Dynamic Memory Networks (https://arxiv.org/abs/1603.01417) in Tensorflow
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README.md

Dynamic Memory Networks in Tensorflow

DMN Structure

Implementation of Dynamic Memory Networks for Visual and Textual Question Answering on the bAbI question answering tasks using Tensorflow.

Prerequisites

  • Python 3.x
  • Tensorflow 0.8+
  • Numpy
  • tqdm - Progress bar module

Usage

First, You need to install dependencies.

sudo pip install tqdm
git clone https://github.com/therne/dmn-tensorflow & cd dmn-tensorflow

Then download the dataset:

mkdir data
curl -O http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz
tar -xzf tasks_1-20_v1-2.tar.gz -C data/

If you want to run original DMN (models/old/dmn.py), you also need to download GloVe word embedding data.

curl -O http://nlp.stanford.edu/data/glove.6B.zip
unzip glove.6B.zip -d data/glove/

Training the model

./main.py --task [bAbi Task Number]

Testing the model

./main.py --test --task [Task Number]

Results

Trained 20 times and picked best results - using DMN+ model trained with paper settings (Batch 128, 3 episodes, 80 hidden, L2) + batch normalization. The skipped tasks achieved 0 error.

Task Error Rate
  1. Two supporting facts | 25.1%
  2. Three supporting facts | (N/A)
  3. Three arguments relations | 1.1%
  4. Compound coreference | 1.5%
  5. Time reasoning | 0.8%
  6. Basic induction | 52.3%
  7. Positional reasoning | 13.1%
  8. Size reasoning | 6.1%
  9. Path finding | 3.5% Average | 5.1%

Overfitting occurs in some tasks and error rate is higher than the paper's result. I think we need some additional regularizations.

References

To-do

  • More regularizations and hyperparameter tuning
  • Visual question answering
  • Attention visualization
  • Interactive mode?