Practical Workflow for Debugging and Tuning Deep Learning Models
Summary
This article collects practical advice for building and debugging deep learning models, using a generative adversarial network for manga coloring as its motivating example. It recommends beginning with a small working baseline, visualizing predictions and metrics, checking data and labels, and investigating model weaknesses before making changes. For debugging, it suggests first confirming that a model can overfit a small sample, then expanding to full data and monitoring validation performance.
The guidance also covers feature scaling, weight initialization, gradient problems, activation functions, regularization, batch size, data augmentation, and staged hyperparameter tuning. Its evidence is experiential advice rather than controlled comparisons or reported benchmark results. The examples and recommendations come from general deep learning practice, not trading research, and several choices—such as dropout or batch normalization—are presented as context dependent. Readers applying these ideas to quantitative models should validate them on their own data and avoid treating the rules of thumb as universal prescriptions.
Key ideas
- Start with a simple model that runs and provides a baseline for debugging.
- Try to overfit a small training sample to check whether the model and training code work.
- Inspect data quality, feature scaling, initialization, and gradients before changing model design.
- Add regularization and tune hyperparameters after establishing a reliable training process.
- Treat recommendations about batch size and regularization as context dependent.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.