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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
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