yizhilll / seqgan_custom

SeqGAN

Requirements:

• Tensorflow r1.0.1
• Python 2.7
• CUDA 7.5+ (For GPU)

Introduction

Apply Generative Adversarial Nets to generating sequences of discrete tokens.

The code is based on the code of SeqGAN but replace the generation data part with custom corpus.

The research paper is SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient .

To run the experiment with default parameters:

$ python sequence_gan.py

You can change the all the parameters in sequence_gan.py.

The experiment has two stages:

• In the first stage, use the positive data provided by the oracle model and Maximum Likelihood Estimation to perform supervise learning.
• In the second stage, use adversarial training to improve the generator.