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+# Daisy
+## Dialog Analogous Intellectual System
+**conditional sequence generative adversarial network trained with policy gradient** 
+![](https://github.com/b03901170/machine-learning/blob/master/conditional%20seqGAN/image/framework.png)
+
+## Requirements: 
+* **Tensorflow r1.5.1**
+* Python 3.6
+* Numpy 1.13.3
+* tempfile (Optional)
+* gtts (Optional)
+* pygame (Optional)
+
+
+## Introduction
+Apply Policy Gradient to Generative Adversarial Nets to improve ChatBot dialog generation quality.
+- Generator: seq2seq model with attention and embedding
+- Discriminator: hierarchical encoder and two class classifier
+- Policy Gradient Update: use reward computed by the discriminator to update the generator
+- Monte Carlo Rollout: to compute reward for every generation step
+- Teacher Forcing: add maximum likelihood estimation training to GAN training
+- sampled softmax: to reduce computation complexity from regular softmax
+- model checkpoint supported: training early stopping and save model with best loss
+
+
+## Preperation
+1. Download corpus from:
+```
+1) https://www.cs.cornell.edu/%7Ecristian/Cornell_Movie-Dialogs_Corpus.html (official site)
+2) https://drive.google.com/file/d/13TvKXXrKVg9X7IEayu7eYpRbxfZ5GDE0/view?usp=sharing (backup link)
+```
+
+2. extract the .zip file, and save them to the path below: 
+```
+config.corpus_path = '../cornell_movie_dialog_corpus'
+```
+This default path can be modified by changing the '--corpus_dir' option in 'config.py'.
+
+3. Run Preprocessing on the raw corpus file:
+```
+python3 preprocess_data.py
+```
+This saves the pre-processed files to the path below:
+```
+config.data_dir = '../data'
+```
+This default path can be modified by changing the '--data_dir' option in 'config.py'.
+
+4. After running pre-processing, 5 files will be created:
+```
+1. processed_corpus.txt : Encoder input for training
+2. word2idx.pkl : word to idx dictionary mapping saved by pickle
+3. idx2word.pkl : idx to word dictionary mapping saved by pickle
+4. train_encode.pkl: model-ready training data: tokenized and index labeled text ready to be fed to the encoder
+5. train_decode.pkl: model-ready training data: tokenized and index labeled text ready to be fed to the decoder
+```
+
+
+## Training
+5. To run MLE pre train on the Generator model:
+```
+python3 run.py --pre_train --force_save
+```
+(optional: '--force_save' saves model every epoch, otherwise the default model checkpoint is activated and only the model with minimum loss will be saved.)
+
+6. To run generative adversarial training with policy gradient:
+```
+python3 run.py --gan_train --force_save
+```
+(optional: '--force_save' saves model every epoch, otherwise the default model checkpoint is activated and only the model with minimum loss will be saved.)
+
+
+## Inference
+7. To chat with the pre-trained model:
+```
+python3 run.py --pre_chat --speak
+```
+(optional: '--speak' enables audio response)
+
+8. To chat with the GAN-trained model:
+```
+python3 run.py --gan_chat --speak
+```
+(optional: '--speak' enables audio response)
+
+9. To evaluate the BLEU score of the pre-trained model:
+```
+python3 run.py --pre_evaluate
+```
+
+10. To evaluate the BLEU score of the GAN-trained model:
+```
+python3 run.py --gan_evaluate
+```
+
+
+## Trained Models
+11. To use exsiting models, place model files under the 'model' directory.
+	Trained Models (pre-trained / gan-trained) can be download from:
+```
+https://drive.google.com/drive/folders/1C0A53qqQpdGyKIEB7HVb6BqlBD5gvEJy?usp=sharing
+```
+
+
+## Other
+12. Source code:
+```
+1. configuration.py
+2. data_loader.py
+3. discriminator.py
+4. generator.py
+5. train.py
+6. test.py
+7. run.py
+8. tf_seq2seq_model.py
+```
+
+# conditional_seqGAN_Tensorflow