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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual rbfgrad | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> rbfgrad | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Evaluate gradient of error function for RBF network. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 | |
20 g = rbfgrad(net, x, t) | |
21 [g, gdata, gprior] = rbfgrad(net, x, t) | |
22 </PRE> | |
23 | |
24 | |
25 <p><h2> | |
26 Description | |
27 </h2> | |
28 <CODE>g = rbfgrad(net, x, t)</CODE> takes a network data structure <CODE>net</CODE> | |
29 together with a matrix <CODE>x</CODE> of input | |
30 vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates the gradient | |
31 <CODE>g</CODE> of the error function with respect to the network weights (i.e. | |
32 including the hidden unit parameters). The error | |
33 function is sum of squares. | |
34 Each row of <CODE>x</CODE> corresponds to one | |
35 input vector and each row of <CODE>t</CODE> contains the corresponding target vector. | |
36 If the output function is <CODE>'neuroscale'</CODE> then the gradient is only | |
37 computed for the output layer weights and biases. | |
38 | |
39 <p><CODE>[g, gdata, gprior] = rbfgrad(net, x, t)</CODE> also returns separately | |
40 the data and prior contributions to the gradient. In the case of | |
41 multiple groups in the prior, <CODE>gprior</CODE> is a matrix with a row | |
42 for each group and a column for each weight parameter. | |
43 | |
44 <p><h2> | |
45 See Also | |
46 </h2> | |
47 <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr> | |
48 <b>Pages:</b> | |
49 <a href="index.htm">Index</a> | |
50 <hr> | |
51 <p>Copyright (c) Ian T Nabney (1996-9) | |
52 | |
53 | |
54 </body> | |
55 </html> |