blob: 2f7dbd4e25a2b468b146c2e4f03decc410fd1833 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
|
<html>
<head>
<title>
Netlab Reference Manual mlpinit
</title>
</head>
<body>
<H1> mlpinit
</H1>
<h2>
Purpose
</h2>
Initialise the weights in a 2-layer feedforward network.
<p><h2>
Synopsis
</h2>
<PRE>
net = mlpinit(net, prior)
</PRE>
<p><h2>
Description
</h2>
<p><CODE>net = mlpinit(net, prior)</CODE> takes a 2-layer feedforward network
<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters
(weights and biases) are sampled from a single isotropic Gaussian with
inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data
structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters
are sampled from multiple Gaussians according to their groupings
(defined by the <CODE>index</CODE> field) with corresponding variances
(defined by the <CODE>alpha</CODE> field).
<p><h2>
See Also
</h2>
<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>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
</body>
</html>
|
