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