complexity, and performance.