function [g, gdata, gprior] = mlpgrad(net, x, t)%MLPGRAD Evaluate gradient of error function for 2-layer network.%%	Description%	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 consistencyerrstring = consist(net, 'mlp', x, t);if ~isempty(errstring);  error(errstring);end[y, z] = mlpfwd(net, x);delout = y - t;gdata = mlpbkp(net, x, z, delout);[g, gdata, gprior] = gbayes(net, gdata);