- Tensorflow r0.10
- Cuda 7.5 (for GPU)
- 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.
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.
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:
