The number of neurons per layer is determined by the complexity of the features the model needs to learn. More neurons allow the network to capture complex patterns, but too many can lead to overfitting. A common approach is to start with fewer neurons and gradually increase while monitoring validation performance.
The input and output layers have fixed sizes based on the data dimensions and task requirements. For hidden layers, choosing neuron counts as powers of 2 (e.g., 64, 128, 256) is a practical heuristic that balances model capacity and computational efficiency.
Regularization techniques like dropout or weight decay can help manage overfitting if the model has too many neurons. Experimentation with hyperparameter tuning tools like Grid Search or Bayesian Optimization can also assist in finding the optimal configuration for neuron counts.