# mlpgrad

## Purpose

Evaluate gradient of error function for 2-layer network.

## Synopsis

    g = mlpgrad(net, x, t)

## Description

`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.

## See Also

`mlp`, `mlppak`, `mlpunpak`, `mlpfwd`, `mlperr`, `mlpbkp`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)