This project is an implementation of the Memory Network (see PAPER).
People : David Panou
Organization : UPMC - Master Data Science
- ✅ Embeddings (inputs & memory )
- ✅ Relational Inference
- ◻️ Results and Model Analysis
- ◻️ Handling other entries than OneHot (Look-up Table for pretrained embeddings)
- ◻️ Adding Optim package for better sgd (momentum and others)
python preprocessing/preprocessing.py
You can adjust the level of detail in the generated answer using the level argument. (default is -level 0) -level 1 compute sentence instead of words.
th main.lua -generate_dataset 1
th main.lua -train_mem_net 1 To run with default parameters
th main.lua -train_mem_net 1 -num_mem 10 -feature_dim 174 -voc_size 58
The written parameters above are the default one.
Be careful that : - voc_size is >= to the number of word in the dataset
The different modules can be found under models/*_module.lua
The current main.lua replicates the Basic Question-Answering task described in the paper, but the implementation should be able to handle other tasks.
Results are stored in results
Upcoming