https://github.com/dongjunlee/gan-pytorch
PyTorch implementation of 'GAN (Generative Adversarial Networks)'
https://github.com/dongjunlee/gan-pytorch
deep-learning estimator experiments gan generative-adversarial-network generative-model hb-experiment pytorch
Last synced: about 2 months ago
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PyTorch implementation of 'GAN (Generative Adversarial Networks)'
- Host: GitHub
- URL: https://github.com/dongjunlee/gan-pytorch
- Owner: DongjunLee
- Created: 2018-04-12T16:41:14.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-04-15T12:38:56.000Z (about 7 years ago)
- Last Synced: 2025-04-15T00:13:27.434Z (about 2 months ago)
- Topics: deep-learning, estimator, experiments, gan, generative-adversarial-network, generative-model, hb-experiment, pytorch
- Language: Python
- Homepage:
- Size: 485 KB
- Stars: 6
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Generative Adversarial Nets [](https://github.com/hb-research)
PyTorch implementation of [Generative Adversarial Nets](https://arxiv.org/abs/1406.2661).
- [images source](http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html)
## Requirements
- Python 3.6
- PyTorch
- [hb-config](https://github.com/hb-research/hb-config) (Singleton Config)
- [Slack Incoming Webhook URL](https://my.slack.com/services/new/incoming-webhook/)
## Project Structure
.
├── config # Config files (.yml, .json) using with hb-config
├── gan # Generative Adversarial Networks architecture
├── __init__.py # train, evaluate, predict logic
├── module.py # Discriminator, Generator module
└── utils.py # Save and Load Model, TensorBoard
├── data_loader.py # make_data_loader (using DataLoader)
├── main.py
├── model.py # define Model Spec
└── model.py # utils
Reference : [hb-config](https://github.com/hb-research/hb-config)
- Manage experiments like [tf.Estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)
## Config
Can control all **Experimental environment**.
example: config.yml
```yml
data:
path: "data/"
model:
z_dim: 100 # Random noise dimension coming into generator, per output vector
real_dim: 784
g_h1: 256
g_h2: 512
g_h3: 1024
d_h1: 1024
d_h2: 512
d_h3: 256
dropout: 0.3
train:
model_dir: "logs/gan"
batch_size: 64
train_steps: 50000
d_learning_rate: 0.0002 # 2e-4
g_learning_rate: 0.0002
optim_betas:
- 0.9
- 0.999
save_checkpoints_steps: 1000
verbose_step_count: 100
predict:
batch_size: 64
slack:
webhook_url: "" # after training notify you using slack-webhook
```
## Usage
Install requirements.
```pip install -r requirements.txt```
Then, start training
```python main.py --mode train```
After training, generate images
```python main.py --mode predict```
- generated image example
### Experiments modes
:white_check_mark: : Working
:white_medium_small_square: : Not tested yet.
- :white_medium_small_square: `evaluate` : Evaluate on the evaluation data.
- :white_check_mark: `train` : Fit the estimator using the training data.
- :white_medium_small_square: `train_and_evaluate` : Interleaves training and evaluation.
- :white_check_mark: `predict` : Generate images.
---
### Tensorboar
```tensorboard --logdir logs```
- example
## Reference
- [hb-research/notes - Generative Adversarial Nets](https://github.com/hb-research/notes/blob/master/notes/gan.md)
- [Paper - Generative Adversarial Nets](https://arxiv.org/abs/1406.2661)
- [pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial)
## Author
[Dongjun Lee](https://github.com/DongjunLee) ([email protected])