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<html><head><title>Netlab Reference Manual mlpgrad</title></head><body><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 errorfunction with respect to the network weights. The error funcioncorresponds to the choice of output unit activation function. Each rowof <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 ofmultiple groups in the prior, <CODE>gprior</CODE> is a matrix with a rowfor 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)</body></html>⌨️ 快捷键说明
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