Attention is a useful machenism in NMT. Recently, the paper "Attention ia all you need" shows a new network architecture which based solely on attention machenisms. I have two goals with this project. One is to have a full understanding of attention machenism and the paper "Effective Approaches to Attention-based Neural Machine Translation". Another is to implement "local attention" by using tensorflow, since I didn't find the corresponding function in tensorflow.

• data_utils.py data preparation, the same one with Tensorflow: tutorials/rnn/translate/data_tuils.py    
• seq2seq_model.py the same one with Tensorflow: tutorials/rnn/translate/data_tuils.py    
• translate.py the same one with Tensorflow: tutorials/rnn/translate/data_tuils.py    
• seq2seq_local.py attention decoeder function. Implement the "local attention" in
  function "local_attention_decoder". Another attention function,
  implements to calculate the new alignment weights with Gaussian
  distribution but not sets the window, is named "local_attention_decodere_nowindow".
  Function "attention_decoder" is the original one in tensorflow.

Just use the same command line in Tensorflow tutorial:   python translate.py  

--data_dir [your_data_directory] --train_dir [checkpoints_directory]

--en_vocab_size=40000 --fr_vocab_size=40000