comparison toolboxes/FullBNT-1.0.7/netlab3.3/mlpderiv.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 g = mlpderiv(net, x)
2 %MLPDERIV Evaluate derivatives of network outputs with respect to weights.
3 %
4 % Description
5 % G = MLPDERIV(NET, X) takes a network data structure NET and a matrix
6 % of input vectors X and returns a three-index matrix G whose I, J, K
7 % element contains the derivative of network output K with respect to
8 % weight or bias parameter J for input pattern I. The ordering of the
9 % weight and bias parameters is defined by MLPUNPAK.
10 %
11 % See also
12 % MLP, MLPPAK, MLPGRAD, MLPBKP
13 %
14
15 % Copyright (c) Ian T Nabney (1996-2001)
16
17 % Check arguments for consistency
18 errstring = consist(net, 'mlp', x);
19 if ~isempty(errstring);
20 error(errstring);
21 end
22
23 [y, z] = mlpfwd(net, x);
24
25 ndata = size(x, 1);
26
27 if isfield(net, 'mask')
28 nwts = size(find(net.mask), 1);
29 temp = zeros(1, net.nwts);
30 else
31 nwts = net.nwts;
32 end
33
34 g = zeros(ndata, nwts, net.nout);
35 for k = 1 : net.nout
36 delta = zeros(1, net.nout);
37 delta(1, k) = 1;
38 for n = 1 : ndata
39 if isfield(net, 'mask')
40 temp = mlpbkp(net, x(n, :), z(n, :), delta);
41 g(n, :, k) = temp(logical(net.mask));
42 else
43 g(n, :, k) = mlpbkp(net, x(n, :), z(n, :),...
44 delta);
45 end
46 end
47 end