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https://github.com/szcom/rnnlib

RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/
https://github.com/szcom/rnnlib

Last synced: about 1 month ago
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RNNLIB is a recurrent neural network library for sequence learning problems. Forked from Alex Graves work http://sourceforge.net/projects/rnnl/

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README 

          
# Origin



The original RNNLIB is hosted at http://sourceforge.net/projects/rnnl

while this "fork" is created to repeat results for the 

online handwriting prediction and synthesis reported in 

http://arxiv.org/abs/1308.0850. The later by now is Alex Graves's classic 

paper on LSTM networks showing of what RNN can learn about the

structure present in the sequential input.



# Building



Building rnnlib requires the following:



* C++11 compiler

* fortran for building OpenBLAS

* cmake

* libcurl

* automake

* libtool

* texinfo



In addition, the following python packages are needed for the auxiliary scripts in the 'utils' directory:



* SciPy

* PyLab

* PIL



And this package is needed to create and manipulate netcdf data files with python, and to run the experiments in the 'examples' directory:



* ScientificPython (NOT Scipy)



To build RNNLIB do



$ cmake -DCMAKE_BUILD_TYPE=Release .

$ cmake --build .



Cmake run creates the binary files 'rnnlib', 'rnnsynth' and 'gradient_check' in the current directory. 



It is recommended that you add the directory containing the 'rnnlib' binary to your path,

as otherwise the tools in the 'utilities' directory will not work.



Project files for the integrated development environments can be generated by cmake. Run cmake --help

to get list of supported IDEs.



 

# Handwriting synthesis



Step in to examples/online_prediction and go through few steps below to prepare the 

training data, train the model and eventually plot the results of the synthesis



## Downloading online handwriting dataset



Start by registering and downloading pen strokes data from 

http://www.iam.unibe.ch/~fkiwww/iamondb/data/lineStrokes-all.tar.gz

Text lables for strokes can be found here

http://www.iam.unibe.ch/~fkiwww/iamondb/data/ascii-all.tar.gz

Then unzip ./lineStrokes and ./ascii under examples/online_prediction.

Data format in the downloaded files can not be used as is 

and requires further preprocessing to convert pen coordinates to offsets from

previous point and merge them into the single file of netcdf format.



## Preparing the training data



Run ./build_netcdf.sh to split dataset to training and validation sets. 

The same script does all necessary preprocessing including normalisation

of the input and makes corresponding online.nc and online_validation.nc 

files for use with rnnlib .



Each point in the input sequences from online.nc consists of three numbers: 

the x and y offset from the previous point, and the binary end-of-stroke feature.



## Gradient check



To gain some confidence that the build is fine run the gradient check:



gradient_check --autosave=false check_synth2.config



## Training



The training goes in two steps. First it is done without weights regularization

and then repeated again with adaptive weight noise (MDL in rnnlib terms) from the

best network recorded by step one. Training with MDL from the beginning will have

too slow convergence rate.



### Step 1



rnnlib --verbose=false  synth1d.config



Where synth1d.config is 1st step configuration file that defines network topology:

3 LSTM hidden layers of 400 cells, 20 gaussian mixtures as output layer, 10 mixtures

for character warping window layer

Somewhere between training epoch 10-15 it will find optimal solution and will do

"early stopping" w/o improvement for 20 epoch. "Early" here takes 3 days on Intel

Sandybridge CPU. Normally training can be stopped as long as loss starts rising up

for 2-3 consequent epochs.

The best solution found is stored in synth1d@



### Step 2



Best loss error from step 1 is expected to be around -1080 nats and it can be further

improved (ca. 10%) by using weights regularisation. Loss error goes up and down during the

training unlike in Step 1. Therefore one must be more patient to declare early stopping and 

wait for 20 epochs with loss worse then the best result so far. Rnnlib has implementation

of MDL regulariser which is used in this step. The command line is as following:



rnnlib --mdl=true --mdlOptimiser=rmsprop from_step1.best_loss.save



### Synthesis



Handwriting synthesis is done by rnnsynth binary using network parameters obtained by

step 2:



rnnsynth from_step2.best_loss.save



The character sequence is given to stdin and output is written to stdout. The output sequence

is the same as input where each data point has x,y offsets and end-of-stroke flag.



### Plotting the results



Rnnsynth output is the sequence of x,y offsets and end-of-stroke flags. To visualise it one

can use show_pen.m Octave script:



octave:>show_pen('/tmp/trace1')



Where /tmp/trace1 contains stdout from rnnsynth.



### Rnnlib configuration file



Configuration options are exlained in http://sourceforge.net/p/rnnl/wiki/Home/. Since then

there are few things added:

* lstm1d as hiddenType layer type - optimised LSTM layer when input dimension is 1d

* rmsprop optimizer type

* mixtures=N where N is number of gaussians in the output layer

* charWindowSize=N where N is the number of gaussians in the character window layer

* skipConnections=true|false - whether to add skip connections; default is true



# Contact



Please create github issues to discuss the problems