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<html><head><title>Netlab Reference Manual glmgrad</title></head><body><H1> glmgrad</H1><h2>Purpose</h2>Evaluate gradient of error function for generalized linear model.<p><h2>Synopsis</h2><PRE>g = glmgrad(net, x, t)[g, gdata, gprior] = glmgrad(net, x, t)</PRE><p><h2>Description</h2><CODE>g = glmgrad(net, x, t)</CODE> takes a generalized linear modeldata 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 functioncorresponds 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] = glmgrad(net, x, t)</CODE> also returns separately the data and prior contributions to the gradient.<p><h2>See Also</h2><CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</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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