Adaptive Computation Time algorithm in Tensorflow
This repo contains a ACTCell.py file impementing ACT and inheriting from the abstract RNN class in the Tensorflow doccumentation here. It implements the Adaptive Computation Time Algorithm, described in this paper.
Also included in the repo is a model which uses this cell for language modelling on the Penn Tree Bank, a common dataset for evaluating language models. To run the ACT_Training.py file, you will need to download the dataset, which can be found here. The files you need are in the data/ directory.
In order to run the code in this repo you will need to have downloaded one of the daily Tensorflow binaries which you can find on their homepage.
run ACT_Training.py with the following parameters:
* model_size, Default: "small", Size of model to train, either small, medium or large(from the config.py file) * data_path, Default: None, full path to the data directory containing the ptb files * model_path, Default: None, the full path of a pickled dictionary of weights(such as from a previously trained model) saved using saveload.py to reload * weights_dir, Default:None, full directory path to save weights into per epoch
Note - you will probably need some fairly heavy machinery to run the large config. The small model will comfortably run with around 4G of memory, although it will be pretty slow.