How do you choose hyperparameters for a neural network?

Choosing hyperparameters means finding settings that help a neural network train efficiently and generalize well to unseen data.

Because the best values depend on the dataset, model, and task, they are usually selected through experimentation and validation rather than a fixed formula.

A practical approach

Start with the hyperparameters that usually have the largest impact:

• Learning rate: often the most important setting to tune first.
• Batch size: choose based on memory limits and training behavior.
• Model size: adjust the number of layers, hidden units, or channels to match the problem complexity.
• Regularization: tune dropout, weight decay, or related methods if the model overfits.
• Optimizer: compare choices such as SGD and Adam when needed.

See “What is a learning rate?”, “What is batch size in deep learning?”, and “What is regularization in deep learning?” for those concepts.

Use a validation set

Hyperparameters should normally be evaluated on a validation set, not the test set.

A typical workflow is:

Choose settings → Train model → Evaluate on validation data → Adjust hyperparameters → Repeat

The test set should generally be reserved for the final evaluation after major tuning decisions are complete.

Common search methods

Hyperparameters can be explored using:

• Manual tuning: change a few important values based on training behavior.
• Grid search: test predefined combinations.
• Random search: sample combinations randomly.
• Bayesian optimization: use previous trials to choose promising settings.

For expensive deep-learning models, random search or more adaptive methods are often more practical than testing every possible combination.

Watch the training signals

Useful signals include:

• Training and validation loss
• Training and validation accuracy or task-specific metrics
• Whether training is stable
• Whether the model is overfitting or underfitting
• Training time and hardware usage

For example, if validation performance stops improving while training performance continues to improve, stronger regularization or early stopping may help.

In short, choose neural-network hyperparameters by starting with high-impact settings, measuring performance on validation data, and systematically refining the values through controlled experiments.