TensorFlow and Deep Learning Rules

Project Structure

  • Separate data loading, model definition, training, evaluation, and serving code.
  • Use tf.data pipelines for scalable input processing.
  • Keep model hyperparameters in typed config files or dataclasses.
  • Store checkpoints, logs, and exported models outside source directories.
  • Keep notebooks exploratory; move repeatable training code into modules.

Model Development

  • Start with a small baseline model and a tiny overfit test before scaling.
  • Use Keras layers and models unless lower-level TensorFlow APIs are required.
  • Prefer explicit input shapes and named inputs/outputs.
  • Use callbacks for checkpointing, early stopping, learning-rate scheduling, and TensorBoard logging.
  • Use mixed precision only after validating numerical stability.
  • Pin random seeds where reproducibility matters, while documenting nondeterministic GPU behavior.

Training

  • Validate data shapes, dtypes, label ranges, and class balance before training.
  • Split data before augmentation or normalization fitting.
  • Use validation data for tuning and a separate test set for final reporting.
  • Track loss curves, metrics, learning rate, and resource use.
  • Save the best checkpoint by validation metric, not by final epoch.

Evaluation

  • Report task-appropriate metrics such as AUROC, F1, calibration, perplexity, BLEU/ROUGE, or MAE/RMSE.
  • Include confusion matrices or error slices for classification tasks.
  • Evaluate on edge cases and distribution shifts when data allows.
  • Compare against non-neural baselines when the dataset is small or tabular.

Deployment

  • Export models with clear input signatures.
  • Keep preprocessing consistent between training and serving.
  • Add smoke tests that load the exported model and run inference on sample inputs.
  • Monitor latency, memory, prediction drift, and input schema changes.

Common Mistakes

  • Do not tune architecture before verifying labels and data quality.
  • Do not leak validation data through preprocessing or augmentation.
  • Do not rely on accuracy alone for imbalanced data.
  • Do not deploy a notebook-only model.
  • Do not ignore batch size, dtype, and device differences between training and inference.