https://github.com/connor-mccarthy/tf-ssgan
Implementing GANBERT model from recent deep learning research paper as an easy-to-use Tensorflow model.
https://github.com/connor-mccarthy/tf-ssgan
bert deep-learning generative-adversarial-network semi-supervised-learning tensorflow
Last synced: about 2 months ago
JSON representation
Implementing GANBERT model from recent deep learning research paper as an easy-to-use Tensorflow model.
- Host: GitHub
- URL: https://github.com/connor-mccarthy/tf-ssgan
- Owner: connor-mccarthy
- Created: 2021-06-14T02:13:23.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-07-14T04:21:23.000Z (almost 5 years ago)
- Last Synced: 2025-10-13T13:39:26.864Z (9 months ago)
- Topics: bert, deep-learning, generative-adversarial-network, semi-supervised-learning, tensorflow
- Language: Python
- Homepage:
- Size: 21.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
SSGAN Implementation Tensorflow
A simple API for a complex idea in current deep learning research: semi-supervised classification using generative adversarial networks (SSGANs).
This particular flavor of SSGANs is motivated by and modeled after the 2020 [research paper](https://www.aclweb.org/anthology/2020.acl-main.191.pdf) on GANBERT (see [citation](#citation)). See [ganbert/](`./ganbert/) for an implementation of the GANBERT model descibred in the paper using the `tf-ssgan` library.
## Getting Started
### Installation
```sh
pip install git+https://github.com/connor-mccarthy/tf-ssgan.git
```
### Code
This implementation uses the simple Keras [`Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) API. This makes it easy to implement an SSGAN for diverse classification problems.
```python
from tf_ssgan import SSGAN
# see ./ganbert/model_components.py for generator/discriminator details
generator = make_generator(...)
discriminator = make_discriminator(...)
ssgan = SSGAN(
generator=generator,
discriminator=discriminator,
name="my_ssgan",
)
ssgan.compile(
g_optimizer=tf.keras.optimizers.Adam(1e-4),
d_optimizer=tf.keras.optimizers.Adam(1e-4),
)
ssgan.fit(
train_ds,
validation_data=val_ds,
epochs=1000,
)
```
## Reproducing GANBERT
With Python 3.8.10:
```python
python -m venv .venv
source .venv/bin/activate
pip install -r ganbert/ganbert_requirements.txt
python ganbert
```
## Citation
GANBERT paper:
```bibtex
@inproceedings{croce-etal-2020-gan,
title = "{GAN}-{BERT}: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples",
author = "Croce, Danilo and
Castellucci, Giuseppe and
Basili, Roberto",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.191",
pages = "2114--2119"
}
```