Neural Monkey

Neural Sequence Learning Using TensorFlow

The Neural Monkey package provides a higher level abstraction for sequential
neural network models, most prominently in Natural Language Processing (NLP).
It is built on TensorFlow. It can be used for fast
prototyping of sequential models in NLP which can be used e.g. for neural
machine translation or sentence classification.

The higher-level API brings together a collection of standard building blocks
(RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new
building blocks implemented directly in TensorFlow.

Usage

bash
neuralmonkey-train <EXPERIMENT_INI>
neuralmonkey-run <EXPERIMENT_INI> <DATASETS_INI>
neuralmonkey-server <EXPERIMENT_INI> [OPTION] ...
neuralmonkey-logbook --logdir <EXPERIMENTS_DIR> [OPTION] ...

Installation

You need Python 3.6 (or higher) to run Neural Monkey.

When using virtual environment, execute these commands to install the Python
dependencies:

bash
$ source path/to/virtualenv/bin/activate

# For GPU-enabled version
(virtualenv)$ pip install --upgrade -r requirements-gpu.txt

# For CPU-only version
(virtualenv)$ pip install --upgrade -r requirements.txt

If you are using the GPU version, make sure that the LD_LIBRARY_PATH
environment variable points to lib and lib64 directories of your CUDA and
CuDNN installations. Similarly, your PATH variable should point to the bin
subdirectory of the CUDA installation directory.
If the training crashes on an unknown dependency, just install it with
pip. Remember to keep your virtual environment up-to-date with the package
requirements file, which may be changed over time. To update the dependencies,
re-run the pip install command from above (pay attention to the distinction
between GPU and non-GPU versions).

Getting Started

There is a
tutorial that
you can follow, which gives you the overwiev of how to design your experiments
with Neural Monkey.

Package Overview

bin: Directory with neuralmonkey executables
examples: Example configuration files for ready-made experiments
lib: Third party software
neuralmonkey: Python package files
scripts: Directory with tools that may come in handy. Note dependencies for
these tools may not be listed in the project requirements.
tests: Test files

Documentation

You can find the API documentation of this package
here. The documentation files
are generated from docstrings using
autodoc and
Napoleon extensions
to the Python documentation package
Sphinx. The docstrings should follow
the recommendations in the Google Python Style
Guide.
Additional details on the docstring formatting can be found in the Napoleon
documentation as well.

tflearn – a more general and less
abstract deep learning toolkit built over TensorFlow
nlpnet – deep learning tools for
tagging and parsing
NNBlocks – a library build over Theano
containing NLP specific models
Nematus - A tool for training and
running Neural Machine Translation models
seq2seq - a general-purpose
encoder-decoder framework for Tensorflow
OpenNMT - open sourcce NMT in Torch

Citation

If you use the tool for academic purporses, please consider citing
the following paper:

bib
@article{NeuralMonkey:2017,
    author = {Jind{\v{r}}ich Helcl and Jind{\v{r}}ich Libovick{\'{y}}},
    title = {{Neural Monkey: An Open-source Tool for Sequence Learning}},
    journal = {The Prague Bulletin of Mathematical Linguistics},
    year = {2017},
    address = {Prague, Czech Republic},
    number = {107},
    pages = {5--17},
    issn = {0032-6585},
    doi = {10.1515/pralin-2017-0001},
    url = {http://ufal.mff.cuni.cz/pbml/107/art-helcl-libovicky.pdf}
}