If your model isn’t improving in evaluation metrics during training, start by checking for issues in three key areas: data quality and formatting, hyperparameter configuration, and model architecture or evaluation setup. Each of these can silently derail training progress, and diagnosing them systematically is critical.
First, data-related issues are often the root cause. Ensure your input data is correctly formatted and preprocessed. For example, misaligned labels or incorrect data types (e.g., strings instead of numerical values) can prevent the model from learning meaningful patterns. Verify that normalization or standardization is applied consistently across training and validation sets—if one dataset is scaled differently, the model’s performance metrics will be unreliable. Additionally, check for class imbalance or insufficient training data. If one class dominates the dataset, the model might appear to “stall” because it optimizes for the majority class. For instance, in a binary classification task with 95% negative samples, accuracy might plateau early if the model simply predicts “negative” every time. Use techniques like oversampling, undersampling, or adjusting class weights in the loss function to address this.
Next, hyperparameter tuning is critical. A poorly chosen learning rate is a common culprit. If the rate is too high, the model’s updates may overshoot optimal weights, causing unstable or diverging training. If it’s too low, progress will be sluggish. Try a learning rate schedule (e.g., cyclical or step-based decay) or tools like learning rate finders to identify a suitable range. Batch size also matters: small batches introduce noise, while overly large batches may reduce generalization. For example, training a vision model with a batch size of 2 might lead to erratic updates, whereas a batch size of 1024 could cause the model to memorize the data. Additionally, check regularization parameters like dropout or weight decay. Excessive regularization can suppress learning, while too little may lead to overfitting without improving validation metrics.
Finally, inspect the model architecture and evaluation process. A model that’s too simple (e.g., a shallow neural network for a complex task) may lack the capacity to learn, while an overly complex one might fail to converge due to optimization challenges. For example, using a single-layer LSTM for a language translation task might underperform compared to a deeper transformer-based architecture. Ensure layers are correctly connected—common mistakes include mismatched tensor shapes or accidentally disabling gradient updates (e.g., freezing layers unintentionally). Also, validate your evaluation pipeline. If metrics aren’t improving, confirm that the validation data isn’t contaminated with training samples or that the metric itself is calculated correctly. For instance, in object detection, an incorrectly implemented IoU (Intersection over Union) calculation could misleadingly suggest stagnation. Additionally, test the model on a small subset of data to confirm it can overfit—if it can’t, there’s likely a bug in the model or data pipeline.
By methodically addressing these areas—data, hyperparameters, and architecture—you can identify and resolve the underlying issues causing training stagnation.