Skip to content

zhouziqunzzq/GP-MANN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

18 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

MANN - Multimodal Attention-based Neural Network

This is a part of Bittersweet's Graduation project. This repo implements the MANN framework (brought up by this paper) using TensorFlow.

PIP dependencies

  • tensorflow (or tensorflow-gpu)
  • sklearn
  • numpy
  • matplotlib
  • lxml

Project structure

  • MANN_naive: MANN implemented without any attention

    • using dummy data to test convergence: python train_test.py
    • training: python train.py
    • inference: python inference.py
    • evaluate: python evaluate.py
    • hyper parameters are read from hyper_params.py
    • constants are read from constants.py
  • MANN_SA: MANN implemented with SA(Similarity Attention)

    • using dummy data to test convergence: python train_test.py
    • training: python train.py
    • inference: python inference.py
    • evaluate: python evaluate.py
    • hyper parameters are read from hyper_params.py
    • constants are read from constants.py
  • MANN_TCA: MANN implemented with TCA(Text-Concept Attention)

    • training: python train.py
    • inference: python inference.py
    • evaluate: python evaluate.py
    • hyper parameters are read from hyper_params.py
    • constants are read from constants.py
  • MANN_TCA_cudnn: MANN implemented with TCA(Text-Concept Attention) written with CuDNNLSTM

    • training: python train.py
    • hyper parameters are read from hyper_params.py
    • constants are read from constants.py
  • MANN_TCA_SA: MANN implemented with TCA(Text-Concept Attention) and SA(Similarity Attention)

    • training: python train.py
    • inference: python inference.py
    • evaluate: python evaluate.py
    • hyper parameters are read from hyper_params.py
    • constants are read from constants.py
  • LCS: Least Common Sub-sequence

    • inference: python inference.py
    • evaluate: python evaluate.py
  • TF-IDF: Term Frequency - Inverse Document Frequency

    • inference: python inference.py
    • evaluate: python evaluate.py

See also

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages