wolffd@0: function net = mlpunpak(net, w)
wolffd@0: %MLPUNPAK Separates weights vector into weight and bias matrices. 
wolffd@0: %
wolffd@0: %	Description
wolffd@0: %	NET = MLPUNPAK(NET, W) takes an mlp network data structure NET and  a
wolffd@0: %	weight vector W, and returns a network data structure identical to
wolffd@0: %	the input network, except that the first-layer weight matrix W1, the
wolffd@0: %	first-layer bias vector B1, the second-layer weight matrix W2 and the
wolffd@0: %	second-layer bias vector B2 have all been set to the corresponding
wolffd@0: %	elements of W.
wolffd@0: %
wolffd@0: %	See also
wolffd@0: %	MLP, MLPPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD
wolffd@0: %
wolffd@0: 
wolffd@0: %	Copyright (c) Ian T Nabney (1996-2001)
wolffd@0: 
wolffd@0: % Check arguments for consistency
wolffd@0: errstring = consist(net, 'mlp');
wolffd@0: if ~isempty(errstring);
wolffd@0:   error(errstring);
wolffd@0: end
wolffd@0: 
wolffd@0: if net.nwts ~= length(w)
wolffd@0:   error('Invalid weight vector length')
wolffd@0: end
wolffd@0: 
wolffd@0: nin = net.nin;
wolffd@0: nhidden = net.nhidden;
wolffd@0: nout = net.nout;
wolffd@0: 
wolffd@0: mark1 = nin*nhidden;
wolffd@0: net.w1 = reshape(w(1:mark1), nin, nhidden);
wolffd@0: mark2 = mark1 + nhidden;
wolffd@0: net.b1 = reshape(w(mark1 + 1: mark2), 1, nhidden);
wolffd@0: mark3 = mark2 + nhidden*nout;
wolffd@0: net.w2 = reshape(w(mark2 + 1: mark3), nhidden, nout);
wolffd@0: mark4 = mark3 + nout;
wolffd@0: net.b2 = reshape(w(mark3 + 1: mark4), 1, nout);