06 Nov 2002
02 Dec 2002)
Pattern analysis toolbox.
|mlpgrad(net, x, t)
function [g, gdata, gprior] = mlpgrad(net, x, t)
%MLPGRAD Evaluate gradient of error function for 2-layer network.
% G = MLPGRAD(NET, X, T) takes a network data structure NET together
% with a matrix X of input vectors and a matrix T of target vectors,
% and evaluates the gradient G of the error function with respect to
% the network weights. The error funcion corresponds to the choice of
% output unit activation function. Each row of X corresponds to one
% input vector and each row of T corresponds to one target vector.
% [G, GDATA, GPRIOR] = MLPGRAD(NET, X, T) also returns separately the
% data and prior contributions to the gradient. In the case of multiple
% groups in the prior, GPRIOR is a matrix with a row for each group and
% a column for each weight parameter.
% See also
% MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP
% Copyright (c) Ian T Nabney (1996-2001)
% Check arguments for consistency
errstring = consist(net, 'mlp', x, t);
[y, z] = mlpfwd(net, x);
delout = y - t;
gdata = mlpbkp(net, x, z, delout);
[g, gdata, gprior] = gbayes(net, gdata);