Implementation of the Incremental Sequence Learning algorithms described in the Incremental Sequence Learning article.

#Requirements Python 3.5

Tensorflow 0.9

#Getting started Parameter files for the first 3 experiments described in the article are available as exp/exp1a..d, exp/exp2a..d, and exp/exp3a..d. The a, b, c, and d variant represent the four different configurations compared in the article.

To start a run for experiment 1a, use:

./runrnn exp1a --runnr 1

#Data This project makes use of the MNIST stroke sequence data set, available here:

https://github.com/edwin-de-jong/mnist-digits-stroke-sequence-data/wiki/MNIST-digits-stroke-sequence-data

#Results

I have included the R scripts used to extract results from the output files. To process the results, you can use:

source('R/process.R')

source('R/processruns.R')

binsize = 1000

requiredfraction = .9 #fraction of the files required to be available for reporting output

windowsize = 1

folder = '~/code/digits/rnn'

exp1atrain = processruns( 'exp1a', 'train', 1, binsize, windowsize, folder, requiredfraction )

exp1atest = processruns( 'exp1a', 'test', 1, binsize, windowsize, folder, requiredfraction )

#Acknowledgements

The network architecture used in this work is based on the article Generating Sequences With Recurrent Neural Networks by Alex Graves.

The implementation is based on the write-rnn-tensorflow by hardmaru, which in turn is based on the char-rnn-tensorflow implementation by sherjilozair. See the blog post Handwriting Generation Demo in TensorFlow.