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<html><head><title>Netlab Reference Manual mdnfwd</title></head><body><H1> mdnfwd</H1><h2>Purpose</h2>Forward propagation through Mixture Density Network.<p><h2>Synopsis</h2><PRE>mixparams = mdnfwd(net, x)[mixparams, y, z] = mdnfwd(net, x)[mixparams, y, z, a] = mdnfwd(net, x)</PRE><p><h2>Description</h2><CODE>mixparams = mdnfwd(net, x)</CODE> takes a mixture density network datastructure <CODE>net</CODE> and a matrix <CODE>x</CODE> of input vectors, and forwardpropagates the inputs through the network to generate a structure<CODE>mixparams</CODE> which contains the parameters of several mixture models. Each row of <CODE>x</CODE> representsone input vector and the corresponding row of the matrices in <CODE>mixparams</CODE> represents the parameters of a mixture model for the conditional probabilityof target vectors given the input vector. This is not represented as an arrayof <CODE>gmm</CODE> structures to improve the efficiency of MDN training.<p>The fields in <CODE>mixparams</CODE> are<PRE> type = 'mdnmixes' ncentres = number of mixture components dimtarget = dimension of target space mixcoeffs = mixing coefficients centres = means of Gaussians: stored as one row per pattern covars = covariances of Gaussians nparams = number of parameters</PRE><p><CODE>[mixparams, y, z] = mdnfwd(net, x)</CODE> also generates a matrix <CODE>y</CODE> ofthe outputs of the MLP and a matrix <CODE>z</CODE> of the hiddenunit activations where each row corresponds to one pattern.<p><CODE>[mixparams, y, z, a] = mlpfwd(net, x)</CODE> also returns a matrix <CODE>a</CODE> giving the summed inputs to each output unit, where each row corresponds to one pattern.<p><h2>See Also</h2><CODE><a href="mdn.htm">mdn</a></CODE>, <CODE><a href="mdn2gmm.htm">mdn2gmm</a></CODE>, <CODE><a href="mdnerr.htm">mdnerr</a></CODE>, <CODE><a href="mdngrad.htm">mdngrad</a></CODE>, <CODE><a href="mlpfwd.htm">mlpfwd</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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