ViVi_2.0 is the code of the Chinese poem generation system, which can generate different styles of Chinese poems based on memory mechanism.
- Linux or MacOS
- Python 2.7
We recommand to use GPUs:
- NVIDIA GPUs
- cuda 7.5
Assume CUDA 7.5 has been installed in "/usr/local/cuda-7.5/", then environment variables need to be set:
export PATH=/usr/local/cuda-7.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH
To have tensorflow 0.10 installed, serval methods can be applied. Here, we only introduce the installation through virtualenv. And we install the tensorflow-gpu, if you choose to use CPU, please install tensorflow of cpu.
pip install virtualenv --user
virtualenv --system-site-packages tf0.10
source tf0.10/bin/activate
download tensorflow_0.10 from https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.10.0-cp27-none-linux_x86_64.whl
pip install tensorflow_0.10
Get into python console, and import tensorflow. If no error is encountered, the installation is successful.
Python 2.7.5 (default, Nov 6 2016, 00:28:07)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-11)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally
>>>
If you are from cslt, you can read run_in_cslt.txt and run the program on servers of cslt.
If you are not from cslt, make sure you have finished the installations above, then follow the commands below.
Prepare:
git clone git@gitlab.com:cslt/vivi_2.0.git
cd vivi_2.0
Train a model:
python train/train.py --decode False --batch_size 80
The models which are generated when the code runs through a round will be Saved in the dir 'tmp'.
Run a selected model:
python predict/predict.py model_name style memory_weight round_num poem_type
model_name : the model which is selected in the dir 'train/tmp/' is put in 'predict/model/'.
style : biansai/tianyuan/yanqing/general. Illegal input will be considered as 'general'.
memory_weight : suggest 0-30.
round_num : suggest 1.
poem_type : poem5/poem7/ymr/dlh/jzmlh/djc/zgt/psm/yja. Illegal input will be considered as 'poem7'.
[五言/七言/虞美人/蝶恋花/减字木兰花/点绛唇/鹧鸪天/菩萨蛮/渔家傲]
Change test keyword input: modify the file 'resource/predict_resource/test_poem_58k.txt'. Run examples: the file 'predict/execute.sh' includes all command. you can modify the command quickly and run it.
Get memory:
python predict/memoryModule_decoder.py model_name style
Add new style/change file for memory: modify in function 'get_memory_options()', file 'data_utils.py'. Put the poetry file under the directory 'resource/memory_resource/text/'.
Basically the poem generation system consists of two modules: train and predict.
train: Train the poem generation model using seq2seq and attention mechanism. models generated are saved in dir 'train/tmp'predict: Use generated model along with memory to decode and generate samples. Models used have to be transferred to dir 'predict/model'resource: Store different sources from word2vec data to augmented memory.
Open source licensing is under the Apache License 2.0, which allows free use for research purposes. For commercial licensing, please email byryuer@gmail.com.
Project leaders: Dong Wang
Project members: Jiyuan Zhang, Shiyue Zhang, Zheling Zhang, Yibo Liu, Xiuqi Jiang
If you have questions, suggestions and bug reports, please email [xiuqi6666@gmail.com].