function [y, z, a] = mlpfwd(net, x)%MLPFWD	Forward propagation through 2-layer network.%%	Description%	Y = MLPFWD(NET, X) takes a network data structure NET together with a%	matrix X of input vectors, and forward propagates the inputs through%	the network to generate a matrix Y of output vectors. Each row of X%	corresponds to one input vector and each row of Y corresponds to one%	output vector.%%	[Y, Z] = MLPFWD(NET, X) also generates a matrix Z of the hidden unit%	activations where each row corresponds to one pattern.%%	[Y, Z, A] = MLPFWD(NET, X) also returns a matrix A  giving the summed%	inputs to each output unit, where each row corresponds to one%	pattern.%%	See also%	MLP, MLPPAK, MLPUNPAK, MLPERR, MLPBKP, MLPGRAD%%	Copyright (c) Ian T Nabney (1996-2001)% Check arguments for consistencyerrstring = consist(net, 'mlp', x);if ~isempty(errstring);  error(errstring);endndata = size(x, 1);z = tanh(x*net.w1 + ones(ndata, 1)*net.b1);a = z*net.w2 + ones(ndata, 1)*net.b2;switch net.outfn  case 'linear'    % Linear outputs    y = a;  case 'logistic'  % Logistic outputs    % Prevent overflow and underflow: use same bounds as mlperr    % Ensure that log(1-y) is computable: need exp(a) > eps    maxcut = -log(eps);    % Ensure that log(y) is computable    mincut = -log(1/realmin - 1);    y = 1./(1 + exp(-a));  case 'softmax'   % Softmax outputs      % Prevent overflow and underflow: use same bounds as glmerr    % Ensure that sum(exp(a), 2) does not overflow    maxcut = log(realmax) - log(net.nout);    % Ensure that exp(a) > 0    mincut = log(realmin);    a = min(a, maxcut);    a = max(a, mincut);    temp = exp(a);    y = temp./(sum(temp, 2)*ones(1, net.nout));  otherwise    error(['Unknown activation function ', net.outfn]);  end