diff toolboxes/FullBNT-1.0.7/netlab3.3/mlphdotv.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/mlphdotv.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,79 @@
+function hdv = mlphdotv(net, x, t, v)
+%MLPHDOTV Evaluate the product of the data Hessian with a vector. 
+%
+%	Description
+%
+%	HDV = MLPHDOTV(NET, X, T, V) takes an MLP network data structure NET,
+%	together with the matrix X of input vectors, the matrix T of target
+%	vectors and an arbitrary row vector V whose length equals the number
+%	of parameters in the network, and returns the product of the data-
+%	dependent contribution to the Hessian matrix with V. The
+%	implementation is based on the R-propagation algorithm of
+%	Pearlmutter.
+%
+%	See also
+%	MLP, MLPHESS, HESSCHEK
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x, t);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = size(x, 1);
+
+[y, z] = mlpfwd(net, x);		% Standard forward propagation.
+zprime = (1 - z.*z);			% Hidden unit first derivatives.
+zpprime = -2.0*z.*zprime;		% Hidden unit second derivatives.
+
+vnet = mlpunpak(net, v);	% 		Unpack the v vector.
+
+% Do the R-forward propagation.
+
+ra1 = x*vnet.w1 + ones(ndata, 1)*vnet.b1;
+rz = zprime.*ra1;
+ra2 = rz*net.w2 + z*vnet.w2 + ones(ndata, 1)*vnet.b2;
+
+switch net.outfn
+
+  case 'linear'      % Linear outputs
+
+    ry = ra2;
+
+  case 'logistic'    % Logistic outputs
+
+    ry = y.*(1 - y).*ra2;
+
+  case 'softmax'     % Softmax outputs
+  
+    nout = size(t, 2);
+    ry = y.*ra2 - y.*(sum(y.*ra2, 2)*ones(1, nout));
+
+  otherwise
+    error(['Unknown activation function ', net.outfn]);  
+end
+
+% Evaluate delta for the output units.
+
+delout = y - t;
+
+% Do the standard backpropagation.
+
+delhid = zprime.*(delout*net.w2');
+
+% Now do the R-backpropagation.
+
+rdelhid = zpprime.*ra1.*(delout*net.w2') + zprime.*(delout*vnet.w2') + ...
+          zprime.*(ry*net.w2');
+
+% Finally, evaluate the components of hdv and then merge into long vector.
+
+hw1 = x'*rdelhid;
+hb1 = sum(rdelhid, 1);
+hw2 = z'*ry + rz'*delout;
+hb2 = sum(ry, 1);
+
+hdv = [hw1(:)', hb1, hw2(:)', hb2];