---
title: "IDE Rule: Tensorflow Deep Learning"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/spec-ide-rule-tensorflow-deep-learning"
modality: "IDE_RULE"
category: "Framework IDE Rules"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# IDE Rule: Tensorflow Deep Learning

**Summary:** IDE Rule: Tensorflow Deep Learning is a verified IDE RULE blueprint on Promptally in Framework IDE Rules optimized for leading AI models. Cursorrules Mdc specification for IDE Rule: Tensorflow Deep Learning. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | IDE_RULE |
| Category | Framework IDE Rules |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 618 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

```mdc
---
description: TensorFlow and deep learning rules for building, training, evaluating, and deploying neural network models
globs: ["**/*.py", "**/*.ipynb", "pyproject.toml", "requirements*.txt", "environment*.yml"]
alwaysApply: false
---

# 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.

```

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

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