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<html><head><title>Netlab Reference Manual mdngrad</title></head><body><H1> mdngrad</H1><h2>Purpose</h2>Evaluate gradient of error function for Mixture Density Network.<p><h2>Synopsis</h2><PRE>g = mdngrad(net, x, t)</PRE><p><h2>Description</h2><CODE>g = mdngrad(net, x, t)</CODE> takes a mixture density network datastructure <CODE>net</CODE>, 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 theerror function with respect to the network weights. The error functionis negative log likelihood of the target data. Each row of <CODE>x</CODE>corresponds to one input vector and each row of <CODE>t</CODE> corresponds toone target vector.<p><h2>See Also</h2><CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdnfwd.htm">mdnfwd</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdnprob.htm">mdnprob</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)<p>David J Evans (1998)</body></html>⌨️ 快捷键说明
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