📄 mlpinit.htm
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<html><head><title>Netlab Reference Manual mlpinit</title></head><body><H1> mlpinit</H1><h2>Purpose</h2>Initialise the weights in a 2-layer feedforward network.<p><h2>Synopsis</h2><PRE>net = mlpinit(net, prior)</PRE><p><h2>Description</h2><p><CODE>net = mlpinit(net, prior)</CODE> takes a 2-layer feedforward network<CODE>net</CODE> and sets the weights and biases by sampling from a Gaussiandistribution. If <CODE>prior</CODE> is a scalar, then all of the parameters(weights and biases) are sampled from a single isotropic Gaussian withinverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a datastructure of the kind generated by <CODE>mlpprior</CODE>, then the parametersare sampled from multiple Gaussians according to their groupings(defined by the <CODE>index</CODE> field) with corresponding variances(defined by the <CODE>alpha</CODE> field).<p><h2>See Also</h2><CODE><a href="mlp.htm">mlp</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE>, <CODE><a href="mlppak.htm">mlppak</a></CODE>, <CODE><a href="mlpunpak.htm">mlpunpak</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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