MXNet implementation of End-To-End Memory Networks for language modelling. The original Tensorflow code from carpedm20 can be found here.
Known issue: SGD does not converge, ADAM converges but is not able to reach a good result(details).
This code requires MXNet. Also, it uses CUDA to run on GPU for faster training. There is a set of sample Penn Tree Bank (PTB) corpus in data directory, which is a popular benchmark for measuring quality of these models. But you can use your own text data set which should be formated like this.
To train a model with 6 hops and memory size of 100, run the following command:
$ python train.py --nhop 6 --mem_size 100
To see all training options, run:
$ python train.py --help
To test a model, run the script file test.py like:
$ python test.py --network checkpoint/memnn-symbol.json --params checkpoint/memnn-0100.params --gpus 0