📄 somtrain.htm
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<html><head><title>Netlab Reference Manual somtrain</title></head><body><H1> somtrain</H1><h2>Purpose</h2>Kohonen training algorithm for SOM.<p><h2>Synopsis</h2><PRE>net = somtrain{net, options, x)</PRE><p><h2>Description</h2><CODE>net = somtrain{net, options, x)</CODE> uses Kohonen's algorithm totrain a SOM. Both on-line and batch algorithms are implemented.The learning rate (for on-line) and neighbourhood size decay linearly.There is no error function minimised during training (so there isno termination criterion other than the number of epochs), but the sum-of-squares is computed and returned in <CODE>options(8)</CODE>.<p>The optional parameters have the following interpretations.<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs learningrate <CODE>alpha</CODE> and neighbourhood size <CODE>nsize</CODE>.Otherwise nothing is displayed.<p><CODE>options(5)</CODE> determines whether the patterns are sampled randomlywith replacement. If it is 0 (the default), then patterns are sampledin order. This is only relevant to the on-line algorithm.<p><CODE>options(6)</CODE> determines if the on-line or batch algorithm isused. If it is 1then the batch algorithm is used. If it is 0(the default) then the on-line algorithm is used.<p><CODE>options(14)</CODE> is the maximum number of iterations (passes throughthe complete pattern set); default 100.<p><CODE>options(15)</CODE> is the final neighbourhood size; default value is thesame as the initial neighbourhood size.<p><CODE>options(16)</CODE> is the final learning rate; default value is the sameas the initial learning rate.<p><CODE>options(17)</CODE> is the initial neighbourhood size; default 0.5*maximummap size.<p><CODE>options(18)</CODE> is the initial learning rate; default 0.9. This parametermust be positive.<p><h2>Examples</h2>The following example performs on-line training on a SOM in two stages:ordering and convergence.<PRE>net = som(nin, [8, 7]);options = foptions;<p>% Ordering phaseoptions(1) = 1;options(14) = 50;options(18) = 0.9; % Initial learning rateoptions(16) = 0.05; % Final learning rateoptions(17) = 8; % Initial neighbourhood sizeoptions(15) = 1; % Final neighbourhood sizenet2 = somtrain(net, options, x);<p>% Convergence phaseoptions(14) = 400;options(18) = 0.05;options(16) = 0.01;options(17) = 0;options(15) = 0;net3 = somtrain(net2, options, x);</PRE><p><h2>See Also</h2><CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somfwd.htm">somfwd</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>⌨️ 快捷键说明
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