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Want the diversity and interestingness that you get with samples from an adversarial process (GAN)?
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Want the directed focus you can give algorithms with Reinforcement Learning? (RL)
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Working with discrete sequence data (text, molecular SMILES, abc musical notation ,etc.)?
Then ORGAN if for you, define simple reward functions and alternate between adversarial and reinforced training.
Based on work from [](arxiv link here)
In order to train the model, cd into model and run
python train_ogan.py exp.json
where exp.json is a experiment configuration file.
A GPU is recommended since it can take several days to run, depending on dataset and sequence extension, algorithm is not parallelized for multiple GPUs.
- Tensorflow 1.0
- Python 2 or 3
- rdkit for molecular purposes