Deciding the number of neurons per layer in a neural network involves balancing model capacity with computational efficiency and avoiding overfitting. There’s no universal formula, but common strategies include analyzing the problem’s complexity, starting with empirical rules, and iterating based on performance. The goal is to use enough neurons to capture patterns in the data without creating unnecessary computational overhead or memorizing noise.
A practical starting point is to consider the input and output dimensions. For example, in a dense layer processing 20 input features, a first hidden layer might use 16-32 neurons to avoid drastic dimensionality reduction while allowing nonlinear transformations. For output layers, the neuron count is often fixed by the problem: 1 neuron for regression, 10 for a 10-class classification task. For hidden layers, a common heuristic is to start with a number between the input and output sizes, then adjust. For instance, in an image classification task with 784 input pixels (28x28 image) and 10 output classes, a middle layer might use 128-256 neurons. Architectures like autoencoders often follow a “bottleneck” pattern, reducing neurons progressively (e.g., 784 → 256 → 64 → 10) and expanding back for reconstruction.
Experimentation is critical. Start with a baseline (e.g., 64 neurons per layer for small datasets, 256+ for complex tasks), then validate performance. If the model underfits (low training accuracy), add neurons or layers. If it overfits (high training but low validation accuracy), reduce neurons, add dropout, or use regularization. For example, training a text classifier with 100-dimensional embeddings might start with two hidden layers of 128 neurons each. If validation accuracy plateaus, increasing to 256 neurons per layer or adding a third layer could help. Conversely, if training accuracy reaches 95% but validation stalls at 70%, cutting neurons to 64 per layer might improve generalization. Tools like hyperparameter tuning libraries (Optuna, Keras Tuner) can automate this exploration. Ultimately, the optimal configuration depends on iterative testing, domain knowledge, and resource constraints.