Awesome
Sequence Generation with GANs trained by Gradient Estimation
Requirements:
- PyTorch v0.3
- Python 3.6
- CUDA 9.1 (For GPU)
Origin
The idea is from paper SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. We build on SeqGAN and compare three gradient estimators for sequence generation with GANs: REINFORCE (as in SeqGAN), and state-of-the-art REBAR (https://arxiv.org/pdf/1703.07370.pdf) and RELAX (https://arxiv.org/pdf/1711.00123.pdf).
The code is rewrited in PyTorch with the structure derived from (https://github.com/LantaoYu/SeqGAN)
Running
$ python main.py
After runing this file, the results will be printed on terminal. You can change the parameters in the main.py
.
Using CUDA
Pass in the gpu device number for e.g. 0
$ python main.py --cude {GPU_DEVICE_NUMBER}
Enable Visualization
Start the server (probably in a screen or tmux):
python -m visdom.server -port 8097
Run with --visualize
parameter
$ python main.py --cude {GPU_DEVICE_NUMBER} --visualize