When a deep learning model stops improving, I usually avoid making random changes and instead debug the training pipeline systematically.

The first things I check are:

• Data quality: Verify that the labels are correct, features are properly normalized, and there are no issues with data leakage or class imbalance.
• Model behavior: Monitor both training and validation loss. If training loss isn’t decreasing, it often points to issues with the learning rate, initialization, or the model architecture. If validation loss worsens while training loss improves, overfitting is likely the problem.
• Hyperparameters: Experiment with the learning rate, batch size, optimizer, and regularization techniques such as dropout or weight decay. A poor learning rate is one of the most common reasons a model fails to converge.
• Sanity checks: Try overfitting the model on a very small subset of the data. If the model can’t achieve near-perfect performance on a handful of samples, there’s usually a bug in the data pipeline, loss function, or training loop.
• Monitoring: Plot learning curves, inspect gradients, and watch for issues like exploding or vanishing gradients.

In my experience, most training issues are caused by problems with the data or preprocessing rather than the neural network itself. Taking a structured, step-by-step debugging approach is usually much more effective than continuously changing the model architecture.