[net, options] = netopt(net, options, x, t, alg) [net, options, varargout] = netopt(net, options, x, t, alg)
netopt is a helper function which facilitates the training of
networks using the general purpose optimizers as well as sampling from the
posterior distribution of parameters using general purpose Markov chain
Monte Carlo sampling algorithms. It can be used with any function that
searches in parameter space using error and gradient functions.
[net, options] = netopt(net, options, x, t, alg) takes a network
data structure net, together with a vector options of
parameters governing the behaviour of the optimization algorithm, a
matrix x of input vectors and a matrix t of target
vectors, and returns the trained network as well as an updated
options vector. The string alg determines which optimization
algorithm (conjgrad, quasinew, scg, etc.) or Monte
Carlo algorithm (such as hmc) will be used.
[net, options, varargout] = netopt(net, options, x, t, alg)
also returns any additional return values from the optimisation algorithm.
net = mlp(4, 3, 2, 'linear'). We can then train
the network with the scaled conjugate gradient algorithm by using
net = netopt(net, options, x, t, 'scg') where x and
t are the input and target data matrices respectively, and the
options vector is set appropriately for scg.
If we also wish to plot the learning curve, we can use the additional
return value errlog given by scg:
[net, options, errlog] = netopt(net, options, x, t, 'scg');
netgrad, bfgs, conjgrad, graddesc, hmc, scgCopyright (c) Ian T Nabney (1996-9)