NeuralWritingMachine

test Updates

Test 1.0 version has born (2017-02-26)

Recent Updates

Support TensorFlow r1.0 (2017-02-24)
Support word vector visualization (2017-03-24)

The following picture shows a top-100 word-vec visualization for Chinese

Introduction and Usage

Neural Writing Machine (NWM) can write styles of documents such like Chinese poems, couplets, novel, lyrics of Jay Chou or Xi Lin. NWM is based on end-to-end RNN encoder-decoder approach, and can be combined with attention mechanism. For more details, please visit my wechat public media:deeplearningdigest.

We provide four types of text databases, which are:

Chinese Poems
Chinese Couplets
Chinese Novels
Lyrics of Jay Chou and Xi Lin

Usage of NWM is as follows:

usage: train.py [-h] [--style STYLE] [--data_dir DATA_DIR]
                [--save_dir SAVE_DIR] [--log_dir LOG_DIR]
                [--rnn_size RNN_SIZE] [--embedding_size EMBEDDING_SIZE]
                [--num_layers NUM_LAYERS] [--model MODEL] [--rnncell RNNCELL]
                [--attention ATTENTION] [--batch_size BATCH_SIZE]
                [--seq_length SEQ_LENGTH] [--num_epochs NUM_EPOCHS]
                [--save_every SAVE_EVERY] [--grad_clip GRAD_CLIP]
                [--learning_rate LEARNING_RATE] [--decay_rate DECAY_RATE]
                [--keep KEEP] [--pretrained PRETRAINED]

optional arguments:
  -h, --help            show this help message and exit
  --style STYLE         set the type of generating sequence,egs: poem,
                        couplet, novel, LX, FWS
  --data_dir DATA_DIR   set the data directory which contains new.txt
  --save_dir SAVE_DIR   set directory to store checkpointed models
  --log_dir LOG_DIR     set directory to store checkpointed models
  --rnn_size RNN_SIZE   set size of RNN hidden state
  --embedding_size EMBEDDING_SIZE
                        set size of word embedding
  --num_layers NUM_LAYERS
                        set number of layers in the RNN
  --model MODEL         set the model
  --rnncell RNNCELL     set the cell of rnn, eg. rnn, gru, or lstm
  --attention ATTENTION
                        set attention mode or not
  --batch_size BATCH_SIZE
                        set minibatch size
  --seq_length SEQ_LENGTH
                        set RNN sequence length
  --num_epochs NUM_EPOCHS
                        set number of epochs
  --save_every SAVE_EVERY
                        set save frequency while training
  --grad_clip GRAD_CLIP
                        set clip gradients when back propagation
  --learning_rate LEARNING_RATE
                        set learning rate
  --decay_rate DECAY_RATE
                        set decay rate for rmsprop
  --keep KEEP           init from trained model
  --pretrained PRETRAINED
                        init from pre-trained model

Environment

Tensorflow rc0.12
Python2.7
Numpy

Training

If you train for the first time, you can input python train.py --style=poem

If you want to train from the restored model, you can input python train.py --style=poem --keep=True

NWM would check for the model file and save to the specified folder automatically!

Generation

You can sample from the trained model to generate new documents, usage is as follows:

usage: sample.py [-h] [--style STYLE] [--save_dir SAVE_DIR] [--n N]
                 [--start START] [--sample SAMPLE]

optional arguments:
  -h, --help           show this help message and exit
  --style STYLE        set the type of generating sequence,egs: novel, jay,
                       linxi, tangshi, duilian
  --save_dir SAVE_DIR  model directory to store checkpointed models
  --n N                number of words to sample
  --start START        prime text
  --sample SAMPLE      three choices:argmax,weighted,combined

There are three methods for sampling, they are argmax, weighted and combined.

argmax : generate the word with highest probability at each timestep

weighted : sample the word with weighted probability at each timestep

combined : generate the word with highest probability at each timestep except that when the previous word is a marker of full stop, which is 。.

I suggest that you should choose the weighted sampling.

Generation Examples

Poem:

一春落去细复来，画眉峰上吴支路。一去紫露微月，夜深锁五月黑。
鸣筝鸟绿青子，风雨更吹魂。恨水来似花云尽，愁尽落灯红。

FWS-style Lyric:

从前进开封 出水芙蓉加了星
在狂风叫我的爱情 让你更暖
心上人在脑海别让人的感觉
想哭 是加了糖果
船舱小屋上的锈却现有没有在这感觉
你的美 没有在
我却很专心分化
还记得
原来我怕你不在 我不是别人
要怎么证明我没力气 我不用 三个马蹄
生命潦草我在蓝脸喜欢笑
中古世界的青春的我
而她被芒草割伤
我等月光落在黑蝴蝶
外婆她却很想多笑对你不懂 我走也绝不会比较多
反方向左右都逢源不恐 只能永远读着对白

chatbot style

A: 在吗？
B: 在
A: 有空吗？
B: 没空呢
A: ...
B: ...

Future Work

Add pretraining support
Add attention mechanism
Add dynamic computation graph without padding

Contact Me

If my code is helpful to you, please give me a star and fork to encourage me to keep updating. Thank you.

For any questions, welcome to send email to :[email protected]. If you use wechat, you can follow me by searching wechat public media id:deeplearningdigest, I would push several articles every week to share my deep learning practices with you. Thanks!

twjefchiu / neural_writing_machine Goto Github PK