Tensorflow And Deep Learning Rules

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.