# mgsalem/Tensorflow-Project-Template

A best practice for tensorflow project template architecture.

Repository: https://github.com/mgsalem/Tensorflow-Project-Template
Canonical: https://ross.abutalabs.com/products/tensorflow-project-template
Language: Python
License: Apache-2.0
License Family: permissive
Topics: tesnorflow, software-engineering, oop, deep-learning, neural-network, convolutional-neural-networks, tensorflow-tutorials, deep-learning-tutorial, best-practices, tensorflow, template
Last push: 2022-04-21T06:56:15+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3146, "days_push": 1595, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3616, forks 790 (observed 2026-08-28T04:08:11.785513+00:00)

## What it is
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
- artifact type: library
- maturity: maintenance
- function: boilerplate, deep-learning, machine-learning, logging, configuration-management
- domain: machine-learning, deep-learning, developer-tools
- platform: python, cross-platform
- tags: tensorflow, project-template, scaffold, oop, best-practices, folder-structure, tensorflow-1x, training-pipeline

## Member repositories
- mgsalem/Tensorflow-Project-Template (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:11.785513+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:33:34.766392+00:00, confidence not recorded.
  - readme: https://github.com/mgsalem/Tensorflow-Project-Template (fetched 2026-08-28T04:08:11.785513+00:00, sha 0f795530e2e3)
- Data as of 2026-08-30T08:39:29.467469+00:00.