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1 function [g, gdata, gprior] = mlpgrad(net, x, t)
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2 %MLPGRAD Evaluate gradient of error function for 2-layer network.
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3 %
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4 % Description
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5 % G = MLPGRAD(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 gradient G of the error function with respect to
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8 % the network weights. The error funcion corresponds to the choice of
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9 % output unit activation function. Each row of X corresponds to one
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10 % input vector and each row of T corresponds to one target vector.
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11 %
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12 % [G, GDATA, GPRIOR] = MLPGRAD(NET, X, T) also returns separately the
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13 % data and prior contributions to the gradient. In the case of multiple
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14 % groups in the prior, GPRIOR is a matrix with a row for each group and
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15 % a column for each weight parameter.
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16 %
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17 % See also
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18 % MLP, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP
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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] = mlpfwd(net, x);
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29 delout = y - t;
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30
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31 gdata = mlpbkp(net, x, z, delout);
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32
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33 [g, gdata, gprior] = gbayes(net, gdata);
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