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1 function [e, edata, eprior] = mlperr(net, x, t)
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2 %MLPERR Evaluate error function for 2-layer network.
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3 %
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4 % Description
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5 % E = MLPERR(NET, X, T) takes a network data structure NET together
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6 % with a matrix X of input vectors and a matrix T of target vectors,
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7 % and evaluates the error function E. The choice of error function
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8 % corresponds to the output unit activation function. Each row of X
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9 % corresponds to one input vector and each row of T corresponds to one
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10 % target vector.
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11 %
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12 % [E, EDATA, EPRIOR] = MLPERR(NET, X, T) additionally returns the data
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13 % and prior components of the error, assuming a zero mean Gaussian
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14 % prior on the weights with inverse variance parameters ALPHA and BETA
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15 % taken from the network data structure NET.
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16 %
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17 % See also
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18 % MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPBKP, MLPGRAD
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19 %
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20
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21 % Copyright (c) Ian T Nabney (1996-2001)
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22
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23 % Check arguments for consistency
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24 errstring = consist(net, 'mlp', x, t);
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25 if ~isempty(errstring);
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26 error(errstring);
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27 end
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28 [y, z, a] = mlpfwd(net, x);
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29
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30 switch net.outfn
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31
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32 case 'linear' % Linear outputs
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33 edata = 0.5*sum(sum((y - t).^2));
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34
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35 case 'logistic' % Logistic outputs
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36 % Ensure that log(1-y) is computable: need exp(a) > eps
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37 maxcut = -log(eps);
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38 % Ensure that log(y) is computable
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39 mincut = -log(1/realmin - 1);
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40 a = min(a, maxcut);
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41 a = max(a, mincut);
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42 y = 1./(1 + exp(-a));
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43 edata = - sum(sum(t.*log(y) + (1 - t).*log(1 - y)));
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44
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45 case 'softmax' % Softmax outputs
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46 nout = size(a,2);
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47 % Ensure that sum(exp(a), 2) does not overflow
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48 maxcut = log(realmax) - log(nout);
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49 % Ensure that exp(a) > 0
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50 mincut = log(realmin);
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51 a = min(a, maxcut);
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52 a = max(a, mincut);
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53 temp = exp(a);
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54 y = temp./(sum(temp, 2)*ones(1,nout));
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55 % Ensure that log(y) is computable
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56 y(y<realmin) = realmin;
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57 edata = - sum(sum(t.*log(y)));
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58
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59 otherwise
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60 error(['Unknown activation function ', net.outfn]);
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61 end
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62 [e, edata, eprior] = errbayes(net, edata);
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