wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual mlpgrad wolffd@0: wolffd@0: wolffd@0: wolffd@0:

mlpgrad wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Evaluate gradient of error function for 2-layer network. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: 
wolffd@0: g = mlpgrad(net, x, t)
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wolffd@0: Description wolffd@0:

wolffd@0: g = mlpgrad(net, x, t) takes a network data structure net wolffd@0: together with a matrix x of input vectors and a matrix t wolffd@0: of target vectors, and evaluates the gradient g of the error wolffd@0: function with respect to the network weights. The error funcion wolffd@0: corresponds to the choice of output unit activation function. Each row wolffd@0: of x corresponds to one input vector and each row of t wolffd@0: corresponds to one target vector. wolffd@0: wolffd@0:

[g, gdata, gprior] = mlpgrad(net, x, t) also returns separately wolffd@0: the data and prior contributions to the gradient. In the case of wolffd@0: multiple groups in the prior, gprior is a matrix with a row wolffd@0: for each group and a column for each weight parameter. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: mlp, mlppak, mlpunpak, mlpfwd, mlperr, mlpbkp

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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