Metadata-Version: 2.1
Name: tf-al
Version: 0.0.1
Summary: Active learning with tensorflow. Create custom and generic active learning loops. Export and share your experiments.
Home-page: https://github.com/ExLeonem/tf-al
License: MIT
Keywords: active learning,deep learning,tensorflow
Author: Maksim Sandybekov
Author-email: maksim.sandybekov@live.de
Requires-Python: >=3.9,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: numpy (>=1.18.5,<2.0.0)
Requires-Dist: scikit-learn (>=0.24.2,<0.25.0)
Requires-Dist: tensorflow (>=2.6.0,<3.0.0)
Requires-Dist: tensorflow-datasets (>=4.4.0,<5.0.0)
Project-URL: Repository, https://github.com/ExLeonem/tf-al
Description-Content-Type: text/markdown


# Active learning in tensorflow


# TODO

[ ] Adding [poetry](https://python-poetry.org/)?

# Index

1. [Installation](#Installation)
2. [Getting started](#Getting-started)
    1. [Model wrapper](#Model-wrapper)
    1. [Generic loop](#Basic-loop)
3. [Development](#Development)
    1. [Setup](#Setup)
    2. [Scripts](#Scripts)
4. [Contribution](#Contribution)
5. [Issues](#Issues)


# Installation

# Getting started

## Model wrapper





# Development

## Setup

1. Create a virtual env
1. [Install and Setup Poetry](https://python-poetry.org/docs/#installation)
2. []


## Scripts

### Create documentation

To create documentation for the `./active_leanring` directory. Execute following command
in `./docs`

```shell
$ make html
```

### Run tests

To perform automated unittests run following command in the root package directory.

```shell
$ pytest
```

To generate additional coverage reports run.

```shell
$ pytest --cov
```



# Contribution

# Issues
