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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 glm
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5 </title>
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6 </head>
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7 <body>
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8 <H1> glm
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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 Create a generalized linear model.
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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 net = glm(nin, nout, func)
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20 net = glm(nin, nout, func, prior)
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21 net = glm(nin, nout, func, prior, beta)
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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
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29 <p><CODE>net = glm(nin, nout, func)</CODE> takes the number of inputs
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30 and outputs for a generalized linear model, together
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31 with a string <CODE>func</CODE> which specifies the output unit activation function,
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32 and returns a data structure <CODE>net</CODE>. The weights are drawn from a zero mean,
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33 isotropic Gaussian, with variance scaled by the fan-in of the
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34 output units. This makes use of the Matlab function
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35 <CODE>randn</CODE> and so the seed for the random weight initialization can be
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36 set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. The optional
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37 argument <CODE>alpha</CODE> sets the inverse variance for the weight
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38 initialization.
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39
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40 <p>The fields in <CODE>net</CODE> are
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41 <PRE>
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42 type = 'glm'
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43 nin = number of inputs
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44 nout = number of outputs
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45 nwts = total number of weights and biases
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46 actfn = string describing the output unit activation function:
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47 'linear'
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48 'logistic'
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49 'softmax'
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50 w1 = first-layer weight matrix
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51 b1 = first-layer bias vector
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52 </PRE>
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53
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54
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55 <p><CODE>net = glm(nin, nout, func, prior)</CODE>, in which <CODE>prior</CODE> is
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56 a scalar, allows the field
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57 <CODE>net.alpha</CODE> in the data structure <CODE>net</CODE> to be set, corresponding
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58 to a zero-mean isotropic Gaussian prior with inverse variance with
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59 value <CODE>prior</CODE>. Alternatively, <CODE>prior</CODE> can consist of a data
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60 structure with fields <CODE>alpha</CODE> and <CODE>index</CODE>, allowing individual
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61 Gaussian priors to be set over groups of weights in the network. Here
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62 <CODE>alpha</CODE> is a column vector in which each element corresponds to a
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63 separate group of weights, which need not be mutually exclusive. The
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64 membership of the groups is defined by the matrix <CODE>index</CODE> in which
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65 the columns correspond to the elements of <CODE>alpha</CODE>. Each column has
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66 one element for each weight in the matrix, in the order defined by the
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67 function <CODE>glmpak</CODE>, and each element is 1 or 0 according to whether
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68 the weight is a member of the corresponding group or not.
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69
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70 <p><CODE>net = glm(nin, nout, func, prior, beta)</CODE> also sets the
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71 additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, where
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72 beta corresponds to the inverse noise variance.
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73
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74 <p><h2>
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75 See Also
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76 </h2>
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77 <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr>
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78 <b>Pages:</b>
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79 <a href="index.htm">Index</a>
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80 <hr>
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81 <p>Copyright (c) Ian T Nabney (1996-9)
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82
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83
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84 </body>
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85 </html> |
