📄 netopt.htm
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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 theposterior distribution of parameters using general purpose Markov chainMonte Carlo sampling algorithms. It can be used with any function thatsearches 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> ofparameters governing the behaviour of the optimization algorithm, amatrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE> of targetvectors, and returns the trained network as well as an updated<CODE>options</CODE> vector. The string <CODE>alg</CODE> determines which optimizationalgorithm (<CODE>conjgrad</CODE>, <CODE>quasinew</CODE>, <CODE>scg</CODE>, etc.) or MonteCarlo 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-forwardnetwork using <CODE>net = mlp(4, 3, 2, 'linear')</CODE>. We can then trainthe 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 theoptions vector is set appropriately for <CODE>scg</CODE>.<p>If we also wish to plot the learning curve, we can use the additionalreturn 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>⌨️ 快捷键说明
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