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<title>
Netlab Reference Manual netevfwd
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<H1> netevfwd
</H1>
<h2>
Purpose
</h2>
Generic forward propagation with evidence for network
<p><h2>
Synopsis
</h2>
<PRE>
[y, extra] = netevfwd(w, net, x, t, x_test)
[y, extra, invhess] = netevfwd(w, net, x, t, x_test, invhess)
</PRE>
<p><h2>
Description
</h2>
<CODE>[y, extra] = netevfwd(w, net, x, t, x_test)</CODE> takes a network data
structure
<CODE>net</CODE> together with the input <CODE>x</CODE> and target <CODE>t</CODE> training data
and input test data <CODE>x_test</CODE>.
It returns the normal forward propagation through the network <CODE>y</CODE>
together with a matrix <CODE>extra</CODE> which consists of error bars (variance)
for a regression problem or moderated outputs for a classification problem.
<p>The optional argument (and return value)
<CODE>invhess</CODE> 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.
<p><h2>
See Also
</h2>
<CODE><a href="mlpevfwd.htm">mlpevfwd</a></CODE>, <CODE><a href="rbfevfwd.htm">rbfevfwd</a></CODE>, <CODE><a href="glmevfwd.htm">glmevfwd</a></CODE>, <CODE><a href="fevbayes.htm">fevbayes</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
<hr>
<p>Copyright (c) Ian T Nabney (1996-9)
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