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first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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wolffd@0 | 1 <html> |
wolffd@0 | 2 <head> |
wolffd@0 | 3 <title> |
wolffd@0 | 4 Netlab Reference Manual netinit |
wolffd@0 | 5 </title> |
wolffd@0 | 6 </head> |
wolffd@0 | 7 <body> |
wolffd@0 | 8 <H1> netinit |
wolffd@0 | 9 </H1> |
wolffd@0 | 10 <h2> |
wolffd@0 | 11 Purpose |
wolffd@0 | 12 </h2> |
wolffd@0 | 13 Initialise the weights in a network. |
wolffd@0 | 14 |
wolffd@0 | 15 <p><h2> |
wolffd@0 | 16 Synopsis |
wolffd@0 | 17 </h2> |
wolffd@0 | 18 <PRE> |
wolffd@0 | 19 net = netinit(net, prior) |
wolffd@0 | 20 </PRE> |
wolffd@0 | 21 |
wolffd@0 | 22 |
wolffd@0 | 23 <p><h2> |
wolffd@0 | 24 Description |
wolffd@0 | 25 </h2> |
wolffd@0 | 26 |
wolffd@0 | 27 <p><CODE>net = netinit(net, prior)</CODE> takes a network data structure |
wolffd@0 | 28 <CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian |
wolffd@0 | 29 distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters |
wolffd@0 | 30 (weights and biases) are sampled from a single isotropic Gaussian with |
wolffd@0 | 31 inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data |
wolffd@0 | 32 structure of the kind generated by <CODE>mlpprior</CODE>, then the parameters |
wolffd@0 | 33 are sampled from multiple Gaussians according to their groupings |
wolffd@0 | 34 (defined by the <CODE>index</CODE> field) with corresponding variances |
wolffd@0 | 35 (defined by the <CODE>alpha</CODE> field). |
wolffd@0 | 36 |
wolffd@0 | 37 <p><h2> |
wolffd@0 | 38 See Also |
wolffd@0 | 39 </h2> |
wolffd@0 | 40 <CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="netunpak.htm">netunpak</a></CODE>, <CODE><a href="rbfprior.htm">rbfprior</a></CODE><hr> |
wolffd@0 | 41 <b>Pages:</b> |
wolffd@0 | 42 <a href="index.htm">Index</a> |
wolffd@0 | 43 <hr> |
wolffd@0 | 44 <p>Copyright (c) Ian T Nabney (1996-9) |
wolffd@0 | 45 |
wolffd@0 | 46 |
wolffd@0 | 47 </body> |
wolffd@0 | 48 </html> |