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<html><head><title>Netlab Reference Manual glm</title></head><body><H1> glm</H1><h2>Purpose</h2>Create a generalized linear model.<p><h2>Synopsis</h2><PRE>net = glm(nin, nout, func)net = glm(nin, nout, func, prior)net = glm(nin, nout, func, prior, beta)</PRE><p><h2>Description</h2><p><CODE>net = glm(nin, nout, func)</CODE> takes the number of inputsand outputs for a generalized linear model, togetherwith a string <CODE>func</CODE> which specifies the output unit activation function,and returns a data structure <CODE>net</CODE>. The weights are drawn from a zero mean,isotropic Gaussian, with variance scaled by the fan-in of theoutput units. This makes use of the Matlab function<CODE>randn</CODE> and so the seed for the random weight initialization can be set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. The optionalargument <CODE>alpha</CODE> sets the inverse variance for the weightinitialization.<p>The fields in <CODE>net</CODE> are<PRE> type = 'glm' nin = number of inputs nout = number of outputs nwts = total number of weights and biases actfn = string describing the output unit activation function: 'linear' 'logistic' 'softmax' w1 = first-layer weight matrix b1 = first-layer bias vector</PRE><p><CODE>net = glm(nin, nout, func, prior)</CODE>, in which <CODE>prior</CODE> isa scalar, allows the field <CODE>net.alpha</CODE> in the data structure <CODE>net</CODE> to be set, corresponding to a zero-mean isotropic Gaussian prior with inverse variance withvalue <CODE>prior</CODE>. Alternatively, <CODE>prior</CODE> can consist of a datastructure with fields <CODE>alpha</CODE> and <CODE>index</CODE>, allowing individualGaussian priors to be set over groups of weights in the network. Here<CODE>alpha</CODE> is a column vector in which each element corresponds to a separate group of weights, which need not be mutually exclusive. Themembership of the groups is defined by the matrix <CODE>index</CODE> in whichthe columns correspond to the elements of <CODE>alpha</CODE>. Each column hasone element for each weight in the matrix, in the order defined by thefunction <CODE>glmpak</CODE>, and each element is 1 or 0 according to whetherthe weight is a member of the corresponding group or not.<p><CODE>net = glm(nin, nout, func, prior, beta)</CODE> also sets the additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, wherebeta corresponds to the inverse noise variance.<p><h2>See Also</h2><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="glmgrad.htm">glmgrad</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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