Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/fevbayes.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess) | |
2 %FEVBAYES Evaluate Bayesian regularisation for network forward propagation. | |
3 % | |
4 % Description | |
5 % EXTRA = FEVBAYES(NET, Y, A, X, T, X_TEST) takes a network data | |
6 % structure NET together with a set of hidden unit activations A from | |
7 % test inputs X_TEST, training data inputs X and T and outputs a matrix | |
8 % of extra information EXTRA that consists of error bars (variance) for | |
9 % a regression problem or moderated outputs for a classification | |
10 % problem. The optional argument (and return value) INVHESS is the | |
11 % inverse of the network Hessian computed on the training data inputs | |
12 % and targets. Passing it in avoids recomputing it, which can be a | |
13 % significant saving for large training sets. | |
14 % | |
15 % This is called by network-specific functions such as MLPEVFWD which | |
16 % are needed since the return values (predictions and hidden unit | |
17 % activations) for different network types are in different orders (for | |
18 % good reasons). | |
19 % | |
20 % See also | |
21 % MLPEVFWD, RBFEVFWD, GLMEVFWD | |
22 % | |
23 | |
24 % Copyright (c) Ian T Nabney (1996-2001) | |
25 | |
26 w = netpak(net); | |
27 g = netderiv(w, net, x_test); | |
28 if nargin < 7 | |
29 % Need to compute inverse hessian | |
30 hess = nethess(w, net, x, t); | |
31 invhess = inv(hess); | |
32 end | |
33 | |
34 ntest = size(x_test, 1); | |
35 var = zeros(ntest, 1); | |
36 for idx = 1:1:net.nout, | |
37 for n = 1:1:ntest, | |
38 grad = squeeze(g(n,:,idx)); | |
39 var(n,idx) = grad*invhess*grad'; | |
40 end | |
41 end | |
42 | |
43 switch net.outfn | |
44 case 'linear' | |
45 % extra is variance | |
46 extra = ones(size(var))./net.beta + var; | |
47 case 'logistic' | |
48 % extra is moderated output | |
49 kappa = 1./(sqrt(ones(size(var)) + (pi.*var)./8)); | |
50 extra = 1./(1 + exp(-kappa.*a)); | |
51 case 'softmax' | |
52 % Use extended Mackay formula; beware that this may not | |
53 % be very accurate | |
54 kappa = 1./(sqrt(ones(size(var)) + (pi.*var)./8)); | |
55 temp = exp(kappa.*a); | |
56 extra = temp./(sum(temp, 2)*ones(1, net.nout)); | |
57 otherwise | |
58 error(['Unknown activation function ', net.outfn]); | |
59 end |