https://github.com/abductivelearning/abl-hed
Handwritten Equations Decipherment with Abductive Learning
https://github.com/abductivelearning/abl-hed
deep-learning logical-programming machine-learning symbolic-ai
Last synced: 3 months ago
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Handwritten Equations Decipherment with Abductive Learning
- Host: GitHub
- URL: https://github.com/abductivelearning/abl-hed
- Owner: AbductiveLearning
- Created: 2019-05-20T18:18:39.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-01-31T05:41:45.000Z (over 1 year ago)
- Last Synced: 2025-04-02T02:46:28.124Z (6 months ago)
- Topics: deep-learning, logical-programming, machine-learning, symbolic-ai
- Language: Python
- Size: 18.1 MB
- Stars: 98
- Watchers: 4
- Forks: 21
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
🌟 **New!** [ABLkit](https://github.com/AbductiveLearning/ABLkit) released: A toolkit for Abductive Learning with high flexibility, user-friendly interface, and optimized performance. Welcome to try it out!🚀
Update: This repository is NO longer actively developed. It has been (mostly) superseded by [ABLkit](https://github.com/AbductiveLearning/ABLkit). The code for ABL-HED with ABLkit is in this [link](https://github.com/AbductiveLearning/ABLkit/tree/main/examples/hed). For the latest advancements and updates, we encourage you to visit the new repository.
# Abductive Learning for Handwritten Equation Decipherment
This is the code repository of the abductive learning framework for handwritten
equation decipherment experiments in _Bridging Machine Learning and Logical
Reasoning by Abductive Learning_ in NeurIPS 2019.
## Environment dependency
**This code is only tested in Linux environment.**
1. Swi-Prolog
2. Python3 with Numpy, Tensorflow and Keras
3. ZOOpt (as a submodule)
### Install Swipl
[http://www.swi-prolog.org/build/unix.html](http://www.swi-prolog.org/build/unix.html)
### Install python3
#### Install required package
```shell
#install numpy tensorflow keras
pip3 install numpy
pip3 install tensorflow
pip3 install keras
pip3 install zoopt
```
**Set environment variables(Should change file path according to your situation)**
```Shell
# cd to ABL-HED
git submodule update --init --recursive
export ABL_HOME=$PWD
cp /usr/local/lib/swipl/lib/x86_64-linux/libswipl.so $ABL_HOME/src/logic/lib/
export LD_LIBRARY_PATH=$ABL_HOME/src/logic/lib
export SWI_HOME_DIR=/usr/local/lib/swipl/
# for GPU user
export LD_LIBRARY_PATH=$ABL_HOME/src/logic/lib:/usr/local/cuda:$LD_LIBRARY_PATH
```
#### Install Abductive Learning code
**First change the `swipl_include_dir` and `swipl_lib_dir` in `setup.py` to your own SWI-Prolog path.**
```Shell
cd src/logic/prolog
python3 setup.py install
```
## Demo for arithmetic addition learning
Change directory to `ABL-HED`, and run equaiton generator to get the training data
```shell
cd src/
python3 equation_generator.py
```
Run abductive learning code
```shell
cd src/
python3 main.py
```
or
```shell
python3 main.py --help
```
To test the RBA example, please specify the `src_data_name` and `src_data_file`
together, e.g.,
```shell
python main.py --src_data_name random_images --src_data_file random_equation_data_train_len_26_test_len_26_sys_2_.pk
```
## Authors
- [Wang-Zhou Dai](http://daiwz.net) (Imperial College London)
- [Yu-Xuan Huang](http://www.lamda.nju.edu.cn/huangyx/) (Nanjing University)
- [Le-Wen Cai](http://www.lamda.nju.edu.cn/cailw/) (Nanjing University)