comparison toolboxes/FullBNT-1.0.7/netlab3.3/mlpevfwd.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function [y, extra, invhess] = mlpevfwd(net, x, t, x_test, invhess)
2 %MLPEVFWD Forward propagation with evidence for MLP
3 %
4 % Description
5 % Y = MLPEVFWD(NET, X, T, X_TEST) takes a network data structure NET
6 % together with the input X and target T training data and input test
7 % data X_TEST. It returns the normal forward propagation through the
8 % network Y together with a matrix EXTRA which consists of error bars
9 % (variance) for a regression problem or moderated outputs for a
10 % classification problem. The optional argument (and return value)
11 % INVHESS is the inverse of the network Hessian computed on the
12 % training data inputs and targets. Passing it in avoids recomputing
13 % it, which can be a significant saving for large training sets.
14 %
15 % See also
16 % FEVBAYES
17 %
18
19 % Copyright (c) Ian T Nabney (1996-2001)
20
21 [y, z, a] = mlpfwd(net, x_test);
22 if nargin == 4
23 [extra, invhess] = fevbayes(net, y, a, x, t, x_test);
24 else
25 [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess);
26 end