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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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+++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/netopt.htm	Tue Feb 10 15:05:51 2015 +0000
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+<html>
+<head>
+<title>
+Netlab Reference Manual netopt
+</title>
+</head>
+<body>
+<H1> netopt
+</H1>
+<h2>
+Purpose
+</h2>
+Optimize the weights in a network model. 
+
+<p><h2>
+Synopsis
+</h2>
+<PRE>
+[net, options] = netopt(net, options, x, t, alg)
+[net, options, varargout] = netopt(net, options, x, t, alg)
+</PRE>
+
+
+<p><h2>
+Description
+</h2>
+
+<p><CODE>netopt</CODE> is a helper function which facilitates the training of 
+networks using the general purpose optimizers as well as sampling from the
+posterior distribution of parameters using general purpose Markov chain
+Monte Carlo sampling algorithms. It can be used with any function that
+searches in parameter space using error and gradient functions.
+
+<p><CODE>[net, options] = netopt(net, options, x, t, alg)</CODE> takes a network 
+data structure <CODE>net</CODE>, together with a vector <CODE>options</CODE> of
+parameters governing the behaviour of the optimization algorithm, a
+matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of target
+vectors, and returns the trained network as well as an updated
+<CODE>options</CODE> vector. The string <CODE>alg</CODE> determines which optimization
+algorithm (<CODE>conjgrad</CODE>, <CODE>quasinew</CODE>, <CODE>scg</CODE>, etc.) or Monte
+Carlo algorithm (such as <CODE>hmc</CODE>) will be used.
+
+<p><CODE>[net, options, varargout] = netopt(net, options, x, t, alg)</CODE>
+also returns any additional return values from the optimisation algorithm.
+
+<p><h2>
+Examples
+</h2>
+Suppose we create a 4-input, 3 hidden unit, 2-output feed-forward
+network using <CODE>net = mlp(4, 3, 2, 'linear')</CODE>. We can then train
+the network with the scaled conjugate gradient algorithm by using
+<CODE>net = netopt(net, options, x, t, 'scg')</CODE> where <CODE>x</CODE> and
+<CODE>t</CODE> are the input and target data matrices respectively, and the
+options vector is set appropriately for <CODE>scg</CODE>.
+
+<p>If we also wish to plot the learning curve, we can use the additional
+return value <CODE>errlog</CODE> given by <CODE>scg</CODE>:
+<PRE>
+
+[net, options, errlog] = netopt(net, options, x, t, 'scg');
+</PRE>
+
+
+<p><h2>
+See Also
+</h2>
+<CODE><a href="netgrad.htm">netgrad</a></CODE>, <CODE><a href="bfgs.htm">bfgs</a></CODE>, <CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="graddesc.htm">graddesc</a></CODE>, <CODE><a href="hmc.htm">hmc</a></CODE>, <CODE><a href="scg.htm">scg</a></CODE><hr>
+<b>Pages:</b>
+<a href="index.htm">Index</a>
+<hr>
+<p>Copyright (c) Ian T Nabney (1996-9)
+
+
+</body>
+</html>
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