SeqGAN (TensorFlow)

An implementation of seqGAN language generation model. Refer to the following paper for more details:

Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. In The Thirty-First AAAI Conference on Artificial Intelligence, 2017.

Require Packages

• cotk

• TensorFlow == 1.13.1

• TensorBoardX >= 1.4

Quick Start

• Downloading dataset and save it to ./data. (Dataset will be released soon.)

• Execute python run.py to train the model.

  • The default dataset is MSCOCO. You can use --dataset to specify other dataloader class.

  • It use gloves pretrained word vector by default setting. You can use --wvclass to specify wordvector class.

  • If you don’t have GPUs, you can add --cpu for switching to CPU, but it may cost very long time.

• You can view training process by tensorboard, the log is at ./tensorboard.

  • For example, tensorboard --logdir=./tensorboard. (You have to install tensorboard first.)

• After training, execute python run.py --mode test --restore best for test.

  • You can use --restore filename to specify checkpoints files, which are in ./model.

  • --restore last means last checkpoint, --restore best means best checkpoints on dev.

• Find results at ./output.

Arguments

usage: run.py [-h] [--name NAME] [--restore RESTORE] [--mode MODE]
              [--dataset DATASET] [--datapath DATAPATH]
              [--wvclass WVCLASS] [--wvpath WVPATH] [--out_dir OUT_DIR]
              [--log_dir LOG_DIR] [--model_dir MODEL_DIR]
              [--cache_dir CACHE_DIR] [--cpu] [--debug] [--cache]

optional arguments:
  -h, --help            show this help message and exit
  --name NAME           The name of your model, used for variable scope and
                        tensorboard, etc. Default: runXXXXXX_XXXXXX
                        (initialized by current time)
  --restore RESTORE     Checkpoints name to load. "last" for last checkpoints,
                        "best" for best checkpoints on dev. Attention: "last"
                        and "best" wiil cause unexpected behaviour when run 2
                        models in the same dir at the same time. Default: None
                        (dont load anything)
  --mode MODE           "train" or "test". Default: train
  --dataset DATASET     Dataloader class. Default: MSCOCO
  --datapath DATAPATH   Directory for data set. Default: ./data
  --wvclass WVCLASS     Wordvector class, None for using Glove pretrained
                        wordvec. Default: None
  --wvpath WVPATH       Path for pretrained wordvector. Default: wordvec
  --out_dir OUT_DIR     Output directory for test output. Default: ./output
  --log_dir LOG_DIR     Log directory for tensorboard. Default: ./tensorboard
  --model_dir MODEL_DIR Checkpoints directory for model. Default: ./model
  --cache_dir CACHE_DIR Checkpoints directory for cache. Default: ./cache
  --cpu                 Use cpu.
  --debug               Enter debug mode (using ptvsd).
  --cache               Use cache for speeding up load data and wordvec. (It
                        may cause problems when you switch dataset.)

For hyperparameter settings, please refer to run.py.

TensorBoard Example

Execute tensorboard --logdir=./tensorboard, you will see the plot in tensorboard pages:

Following plot are shown in this model:

• gen_loss: loss when pre-training the generator.

  

• gen_reward: average reward when adversarially training the generator.

  

• dis_loss: loss when pre-training and adversarially training the discriminator.

  

• dis_accuracy: accuracy when pre-training and adversarially training

  

Case Study of Model Results

Execute python run.py --mode test --restore best

The following output will be in ./output/[name]_test.txt:

self-bleu-4:0.129181
bw-bleu-4:0.464359
fw-bw-bleu-2:0.569625
fw-bleu-4:0.124722
fw-bleu-2:0.422881
fw-bw-bleu-3:0.325708
bw-bleu-3:0.662481
self-bleu-3:0.243009
fw-bleu-3:0.215936
fw-bw-bleu-4:0.196631
bw-bleu-2:0.872336
self-bleu-2:0.471246
A view of a taxi at night with two streetlights .
An goods vendor waiting for food to be made from the ceiling .
Pizza with forks , cucumbers , pickle and strawberry on top .
A man riding a wave on top of a surfboard .
A close view of an old toilet in a boat .
A red aircraft hanging at an airport runway .
A group of young men standing around a clock in a field .
Two soldiers eating doughnuts are looked out on the ground .
Cats blankets are on the floor with things behind them .
A bunch of people in dining area that are near .
A beach area with lots of yellow benches and cars on the back .
A plate holds a sandwich and mashed potatoes and cheese .
The keyboard is above the monitor as well .
A tennis player going one just after a tennis ball .
A man wearing winter skis admires a <unk> .
Some people on a bike row of motorcycles .
An airplane parked at the airport in the large city .
People walking down a street outside , at night .
A white cat laying on top of a bed .
...

Performance

	self-bleu-2	self-bleu-3	self-bleu-4	fw-bw-bleu-2	fw-bw-bleu-3	fw-bw-bleu-4
MSCOCO	0.4712	0.2430	0.1292	0.5696	0.3257	0.1966

Author

Jian Guan