Neural Sequence Learning Using Tensorflow

The Neural Monkey package provides a higher level abstraction for sequential models, most prominently in Natural Language Processing (NLP), 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 percetpron) and a simple way of adding new building blocks implemented directly in TensorFlow.

neuralmonkey-train <EXPERIMENT_INI>

• bin: Directory with executables and tools
• examples: Example configuration files for ready-made experiments
• lib: Third party software
• neuralmonkey: Python package files
• tests: Test files

The neuralmonkey package is organized into subpackages as follows:

• encoders: The encoders (get sequences of data and encode them)
• decoders: The decoders (get outputs of encoders and decode them)
• nn: The NN (as in Neural Network components)
• trainers: The trainers (train the whole thing)
• runners: The runners (run the trained thing)
• readers: The readers (read files)
• processors: The pre- and postprocessors (pre- and postprocess data)
• evaluators: The evaluators (used for measuring performance)
• config: The configuration (loading, saving, parsing)
• logbook: The Logbook (server for checking up on experiments)
• tests: The unit tests

• caffe_image_features.py extracts features from images using pre-trained network
• install_caffe.sh installs caffe including prerequisites
• precompute_image_features.py deprecated
• reformat_downloaded_image_features.py deprecated
• tokenize_data.py tokenizes data
• tokenize_persistable.sh manages the tokenize_data script

• Install Tensorflow by following their installation docs here
• Clone this repository and follow the installation procedure outlined in prepare_env.sh. If the training crashes on an unknown dependency, just install it with pip.

• 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

The software is distributed under the BSD License.