Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/nethelp3.3/mlp.htm @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/mlp.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,94 @@ +<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 unit +activation function, and returns a data structure <CODE>net</CODE>. The +weights are drawn from a zero mean, unit variance isotropic Gaussian, +with varianced scaled by the fan-in of the hidden or output units as +appropriate. 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> has +dimensions <CODE>1</CODE> times <CODE>nhidden</CODE>, <CODE>w2</CODE> has +dimensions <CODE>nhidden</CODE> times <CODE>nout</CODE>, and <CODE>b2</CODE> has +dimensions <CODE>1</CODE> times <CODE>nout</CODE>. + +<p><CODE>net = mlp(nin, nhidden, nout, func, prior)</CODE>, in which <CODE>prior</CODE> is +a 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 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 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. The membership of the groups is +defined by the matrix <CODE>indx</CODE> in which the columns correspond to +the elements of <CODE>alpha</CODE>. Each column has one element for each +weight in the matrix, in the order defined by the function +<CODE>mlppak</CODE>, and each element is 1 or 0 according to whether the +weight is a member of the corresponding group or not. A utility +function <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>, where +beta 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> \ No newline at end of file