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TensorFlow Project Structure

A scalable and maintainable folder structure for TensorFlow applications

File Structure

  • src
    • train.py
    • evaluate.py
    • predict.py
    • serve.py
    • models
      • __init__.py
      • cnn_model.py
      • transformer_model.py
      • base_model.py
      • layers.py
    • data
      • __init__.py
      • preprocessing.py
      • data_loader.py
      • augmentation.py
      • pipeline.py
    • training
      • __init__.py
      • trainer.py
      • callbacks.py
      • metrics.py
    • utils
      • __init__.py
      • helpers.py
      • visualization.py
      • logging.py
  • data
    • raw
    • processed
    • external
    • interim
  • models
    • checkpoints
    • saved_model
    • tensorboard
  • notebooks
    • 01_data_exploration.ipynb
    • 02_model_development.ipynb
    • 03_evaluation.ipynb
  • tests
    • __init__.py
    • test_models.py
    • test_data.py
  • configs
    • model_config.yaml
    • training_config.yaml
  • scripts
    • download_data.py
    • export_model.py
  • Dockerfile
  • requirements.txt
  • setup.py
  • README.md

Directory Structure Explanation

src/serve.py

Model serving script for deploying models as web services or APIs.

src/models/layers.py

Custom TensorFlow layers and building blocks for neural networks.

src/data/augmentation.py

Data augmentation techniques to increase dataset diversity.

src/data/pipeline.py

TensorFlow data pipeline for efficient data loading and preprocessing.

src/training/

Training infrastructure including trainers, callbacks, and metrics.

src/training/callbacks.py

Custom TensorFlow callbacks for training monitoring and control.

models/tensorboard/

TensorBoard logs for visualizing training progress and model performance.

tests/

Unit tests for models, data processing, and utility functions.

configs/

YAML configuration files for different experiments and model variants.

scripts/

Utility scripts for data downloading, model export, and automation.

Dockerfile

Docker configuration for containerizing the TensorFlow application.