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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/netopt.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/netopt.htm b/sourcecodes/bnt-master/nethelp3.3/netopt.htm new file mode 100644 index 00000000..369c959c --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/netopt.htm @@ -0,0 +1,75 @@ +<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> \ No newline at end of file |