diff options
| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/graddesc.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
Diffstat (limited to 'sourcecodes/bnt-master/nethelp3.3/graddesc.htm')
| -rw-r--r-- | sourcecodes/bnt-master/nethelp3.3/graddesc.htm | 104 |
1 files changed, 104 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/nethelp3.3/graddesc.htm b/sourcecodes/bnt-master/nethelp3.3/graddesc.htm new file mode 100644 index 00000000..fe8a3df1 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/graddesc.htm @@ -0,0 +1,104 @@ +<html> +<head> +<title> +Netlab Reference Manual graddesc +</title> +</head> +<body> +<H1> graddesc +</H1> +<h2> +Purpose +</h2> +Gradient descent optimization. + +<p><h2> +Description +</h2> +<CODE>[x, options, flog, pointlog] = graddesc(f, x, options, gradf)</CODE> uses +batch gradient descent to find a local minimum of the function +<CODE>f(x)</CODE> whose gradient is given by <CODE>gradf(x)</CODE>. A log of the function values +after each cycle is (optionally) returned in <CODE>errlog</CODE>, and a log +of the points visited is (optionally) returned in <CODE>pointlog</CODE>. + +<p>Note that <CODE>x</CODE> is a row vector +and <CODE>f</CODE> returns a scalar value. +The point at which <CODE>f</CODE> has a local minimum +is returned as <CODE>x</CODE>. The function value at that point is returned +in <CODE>options(8)</CODE>. + +<p><CODE>graddesc(f, x, options, gradf, p1, p2, ...)</CODE> allows +additional arguments to be passed to <CODE>f()</CODE> and <CODE>gradf()</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>, and the points visited +in the return argument <CODE>pointslog</CODE>. If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(2)</CODE> is the absolute precision required for the value +of <CODE>x</CODE> at the solution. If the absolute difference between +the values of <CODE>x</CODE> between two successive steps is less than +<CODE>options(2)</CODE>, then this condition is satisfied. + +<p><CODE>options(3)</CODE> is a measure of the precision required of the objective +function at the solution. If the absolute difference between the +objective function values between two successive steps is less than +<CODE>options(3)</CODE>, then this condition is satisfied. +Both this and the previous condition must be +satisfied for termination. + +<p><CODE>options(7)</CODE> determines the line minimisation method used. If it +is set to 1 then a line minimiser is used (in the direction of the negative +gradient). If it is 0 (the default), then each parameter update +is a fixed multiple (the learning rate) +of the negative gradient added to a fixed multiple (the momentum) of +the previous parameter update. + +<p><CODE>options(9)</CODE> should be set to 1 to check the user defined gradient +function <CODE>gradf</CODE> with <CODE>gradchek</CODE>. This is carried out at +the initial parameter vector <CODE>x</CODE>. + +<p><CODE>options(10)</CODE> returns the total number of function evaluations (including +those in any line searches). + +<p><CODE>options(11)</CODE> returns the total number of gradient evaluations. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p><CODE>options(15)</CODE> is the precision in parameter space of the line search; +default <CODE>foptions(2)</CODE>. + +<p><CODE>options(17)</CODE> is the momentum; default 0.5. It should be scaled by the +inverse of the number of data points. + +<p><CODE>options(18)</CODE> is the learning rate; default 0.01. It should be +scaled by the inverse of the number of data points. + +<p><h2> +Examples +</h2> +An example of how this function can be used to train a neural network is: +<PRE> + +options = zeros(1, 18); +options(17) = 0.1/size(x, 1); +net = netopt(net, options, x, t, 'graddesc'); +</PRE> + +Note how the learning rate is scaled by the number of data points. + +<p><h2> +See Also +</h2> +<CODE><a href="conjgrad.htm">conjgrad</a></CODE>, <CODE><a href="linemin.htm">linemin</a></CODE>, <CODE><a href="olgd.htm">olgd</a></CODE>, <CODE><a href="minbrack.htm">minbrack</a></CODE>, <CODE><a href="quasinew.htm">quasinew</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 |