If your model isn't improving during training, start by verifying your data pipeline and preprocessing. Common issues include incorrect data formatting (e.g., mismatched input shapes or misaligned labels), improper normalization/scaling (e.g., using [0, 255] pixel values without scaling to [0, 1] for images), or data leakage between training and validation sets. For example, if you forgot to shuffle the dataset before splitting, the validation set might contain a non-representative sample of classes, making metrics unreliable. Check for class imbalance by confirming label distributions in training batches—if one class dominates, the model might ignore minority classes. Also, ensure data augmentation (if used) isn’t overly destructive (e.g., cropping out critical features in images).
Next, inspect hyperparameter choices, particularly the learning rate. A rate that’s too high can cause unstable training (loss oscillates wildly), while one that’s too low leads to slow or stalled progress. For example, a learning rate of 0.1 might overshoot optimal weights in a neural network, whereas 1e-5 might take thousands of epochs to converge. Use learning rate range tests or adaptive optimizers like Adam (with default settings) as a baseline. Check if batch size is too small (noisy gradients) or too large (reduced generalization). Also, verify that regularization terms (e.g., weight decay, dropout) aren’t overly aggressive—setting dropout to 0.8 might prevent the model from learning meaningful patterns.
Finally, debug the model architecture and implementation. A model that’s too shallow or narrow might lack the capacity to learn the task. For instance, a single-layer CNN won’t capture hierarchical features in complex images. Check for vanishing/exploding gradients by monitoring weight updates—use gradient clipping or normalization layers (e.g., BatchNorm) if needed. Verify that loss functions and metrics align with the task (e.g., using cross-entropy for classification, not MSE). Look for implementation errors, like accidentally skipping layers in forward passes or using incorrect activation functions (e.g., ReLU in the final layer for a regression task). Test with a small subset of data—if the model can’t overfit to a few samples, there’s likely a structural flaw.