Daniel@0: net = glm(nin, nout, func) Daniel@0: net = glm(nin, nout, func, prior) Daniel@0: net = glm(nin, nout, func, prior, beta) Daniel@0:Daniel@0: Daniel@0: Daniel@0:
net = glm(nin, nout, func) takes the number of inputs
Daniel@0: and outputs for a generalized linear model, together
Daniel@0: with a string func which specifies the output unit activation function,
Daniel@0: and returns a data structure net. The weights are drawn from a zero mean,
Daniel@0: isotropic Gaussian, with variance scaled by the fan-in of the
Daniel@0: output units. This makes use of the Matlab function
Daniel@0: randn and so the seed for the random weight initialization can be
Daniel@0: set using randn('state', s) where s is the seed value. The optional
Daniel@0: argument alpha sets the inverse variance for the weight
Daniel@0: initialization.
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The fields in net are
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Daniel@0: type = 'glm' Daniel@0: nin = number of inputs Daniel@0: nout = number of outputs Daniel@0: nwts = total number of weights and biases Daniel@0: actfn = string describing the output unit activation function: Daniel@0: 'linear' Daniel@0: 'logistic' Daniel@0: 'softmax' Daniel@0: w1 = first-layer weight matrix Daniel@0: b1 = first-layer bias vector Daniel@0:Daniel@0: Daniel@0: Daniel@0:
net = glm(nin, nout, func, prior), in which prior is
Daniel@0: a scalar, allows the field
Daniel@0: net.alpha in the data structure net to be set, corresponding
Daniel@0: to a zero-mean isotropic Gaussian prior with inverse variance with
Daniel@0: value prior. Alternatively, prior can consist of a data
Daniel@0: structure with fields alpha and index, allowing individual
Daniel@0: Gaussian priors to be set over groups of weights in the network. Here
Daniel@0: alpha is a column vector in which each element corresponds to a
Daniel@0: separate group of weights, which need not be mutually exclusive. The
Daniel@0: membership of the groups is defined by the matrix index in which
Daniel@0: the columns correspond to the elements of alpha. Each column has
Daniel@0: one element for each weight in the matrix, in the order defined by the
Daniel@0: function glmpak, and each element is 1 or 0 according to whether
Daniel@0: the weight is a member of the corresponding group or not.
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net = glm(nin, nout, func, prior, beta) also sets the
Daniel@0: additional field net.beta in the data structure net, where
Daniel@0: beta corresponds to the inverse noise variance.
Daniel@0:
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glmpak, glmunpak, glmfwd, glmerr, glmgrad, glmtrainCopyright (c) Ian T Nabney (1996-9) Daniel@0: Daniel@0: Daniel@0: Daniel@0: