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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual mlpinit
5 </title>
6 </head>
7 <body>
8 <H1> mlpinit
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Initialise the weights in a 2-layer feedforward network.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 net = mlpinit(net, prior)
20 </PRE>
21
22
23 <p><h2>
24 Description
25 </h2>
26
27 <p><CODE>net = mlpinit(net, prior)</CODE> takes a 2-layer feedforward network
28 <CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
29 distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters
30 (weights and biases) are sampled from a single isotropic Gaussian with
31 inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data
32 structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters
33 are sampled from multiple Gaussians according to their groupings
34 (defined by the <CODE>index</CODE> field) with corresponding variances
35 (defined by the <CODE>alpha</CODE> field).
36
37 <p><h2>
38 See Also
39 </h2>
40 <CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE><hr>
41 <b>Pages:</b>
42 <a href="index.htm">Index</a>
43 <hr>
44 <p>Copyright (c) Ian T Nabney (1996-9)
45
46
47 </body>
48 </html>