https://github.com/salu133445/dan
Source code for "Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting"
https://github.com/salu133445/dan
generative-adversarial-network machine-learning
Last synced: over 1 year ago
JSON representation
Source code for "Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting"
- Host: GitHub
- URL: https://github.com/salu133445/dan
- Owner: salu133445
- License: mit
- Created: 2018-11-26T13:25:12.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-09-01T18:35:57.000Z (almost 4 years ago)
- Last Synced: 2025-03-19T03:33:46.933Z (over 1 year ago)
- Topics: generative-adversarial-network, machine-learning
- Language: Python
- Homepage: https://salu133445.github.io/dan/
- Size: 7.96 MB
- Stars: 42
- Watchers: 4
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# DANTest
Source code for "Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting"
## Prerequisites
> __Below we assume the working directory is the repository root.__
### Install dependencies
- Using pipenv (recommended)
> Make sure `pipenv` is installed. (If not, simply run `pip install --user pipenv`.)
```sh
# Install the dependencies
pipenv install
# Activate the virtual environment
pipenv shell
```
- Using pip
```sh
# Install the dependencies
pip install -r requirements.txt
```
### Prepare training data
```sh
# Download the training data
./scripts/download_data.sh
# Store the training data to shared memory
./scripts/process_data.sh
```
You can also download the MNIST handwritten digit database manually
[here](http://yann.lecun.com/exdb/mnist/).
## Scripts
We provide several shell scripts for easy managing the experiments. (See
`scripts/README.md` for a detailed documentation.)
> __Below we assume the working directory is the repository root.__
### Train a new model
1. Run the following command to set up a new experiment with default settings.
```sh
# Set up a new experiment (for one run only)
./scripts/setup_exp.sh -r 1 "./exp/my_experiment/"
```
2. Modify the configuration files for different experimental settings. The
configuration file can be found at `./exp/my_experiment/config.yaml`.
3. Train the model by running the following command.
```sh
# Train the model (on GPU 0)
./scripts/run_train.sh -c -g 0 "./exp/my_experiment/"
```
## Outputs
For each run, there will be three folders created in the experiment folder.
- `logs/`: contain all the logs created
- `model/`: contain the trained model
- `src/`: contain a backup of the source code
Note that the _validation results_ can be found in the `logs/` folder.
## Paper
__Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting__
Hao-Wen Dong and Yi-Hsuan Yang
_arXiv preprint arXiv:1901.08753_, 2019.
[[website](https://salu133445.github.io/dan/)]
[[paper](https://salu133445.github.io/dan/pdf/dan-arxiv-paper.pdf)]
[[arxiv](https://arxiv.org/abs/1901.08753)]
[[code](https://github.com/salu133445/dan)]