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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/mlp.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/mlp.htm b/sourcecodes/bnt-master/nethelp3.3/mlp.htm new file mode 100644 index 00000000..b8237991 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/mlp.htm @@ -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 |