net = glm(nin, nout, func) net = glm(nin, nout, func, prior) net = glm(nin, nout, func, prior, beta)
net = glm(nin, nout, func) takes the number of inputs
and outputs for a generalized linear model, 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,
isotropic Gaussian, with variance scaled by the fan-in of the
output units. 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 optional
argument alpha sets the inverse variance for the weight
initialization.
The fields in net are
type = 'glm'
nin = number of inputs
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
net = glm(nin, 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 index 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 glmpak, and each element is 1 or 0 according to whether
the weight is a member of the corresponding group or not.
net = glm(nin, nout, func, prior, beta) also sets the
additional field net.beta in the data structure net, where
beta corresponds to the inverse noise variance.
glmpak, glmunpak, glmfwd, glmerr, glmgrad, glmtrainCopyright (c) Ian T Nabney (1996-9)