g = mlpgrad(net, x, t)
g = mlpgrad(net, x, t) takes a network data structure net
together with a matrix x of input vectors and a matrix t
of target vectors, and evaluates the gradient g of the error
function with respect to the network weights. The error funcion
corresponds to the choice of output unit activation function. Each row
of x corresponds to one input vector and each row of t
corresponds to one target vector.
[g, gdata, gprior] = mlpgrad(net, x, t) also returns separately
the data and prior contributions to the gradient. In the case of
multiple groups in the prior, gprior is a matrix with a row
for each group and a column for each weight parameter.
mlp, mlppak, mlpunpak, mlpfwd, mlperr, mlpbkpCopyright (c) Ian T Nabney (1996-9)