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+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 consistency
+errstring = consist(net, 'mlp', x);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+ndata = 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);
+    a = min(a, maxcut);
+    a = max(a, mincut);
+    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