comparison toolboxes/FullBNT-1.0.7/netlab3.3/mlp.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function net = mlp(nin, nhidden, nout, outfunc, prior, beta)
+%MLP	Create a 2-layer feedforward network.
+%
+%	Description
+%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC) takes the number of inputs,
+%	hidden units and output units for a 2-layer feed-forward network,
+%	together with a string FUNC which specifies the output unit
+%	activation function, and returns a data structure NET. 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 RANDN and so the
+%	seed for the random weight initialization can be  set using
+%	RANDN('STATE', S) where S is the seed value.  The hidden units use
+%	the TANH activation function.
+%
+%	The fields in NET are
+%	  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
+%	 Here W1 has dimensions NIN times NHIDDEN, B1 has dimensions 1 times
+%	NHIDDEN, W2 has dimensions NHIDDEN times NOUT, and B2 has dimensions
+%	1 times NOUT.
+%
+%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC, PRIOR), in which PRIOR is a
+%	scalar, allows the field NET.ALPHA in the data structure NET to be
+%	set, corresponding to a zero-mean isotropic Gaussian prior with
+%	inverse variance with value PRIOR. Alternatively, PRIOR can consist
+%	of a data structure with fields ALPHA and INDEX, allowing individual
+%	Gaussian priors to be set over groups of weights in the network. Here
+%	ALPHA 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 INDX in which the
+%	columns correspond to the elements of ALPHA. Each column has one
+%	element for each weight in the matrix, in the order defined by the
+%	function MLPPAK, and each element is 1 or 0 according to whether the
+%	weight is a member of the corresponding group or not. A utility
+%	function MLPPRIOR is provided to help in setting up the PRIOR data
+%	structure.
+%
+%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC, PRIOR, BETA) also sets the
+%	additional field NET.BETA in the data structure NET, where beta
+%	corresponds to the inverse noise variance.
+%
+%	See also
+%	MLPPRIOR, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'mlp';
+net.nin = nin;
+net.nhidden = nhidden;
+net.nout = nout;
+net.nwts = (nin + 1)*nhidden + (nhidden + 1)*nout;
+
+outfns = {'linear', 'logistic', 'softmax'};
+
+if sum(strcmp(outfunc, outfns)) == 0
+  error('Undefined output function. Exiting.');
+else
+  net.outfn = outfunc;
+end
+
+if nargin > 4
+  if isstruct(prior)
+    net.alpha = prior.alpha;
+    net.index = prior.index;
+  elseif size(prior) == [1 1]
+    net.alpha = prior;
+  else
+    error('prior must be a scalar or a structure');
+  end  
+end
+
+net.w1 = randn(nin, nhidden)/sqrt(nin + 1);
+net.b1 = randn(1, nhidden)/sqrt(nin + 1);
+net.w2 = randn(nhidden, nout)/sqrt(nhidden + 1);
+net.b2 = randn(1, nout)/sqrt(nhidden + 1);
+
+if nargin == 6
+  net.beta = beta;
+end