# mlpevfwd

## Purpose

Forward propagation with evidence for MLP

## Synopsis

    [y, extra] = mlpevfwd(net, x, t, x_test)
    [y, extra, invhess] = mlpevfwd(net, x, t, x_test, invhess)

## Description

`y = mlpevfwd(net, x, t, x_test)` takes a network data structure `net` together with the input `x` and target `t` training data and input test data `x_test`. It returns the normal forward propagation through the network `y` together with a matrix `extra` which consists of error bars (variance) for a regression problem or moderated outputs for a classification problem. The optional argument (and return value) `invhess` is the inverse of the network Hessian computed on the training data inputs and targets. Passing it in avoids recomputing it, which can be a significant saving for large training sets.

## See Also

`fevbayes`

Pages: Index

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