Dynamic memory networks in Theano
The aim of this repository is to implement Dynamic memory networks as described in the paper by Kumar et al. and to experiment with its various extensions.
Pretrained models on bAbI tasks can be tested online.
We will cover the process in a series of blog posts.
- The first post describes the details of the basic architecture and presents our first results on bAbI tasks v1.2.
- The second post describes our second model called
dmn_smoothand introduces our playground for bAbI tasks.
Repository contents
| file | description |
|---|---|
main.py |
the main entry point to train and test available network architectures on bAbI-like tasks |
dmn_basic.py |
our baseline implementation. It is as close to the original as we could understand the paper, except the number of steps in the main memory GRU is fixed. Attention module uses T.abs_ function as a distance between two vectors which causes gradients to become NaN randomly. The results reported in this blog post are based on this network |
dmn_smooth.py |
uses the square of the Euclidean distance instead of abs in the attention module. Training is very stable. Performance on bAbI is slightly better |
dmn_batch.py |
dmn_smooth with minibatch training support. The batch size cannot be set to 1 because of the Theano bug |
dmn_qa_draft.py |
draft version of a DMN designed for answering multiple choice questions |
utils.py |
tools for working with bAbI tasks and GloVe vectors |
nn_utils.py |
helper functions on top of Theano and Lasagne |
fetch_babi_data.sh |
shell script to fetch bAbI tasks (adapted from MemN2N) |
fetch_glove_data.sh |
shell script to fetch GloVe vectors (by 5vision) |
server/ |
contains Flask-based restful api server |
Usage
This implementation is based on Theano and Lasagne. One way to install them is:
pip install -r https://raw.githubusercontent.com/Lasagne/Lasagne/master/requirements.txt
pip install https://github.com/Lasagne/Lasagne/archive/master.zip
The following bash scripts will download bAbI tasks and GloVe vectors.
./fetch_babi_data.sh
./fetch_glove_data.sh
Use main.py to train a network:
python main.py --network dmn_basic --babi_id 1
The states of the network will be saved in states/ folder.
There is one pretrained state on the 1st bAbI task. It should give 100% accuracy on the test set:
python main.py --network dmn_basic --mode test --babi_id 1 --load_state states/dmn_basic.mh5.n40.babi1.epoch4.test0.00033.state
Server
If you want to start a server which will return the predication for bAbi tasks, you should do the following:
- Generate UI files as described in YerevaNN/dmn-ui
- Copy the UI files to
server/ui - Run the server
cd server && python api.pyIf have Docker installed, you can pull our Docker image with ready DMN server.
docker pull yerevann/docker
docker run --name dmn_1 -it --rm -p 5000:5000 yerevann/dmnRoadmap
- Mini-batch training (done, 08/02/2016)
- Web interface (done, 08/23/2016)
- Visualization of episodic memory module (done, 08/23/2016)
- Regularization (work in progress, L2 doesn't help at all, dropout and batch normalization help a little)
- Support for multiple-choice questions (work in progress)
- Evaluation on more complex datasets
- Import some ideas from Neural Reasoner
License
The MIT License (MIT) Copyright (c) 2016 YerevaNN