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https://github.com/YOUSIKI/TensorLayer-BiGAN
A TensorLayer implementation of BiGAN (Adversarial Feature Learning).
https://github.com/YOUSIKI/TensorLayer-BiGAN
gan tensorflow tensorlayer
Last synced: 6 days ago
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A TensorLayer implementation of BiGAN (Adversarial Feature Learning).
- Host: GitHub
- URL: https://github.com/YOUSIKI/TensorLayer-BiGAN
- Owner: YOUSIKI
- License: mit
- Created: 2019-10-16T00:16:50.000Z (about 5 years ago)
- Default Branch: celeba
- Last Pushed: 2020-09-14T16:01:28.000Z (about 4 years ago)
- Last Synced: 2024-10-30T02:58:36.252Z (14 days ago)
- Topics: gan, tensorflow, tensorlayer
- Language: Python
- Homepage:
- Size: 15.2 MB
- Stars: 9
- Watchers: 4
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-tensorlayer - BiGAN
README
# TensorLayer-BiGAN
A TensorLayer implementation of [Adversarial Feature Learning](https://arxiv.org/abs/1605.09782), which is also known as BiGAN.
## Prerequisites
- Python 3.7
- TensorFlow 2.0.0
- TensorLayer 2.1.1
We highly recommend you to install the packages above using Anaconda (or Miniconda).
### Install TensorFlow with GPU support
``` bash
conda create -n bigan python=3.7 tensorflow-gpu
```
### Install TensorFlow with only CPU support
``` bash
conda create -n bigan python=3.7 tensorflow
```
### Install TensorLayer
```bash
conda activate bigan && pip install tensorlayer
```
## Usage
### Evaluation
First, download the pre-trained weights from [here](https://github.com/YOUSIKI/TensorLayer-BiGAN/releases).
Second, use the follow script to generate an images.
``` bash
python eval.py
```
This will ganerate 8x8 fake human faces and save the image to `samples.png`. For further evaluation usage, please read the code in `eval.py` and modify it as you like.
### Training
Clone this repository to your computer.
``` bash
git clone https://github.com/YOUSIKI/BiGAN.TensorLayer.git
```
To train a BiGAN network from nothing, please download CelebA Dataset from eigher [Google Drive](https://drive.google.com/open?id=0B7EVK8r0v71pWEZsZE9oNnFzTm8) or [Baidu Netdisk](https://pan.baidu.com/s/1eSNpdRG#list/path=%2F).
*Here, I recommend you to download only `Img/img_align_celeba.zip` to save some time. Moreover, you may look for this dataset on other unofficial sites such as [BYR](https://bt.byr.cn/).*
After downloading the zip file, extract it to a fold such as `data` under the project directory. You can also extract it to other directories you like, but remember to modify `DATA_PATH` in `data.py` if you do so.
Next, use the follow script to train.
``` bash
python train.py
```
The training configurations can be found and modified in `config.py`.
If you want to train the network on your own dataset, please view every `.py` file and change them as your will.
## Result on CelebA
For more sample images saved during training, check `samples` folder.
## More
This project is mostly based on [dcgan implementation of tensorlayer](https://github.com/tensorlayer/dcgan), you may find this repository useful while reviewing the code. Many thanks to its contributors ([zsdonghao](https://github.com/zsdonghao) et al.)
We are just beginners of neural networks (and TensorLayer). There may be many mistakes in this project. Please contact us if you found. All issues and pull requests are welcomed.