models/research/maskgan/README.md

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# MaskGAN: Better Text Generation via Filling in the ______

Code for [*MaskGAN: Better Text Generation via Filling in the
______*](https://arxiv.org/abs/1801.07736) published at ICLR 2018.

## Requirements

*   TensorFlow >= v1.5

## Instructions

Warning: The open-source version of this code is still in the process of being
tested. Pretraining may not work correctly.

For training on PTB:

1. Follow instructions here ([Tensorflow RNN Language Model Tutorial](https://www.tensorflow.org/tutorials/sequences/recurrent)) to train a language model on PTB dataset.
Copy PTB data downloaded from the above tensorflow RNN tutorial to folder "/tmp/ptb". It should contain following three files: ptb.train.txt, ptb.test.txt, ptb.valid.txt
Make folder /tmp/pretrain-lm and copy checkpoints from above Tensorflow RNN tutorial under this folder.


2. Run MaskGAN in MLE pretraining mode. If step 1 was not run*, set
`language_model_ckpt_dir` to empty.

```bash
python train_mask_gan.py \
 --data_dir='/tmp/ptb' \
 --batch_size=20 \
 --sequence_length=20 \
 --base_directory='/tmp/maskGAN' \
 --hparams="gen_rnn_size=650,dis_rnn_size=650,gen_num_layers=2,dis_num_layers=2,gen_learning_rate=0.00074876,dis_learning_rate=5e-4,baseline_decay=0.99,dis_train_iterations=1,gen_learning_rate_decay=0.95" \
 --mode='TRAIN' \
 --max_steps=100000 \
 --language_model_ckpt_dir=/tmp/pretrain-lm/ \
 --generator_model='seq2seq_vd' \
 --discriminator_model='rnn_zaremba' \
 --is_present_rate=0.5 \
 --summaries_every=10 \
 --print_every=250 \
 --max_num_to_print=3 \
 --gen_training_strategy=cross_entropy \
 --seq2seq_share_embedding
```

3. Run MaskGAN in GAN mode. If step 2 was not run, set `maskgan_ckpt` to empty.
```bash
python train_mask_gan.py \
 --data_dir='/tmp/ptb' \
 --batch_size=128 \
 --sequence_length=20 \
 --base_directory='/tmp/maskGAN' \
 --mask_strategy=contiguous \
 --maskgan_ckpt='/tmp/maskGAN' \
 --hparams="gen_rnn_size=650,dis_rnn_size=650,gen_num_layers=2,dis_num_layers=2,gen_learning_rate=0.000038877,gen_learning_rate_decay=1.0,gen_full_learning_rate_steps=2000000,gen_vd_keep_prob=0.33971,rl_discount_rate=0.89072,dis_learning_rate=5e-4,baseline_decay=0.99,dis_train_iterations=2,dis_pretrain_learning_rate=0.005,critic_learning_rate=5.1761e-7,dis_vd_keep_prob=0.71940" \
 --mode='TRAIN' \
 --max_steps=100000 \
 --generator_model='seq2seq_vd' \
 --discriminator_model='seq2seq_vd' \
 --is_present_rate=0.5 \
 --summaries_every=250 \
 --print_every=250 \
 --max_num_to_print=3 \
 --gen_training_strategy='reinforce' \
 --seq2seq_share_embedding=true \
 --baseline_method=critic \
 --attention_option=luong
```

4. Generate samples:
```bash
python generate_samples.py \
 --data_dir /tmp/ptb/ \
 --data_set=ptb \
 --batch_size=256 \
 --sequence_length=20 \
 --base_directory /tmp/imdbsample/ \
 --hparams="gen_rnn_size=650,dis_rnn_size=650,gen_num_layers=2,gen_vd_keep_prob=0.33971" \
 --generator_model=seq2seq_vd \
 --discriminator_model=seq2seq_vd \
 --is_present_rate=0.0 \
 --maskgan_ckpt=/tmp/maskGAN \
 --seq2seq_share_embedding=True \
 --dis_share_embedding=True \
 --attention_option=luong \
 --mask_strategy=contiguous \
 --baseline_method=critic \
 --number_epochs=4
```


*  While trying to run Step 2, the following error appears:
   NotFoundError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a Variable name or other graph    key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original    error:

   Key critic/rnn/biases not found in checkpoint
   [[node save/RestoreV2 (defined at train_mask_gan.py:431) ]]

   This is an issue with seq2seq model because it uses the attention mechanism.
   The issue arises if you saved the model with an earlier version (seq2seq is old) and restore with a recent one (saver.restore got updated).
   The naming convention for LSTM parameters changed, e.g. cell_0/basic_lstm_cell/weights became cell_0/basic_lstm_cell/kernel.
   Which is why you cannot restore them if you try to restore old checkpoints with recent TF.
   The below script will help rename the variables and everything will work as expected.
   https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/rnn/python/tools/checkpoint_convert.py

## Contact for Issues

*   Liam Fedus, @liamb315 <liam.fedus@gmail.com>
*   Andrew M. Dai, @a-dai <adai@google.com>