mgsalem/Tensorflow-Project-Template

Health v2 · maintenance only

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Adoption not part of the score

3616 stars · 790 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python project template that provides a recommended folder structure and object-oriented skeleton (base model, base trainer, data loader, logger, and config classes) for TensorFlow deep-learning projects. You start a new project by subclassing the provided base classes and implementing only your model graph and training logic.

Use cases

• structure my tensorflow project with best practices
• tensorflow deep learning project boilerplate
• folder layout template for a neural network project
• oop skeleton for model, trainer, and data loader
• starting point for a new deep learning experiment
• how to organize machine learning training code
• reusable scaffold for tensorflow experiments

When to choose

• You are starting a TensorFlow (1.x-style, session/Saver-based) deep-learning project and want a clean OOP structure from day one
• You maintain multiple ML experiments and want a consistent, familiar folder and class layout across them
• You want a teaching example of how models, trainers, data loaders, logging, and configuration fit together in a DL codebase

When to avoid

• You are building with TensorFlow 2.x/Keras, PyTorch, or JAX, since the template targets the legacy TF1 session-based API
• You need an actively maintained or feature-rich training framework rather than a static scaffold
• You just need a quick notebook or script for a small experiment where full project architecture is overkill

Facets

library · maturity maintenance

boilerplate deep-learning machine-learning logging configuration-management machine-learning deep-learning developer-tools python cross-platform tensorflow project-template scaffold oop best-practices folder-structure tensorflow-1x training-pipeline

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