/SeqGAN

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

Primary LanguagePython

SeqGAN

Requirements:

Tensorflow r0.10
Cuda 7.5 (for GPU)
nltk python package

Introduction

For full information, see the paper:
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient (http://arxiv.org/abs/1609.05473)

We provide example codes to repeat the synthetic data experiments with oracle evaluation mechanisms. Move to MLE_SeqGAN folder and run

python pretrain_experiment.py

will start maximum likelihood training with default parameters. In the same folder, run

python sequence_gan.py

will start SeqGAN training. After installing nltk python package, move to pg_bleu folder and run

python pg_bleu.py

will start policy gradient algorithm with BLEU score (PG-BLEU), where the final reward for MC search comes
from a predefined score function instead of a CNN classifier. Finally, move to schedule_sampling folder and run

python schedule_sampling.py

will launch SS algorithm with default parameters.