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<html><head><title>Netlab Reference Manual mlp</title></head><body><H1> mlp</H1><h2>Purpose</h2>Create a 2-layer feedforward network.<p><h2>Synopsis</h2><PRE>net = mlp(nin, nhidden, nout, func)net = mlp(nin, nhidden, nout, func, prior)net = mlp(nin, nhidden, nout, func, prior, beta)</PRE><p><h2>Description</h2><CODE>net = mlp(nin, nhidden, nout, func)</CODE> takes the number of inputs, hidden units and output units for a 2-layer feed-forward network,together with a string <CODE>func</CODE> which specifies the output unitactivation function, and returns a data structure <CODE>net</CODE>. Theweights are drawn from a zero mean, unit variance isotropic Gaussian,with varianced scaled by the fan-in of the hidden or output units asappropriate. 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 hidden units use the <CODE>tanh</CODE> activation function.<p>The fields in <CODE>net</CODE> are<PRE> type = 'mlp' nin = number of inputs nhidden = number of hidden units 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 w2 = second-layer weight matrix b2 = second-layer bias vector</PRE>Here <CODE>w1</CODE> has dimensions <CODE>nin</CODE> times <CODE>nhidden</CODE>, <CODE>b1</CODE> hasdimensions <CODE>1</CODE> times <CODE>nhidden</CODE>, <CODE>w2</CODE> hasdimensions <CODE>nhidden</CODE> times <CODE>nout</CODE>, and <CODE>b2</CODE> hasdimensions <CODE>1</CODE> times <CODE>nout</CODE>.<p><CODE>net = mlp(nin, nhidden, 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 Gaussianprior with inverse variance with value <CODE>prior</CODE>. Alternatively,<CODE>prior</CODE> can consist of a data structure with fields <CODE>alpha</CODE>and <CODE>index</CODE>, allowing individual Gaussian priors to be set overgroups of weights in the network. Here <CODE>alpha</CODE> is a column vectorin which each element corresponds to a separate group of weights,which need not be mutually exclusive. The membership of the groups isdefined by the matrix <CODE>indx</CODE> in which the columns correspond tothe elements of <CODE>alpha</CODE>. Each column has one element for eachweight in the matrix, in the order defined by the function<CODE>mlppak</CODE>, and each element is 1 or 0 according to whether theweight is a member of the corresponding group or not. A utilityfunction <CODE>mlpprior</CODE> is provided to help in setting up the<CODE>prior</CODE> data structure.<p><CODE>net = mlp(nin, nhidden, 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="mlpprior.htm">mlpprior</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>, <CODE><a href="mlpgrad.htm">mlpgrad</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>?? 快捷鍵說明
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