Metadata-Version: 2.1
Name: tfaip
Version: 0.0.1
Summary: UNKNOWN
Home-page: https://github.com/Planet-AI-GmbH/tf2_aip_base
Author: Planet AI GmbH
Author-email: admin@planet-ai.de
License: GPL-v3.0
Download-URL: https://github.com/Planet-AI-GmbH/tf2_aip_base/archive/0.0.1.tar.gz
Keywords: machine learning,tensorflow,framework
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: PyYAML
Requires-Dist: dataclasses-json
Requires-Dist: imageio
Requires-Dist: nibabel
Requires-Dist: pandas
Requires-Dist: pillow
Requires-Dist: prettytable
Requires-Dist: pytest
Requires-Dist: python-Levenshtein
Requires-Dist: scikit-image
Requires-Dist: tensorflow-datasets (~=3.0.0)
Requires-Dist: tensorflow (<=2.4.0,>=2.3.0)
Requires-Dist: tensorflow-addons
Requires-Dist: tqdm
Requires-Dist: xlrd

# tf2_aip_base
This repository is designed as a research framework for supervised machine learning. 
It aims to reduce your work on the train-loop, validation, saving, optimizer, multi-gpu and 
provides lot more features, which can be configured via command line.

# Setup
see: [Setup](https://github.com/Planet-AI-GmbH/tf2_aip_base/wiki/Install)
# Usage
To setup your own scenario see: [Scenario setup](https://github.com/Planet-AI-GmbH/tf2_aip_base/wiki/Scenario-setup.md)

## Running the tutorial scenario:
The default tutorial scenario is 'fashion-mnist'. Run your first training with:

`tfaip-train tutorial --trainer_params checkpoint_dir=models/fashion_default`

tfaip-train refers to `tfaip/scripts/train.py`. You can switch to mnist data with `--data_params dataset=mnist`.

You can evaluate the model on the validation dataset with:
`tfaip-lav --export_dir models/fashion_default/export`

Most hyper parameter can be configured via command line see `tfaip-train -h` and `tfaip-train tutorial -h`.
Checkout the [Wiki](https://github.com/Planet-AI-GmbH/tf2_aip_base/wiki) for further explanations.

_Contributions are welcome, and they are greatly appreciated!_






