Poem Generation Model by Sequence to Seqeunce with Attention

Attention based sequence to sequence model with tensorflow

Korean Poem generation model trained by korean poem dataset

Basic about the model

This model architecture based on 'Attiontion based Sequnce to Sequence' model. The encoder is consists of Bidirectional GRU. And the decoder receives the output of value of the encoder to obtain the attention score, the hidden state vector of encoder's last step to initialize hidden state vector of decoder's GRU cell.

Additionaly, in the case of the embedding vector, we learn by using word2vec in advance, not as learning as the model.

Repository contents

• data: containing data file, embedding matrix
• seq2seq: contain all modules for model
  • __init__.py: make graph consisting of encoder & decoder
  • decoder.py: make decoder layer
  • encoder.py: make encoder layer
  • module.py: Additive attention & Bidirectional GRU class
• configs.py: configs about model
• data_process.py: data process fuction
• main.py: execution train by TensorFlow estimator
• predict.py: predict by trained model

Dependencies

• Python >= 3.6
• Tensorflow >= 1.8
• gensim
• numpy

Datasets

Crawled koeran poem dataset

• Total 1034 poem
• Total 17066 sequnece to trainable

Config

tf.app.flags.DEFINE_integer('batch_size', 64, 'batch size')
tf.app.flags.DEFINE_integer('epoch', 1, 'epoch')
tf.app.flags.DEFINE_integer('hidden_dim', 128, 'hidden dim')
tf.app.flags.DEFINE_integer('embedding_dim', 128, 'embedding dim')
tf.app.flags.DEFINE_float('teacher_forcing_rate', 0.5, 'teacher forcing rate')
tf.app.flags.DEFINE_float('dropout_rate', 0.5, 'dropout rate')
tf.app.flags.DEFINE_float('learning_rate', 0.001, 'learning rate')
tf.app.flags.DEFINE_string('data_path', './data/poem_data.json', 'data path')
tf.app.flags.DEFINE_string('embedding_path', './data/embedding_matrix.npy', 'embeding path')
tf.app.flags.DEFINE_string('check_point', './check_point', 'chech_point')

Usage

Install requirements

pip install -r requirements.txt

Training model

python main.py

Predict

For prediction, you should put one sentence used by input

example

python predict.py 맨발로 거친 산길을 오르는 나의 발바닥은 돌멩이에 찢겨 나뭇가지에 찢겨

Generated Poem Example

Input: 맨발로 거친 산길을 오르는 나의 발바닥은 돌멩이에 찢겨 나뭇가지에 찢겨

먹다버린 깨진 콜라병과 눈총과 온갖 쓰레기에 치여
검붉은 피로 멍들어
불혹의 동반자로 떠도는 편지를
그 사이로 일은
곱게 물째 은행잎을 바라보지

Reference Paper

• Neural Machine Translation by Jointly Learning to Align and Translate
• Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Support

Send any bug reports to the below e-mail.

[email protected]

I hope this tool can help your practically.