diff toolboxes/FullBNT-1.0.7/netlab3.3/mlpbkp.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/mlpbkp.m	Tue Feb 10 15:05:51 2015 +0000
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+function g = mlpbkp(net, x, z, deltas)
+%MLPBKP	Backpropagate gradient of error function for 2-layer network.
+%
+%	Description
+%	G = MLPBKP(NET, X, Z, DELTAS) takes a network data structure NET
+%	together with a matrix X of input vectors, a matrix  Z of hidden unit
+%	activations, and a matrix DELTAS of the  gradient of the error
+%	function with respect to the values of the output units (i.e. the
+%	summed inputs to the output units, before the activation function is
+%	applied). The return value is the gradient G of the error function
+%	with respect to the network weights. Each row of X corresponds to one
+%	input vector.
+%
+%	This function is provided so that the common backpropagation
+%	algorithm can be used by multi-layer perceptron network models to
+%	compute gradients for mixture density networks as well as standard
+%	error functions.
+%
+%	See also
+%	MLP, MLPGRAD, MLPDERIV, MDNGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Evaluate second-layer gradients.
+gw2 = z'*deltas;
+gb2 = sum(deltas, 1);
+
+% Now do the backpropagation.
+delhid = deltas*net.w2';
+delhid = delhid.*(1.0 - z.*z);
+
+% Finally, evaluate the first-layer gradients.
+gw1 = x'*delhid;
+gb1 = sum(delhid, 1);
+
+g = [gw1(:)', gb1, gw2(:)', gb2];