• Tensorflow (r0.12)
• Cuda (7.5+)
• nltk python package

Apply Generative Adversarial Nets to generating sequences of discrete tokens.

The illustration of SeqGAN. Left: D is trained over the real data and the generated data by G. Right: G is trained by policy gradient where the final reward signal is provided by D and is passed back to the intermediate action value via Monte Carlo search.

The research paper SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient has been accepted at the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).

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.

Note: this code is based on the previous work by ofirnachum. Many thanks to ofirnachum.

After running the experiments, the learning curve should be like this: