Dynamic Memory Networks in TensorFlow

DMN+ implementation in TensorFlow for question answering on the bAbI 10k dataset.

Structure and parameters from Dynamic Memory Networks for Visual and Textual Question Answering which is henceforth referred to as Xiong et al.

Adapted from Stanford's cs224d assignment 2 starter code and using methods from Dynamic Memory Networks in Theano for importing the Babi-10k dataset.

Repository Contents

Usage

Install TensorFlow r1.4

Run the included shell script to fetch the data

bash fetch_babi_data.sh

Use 'dmn_train.py' to train the DMN+ model contained in 'dmn_plus.py'

python dmn_train.py --babi_task_id 2

Once training is finished, test the model on a specified task

python dmn_test.py --babi_task_id 2

The l2 regularization constant can be set with -l2-loss (-l). All other parameters were specified by Xiong et al and can be found in the 'Config' class in 'dmn_plus.py'.

Benchmarks

The TensorFlow DMN+ reaches close to state of the art performance on the 10k dataset with weak supervision (no supporting facts).

Each task was trained on separately with l2 = 0.001. As the paper suggests, 10 training runs were used for tasks 2, 3, 17 and 18 (configurable with --num-runs), where the weights which produce the lowest validation loss in any run are used for testing.

The pre-trained weights which achieve these benchmarks are available in 'pretrained'.

I haven't yet had the time to fully optimize the l2 parameter which is not specified by the paper. My hypothesis is that fully optimizing l2 regularization would close the final significant performance gap between the TensorFlow DMN+ and original DMN+ on task 3.

Below are the full results for each bAbI task (tasks where both implementations achieved 0 test error are omitted):