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<title>
Netlab Reference Manual mlpgrad
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<H1> mlpgrad
</H1>
<h2>
Purpose
</h2>
Evaluate gradient of error function for 2-layer network.
<p><h2>
Synopsis
</h2>
<PRE>
g = mlpgrad(net, x, t)
</PRE>
<p><h2>
Description
</h2>
<CODE>g = mlpgrad(net, x, t)</CODE> takes a network data structure <CODE>net</CODE>
together with a matrix <CODE>x</CODE> of input vectors and a matrix <CODE>t</CODE>
of target vectors, and evaluates the gradient <CODE>g</CODE> 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 <CODE>x</CODE> corresponds to one input vector and each row of <CODE>t</CODE>
corresponds to one target vector.
<p><CODE>[g, gdata, gprior] = mlpgrad(net, x, t)</CODE> also returns separately
the data and prior contributions to the gradient. In the case of
multiple groups in the prior, <CODE>gprior</CODE> is a matrix with a row
for each group and a column for each weight parameter.
<p><h2>
See Also
</h2>
<CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</a></CODE>, <CODE><a href="mlperr.htm">mlperr</a></CODE>, <CODE><a href="mlpbkp.htm">mlpbkp</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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