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 |
