As machine learning continues to permeate every industry, neural networks have become a hot topic. Models like GPT-3 dominate social media and tech headlines, often with sensational claims. Meanwhile, deep learning frameworks and libraries make state-of-the-art research more accessible than ever, with plug-and-play code promising impressive results.
Working at Hugging Face, I admit I'm partly responsible for this trend. But this ease of use can give inexperienced users the false impression that neural networks are a mature technology, when in fact the field is still evolving rapidly. In reality, building and training neural networks can be extremely frustrating. It's often hard to tell if poor performance is due to a bug in your code or a limitation of the model itself. You can make countless small mistakes without realizing it, and your model will still train and produce decent results.
This post outlines my mental process for building and debugging neural networks. By "debugging," I mean ensuring that what you've built aligns with what you intended. I'll also share questions to ask yourself when you're unsure of the next step. While my experience is in natural language processing, these principles apply broadly.
1. Start by Putting Machine Learning Aside
It may sound counterintuitive, but the first step is to ignore machine learning and focus on your data. Examine examples, labels, vocabulary diversity, length distributions, and so on. Dive into the data to get a sense of what you're working with and identify patterns a model might capture. By looking at a few hundred examples, you can spot high-level patterns. Key questions to ask:
- Are the labels balanced?
- Are there any gold labels you disagree with?
- How were the data collected? What are potential sources of noise?
- Are there natural preprocessing steps (tokenization, removing URLs or hashtags, etc.)?
- How diverse are the examples?
- What simple rule-based algorithm would perform decently on this problem?
It's important to get both a qualitative feel and a quantitative analysis of your dataset. If you're using a public dataset, check if someone has already analyzed it (common in Kaggle competitions).
2. Continue as If You Just Started Machine Learning
Once you understand your data deeply, put yourself in the shoes of your beginner self watching Andrew Ng's Coursera lectures. Start as simply as possible to gauge task difficulty and baseline performance. For example, for binary text classification, a logistic regression on word2vec or fastText embeddings can be a strong baseline. With modern tools, running these baselines is as easy as running BERT. If other baselines are available, run them to get familiar with the data.
It's tempting to build something fancy, but if it only beats simple baselines by a few points, you need to justify it rationally. Ensure you have reasonable comparisons:
- How would a random predictor perform (especially with unbalanced datasets)?
- What would the loss be for a random predictor?
- What metric(s) best measure progress on your task?
- What are the limits of that metric? If it's perfect, what can you conclude? What can't you?
- What's missing in simple approaches to achieve a perfect score?
- Are there architectures in your neural network toolbox that could model the data's inductive bias?
3. Don't Be Afraid to Look Under the Hood
Now you can start building your model based on your earlier insights. Implementing neural networks can be tricky due to many moving parts (optimizer, model, input pipeline), and small mistakes can go unnoticed while still yielding decent performance.
A good habit is to overfit a small batch of examples (e.g., 16) as soon as you think your implementation is complete. If correct, your model should achieve zero loss (remove all regularization). If not, there's likely a bug. In rare cases, the model may lack capacity. Start with a small-scale model (fewer layers) to quickly debug rather than chase high performance.
Pro-tip: Keep your initial experiments simple and fast. Focus on correctness before complexity.
By following these steps, you can avoid common pitfalls and build more reliable neural network models.