Recurrent neural network package for problems of time-series prediction and generation
Copyright (c) 2009-2011, Jun Namikawa firstname.lastname@example.org License: ISC license
This package implements a gradient-based learning algorithm for recurrent neural networks. The package supports (1) both fully connected and sparsely connected networks, (2) both discrete-time neural networks and continuous-time neural networks, (3) training examples of both symbolic data and floating point numbers, (4) multi-threading, and (5) analyzing some characteristics (ex: Lyapunov spectrum, Kullback-Leibler divergence).
=== Installation ===
./autogen.sh' in the current directory to create configure file. Next, type ./configure' and when it finishes, type
make'. This will create rnn-learn', `rnn-generate' and other utility programs.
Run them with the argument `-h' to show the usages of them.
If you wish to install the programs, type
make install'. By default, this will install all the files in /usr/local/bin' or
/usr/local/lib'. You can change the install path with the --prefix' option of the configure script, for instance
--prefix=$HOME' (use ./configure --help' for other options).
=== Requirements ===
Building this package requires a C compiler supporting C99 and Autotools (GNU Autoconf, Automake and Libtool).
In addition, utility scripts in the
src/python' directory require python version 2.5 or later (but not python-3.x). Gnuplot is also needed to run rnn-plot-log' script.
=== Example ===
Here is a sample session.
cd examples echo "import gen_target gen_target.print_sin_curve(500, 20)" | python > target.txt rnn-learn -e 5000 target.txt rnn-generate -n 1000 rnn.dat