# glmgrad

## Purpose

Evaluate gradient of error function for generalized linear model.

## Synopsis

    g = glmgrad(net, x, t)
    [g, gdata, gprior] = glmgrad(net, x, t)

## Description

`g = glmgrad(net, x, t)` takes a generalized linear model 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 function 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] = glmgrad(net, x, t)` also returns separately the data and prior contributions to the gradient.

## See Also

`glm`, `glmpak`, `glmunpak`, `glmfwd`, `glmerr`, `glmtrain`

Pages: Index

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