Underfitting in neural networks occurs when a model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and validation datasets. This typically happens when the model lacks the capacity to learn complex relationships, is trained for too few iterations, or is constrained by excessive regularization. To address underfitting, developers can adjust the model’s architecture, training process, or data preprocessing to better align the model’s complexity with the problem’s requirements.
One effective approach is to increase the model’s capacity by adding more layers or neurons. For example, a neural network with only one hidden layer might struggle to learn non-linear patterns in image classification tasks. Upgrading to a deeper architecture—such as adding two or three hidden layers with ReLU activation functions—can provide the necessary flexibility to capture intricate features. Additionally, extending the training duration or adjusting the learning rate can help. If training stops too early (e.g., due to a fixed number of epochs), the optimizer might not have converged to a good solution. Using adaptive optimizers like Adam with a lower learning rate (e.g., 0.001 instead of 0.1) can help the model learn more effectively without overshooting minima. For instance, a model trained for 50 epochs might underfit, but extending it to 200 epochs could resolve the issue if early stopping isn’t prematurely halting progress.
Another strategy involves improving feature engineering or reducing regularization. If input features lack meaningful information, even a complex model will struggle. Developers can create more informative features—like polynomial terms for regression problems or embeddings for categorical data—to help the model learn. For example, adding interaction terms (e.g., multiplying two features) in a sales prediction model might reveal hidden relationships. Similarly, excessive regularization (e.g., high L2 penalty or aggressive dropout rates) can overly constrain the model. Reducing the weight decay parameter from 0.1 to 0.01 or lowering the dropout rate from 50% to 20% might strike a better balance between generalization and learning capacity. Finally, if the dataset is small, synthetic data augmentation (e.g., rotating images or adding noise) can provide more training examples, though this is often more relevant for overfitting. By systematically testing these adjustments, developers can diagnose and resolve underfitting issues.