[y, extra] = glmevfwd(net, x, t, x_test) [y, extra, invhess] = glmevfwd(net, x, t, x_test, invhess)
y = glmevfwd(net, x, t, x_test) takes a network data structure
net together with the input x and target t training data
and input test data x_test.
It returns the normal forward propagation through the network y
together with a matrix extra which consists of error bars (variance)
for a regression problem or moderated outputs for a classification problem.
The optional argument (and return value)
invhess is the inverse of the network Hessian
computed on the training data inputs and targets. Passing it in avoids
recomputing it, which can be a significant saving for large training sets.
fevbayesCopyright (c) Ian T Nabney (1996-9)