Agent skill · mindrally
tensorflow-deep-learning
Best practices for building, training, evaluating, and deploying neural networks with TensorFlow and Keras. Use when writing tf.data input pipelines, defining or training a Keras model, configuring callbacks for checkpointing and early stopping, evaluating a trained model, or exporting a model for serving.
What it needs
About 5k tokens when loaded.
What this skill does
TensorFlow and Deep Learning This skill covers project structure, model development, training, evaluation, and deployment practices for neural networks built with TensorFlow and Keras. Workflow for Training and Shipping a Model 1. Separate concerns into modules — Keep data loading, model definition, training, evaluation, and serving code in distinct files; treat notebooks as exploratory only. 2. Build the input pipeline first — Use tf.data.Dataset for scalable, prefetching input processing; validate shapes, dtypes, label ranges, and class balance before writing any model code. 3. Split before any fitting — Create train/validation/test splits before fitting normalization statistics or augmentation parameters, to avoid leakage. 4. Start with a tiny baseline — Build a small model and run a tiny overfit test (fit on a handful of examples until loss goes to ~0) to prove the training loop works before scaling up. 5. Train with callbacks — Wire in ModelCheckpoint (save best by validation metric, not final epoch), EarlyStopping, a learning-rate schedule, and TensorBoard logging. 6. Evaluate with task-appropriate metrics — Use a held-out test set only for final reporting, never for tuning. 7. Export with a explicit signature — Save with model.export() or tf.savedmodel.save() and define serving input signatures explicitly. 8. Smoke test the export — Load the exported model and run inference on sample inputs before deploying. Project Structure Separate data loading, model definition, training, evaluation, and serving code into distinct modules (e.g., data.py, model.py, train.py, evaluate.py, serve.py). Keep model hyperparameters in typed config objects (dataclasses) or config files, not scattered as literals through training code. Store checkpoints, logs, and exported models outside source directories (e.g., under artifacts/ or a configured output path), so they don't get swept into version control accidentally. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills mindrally/tensorflow-deep-learning