From: lindahl@cse.arl.utexas.edu (Charlie Lindahl)
Subject: Demos for training & analysis of mapping NNs from UTexas@Arlington
Organization: Computer Science Engineering Univ. of Texas at Arlington
Distribution: comp
Date: Mon, 16 Nov 1992 04:55:32 GMT

All: 

I'm posting demos for an Electrical Engineering professor here 
at UTexas Arlington (Dr. Mike Manry). Below is the description for the
demos related to MAPPING. The ABSTRACT file appears below. 

I've posted on me.uta.edu (anon ftp) under /pub/neural/uta-nn-ee.tar
(this is a TAR file containing ZIP files, and hence should be transferred
IN BINARY MODE. Interested parties can EMAIL me as to why this file wouldn't
COMPRESS). 

Download procedure: 

1) Use ANONYMOUS FTP in BINARY mode. 
2) Use the UNIX TAR program to unpack into two ZIP files. 
3) Use the PKUNZIP PC program to unpack onto the PC. 

PLEASE don't send EMAIL directly to me, or post on this bboard (as 
I don't frequent this regularly); rather, send EMAIL directly to 
Dr. Manry at B496MTM@UTARLG.UTA.EDU. 

Charlie Lindahl
Electrical Engineering Dept
University of Texas at Arlington
EMAIL  :     lindahl@cse.uta.edu
------------------------------------------------------------------------
File Neumap.zip   Size; 206,746 bytes

Five demo programs are included, for the training and analysis of 
multilayer perceptron (MLP) neural networks for mapping. Such 
networks have sigmoidal hidden units and linear activations in 
the output units. The enclosed programs are demos in that the 
training data file must have exactly 300 training patterns, and 
only 4 or fewer inputs are allowed.

Program BPmap designs MLP mapping networks using backpropagation
(BP). Nonstandard features in this program are (1) batching of weight 
changes is allowed, (2) The learning factor is adaptive, and 
(3) the importance of each input feature is calculated. Mean-squared 
error (MSE) is calculated and printed for each iteration. The 
Relative Root Mean-squared error (RRMSE) is calculated and printed
after the data has been processed.

Program Inmap designs MLPs using a fast technique. Networks are 
often better than those from BPmap and can be designed one to two 
orders of magnitude faster. The RRMSE is calculated and printed 
after the data has been processed.

Program Modc analyzes MLP networks trained via BPmap or Inmap. 
Less useful units can be pruned under user control.
Program Test3 applies a trained MLP to a data file, and writes the 
output layer signals and the desired outputs to a user-chosen disk
file. The RRMSE is calculated and printed after the data has been
processed.

Program Watecon converts the weight file from Inmap or BPmap to 
an easily understood formatted form, or vice versa. This should 
allow users to process networks from other neural net design 
packages, and vice versa.




 
--
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"If Unix is the answer, surely    | Charlie S. Lindahl
we've forgotten the question." -  | lindahl@cse.uta.edu
Anonymous                         | Electrical Engineering Dept
                                  | University of Texas at Arlington
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Disclaimer: If my employer shares these views, I'd be most surprised.














Newsgroups: comp.ai.neural-nets