FCNN4R (version 0.6.2)

mlp_teach_grprop: Rprop teaching - minimising arbitrary objective function

Description

This implementation (`generalisation') of the Rprop algorithm allows users to teach network to minimise arbitrary objective function provided that functions evaluating objective and computing gradient are provided.

Usage

mlp_teach_grprop(net, obj_func, gradient, epochs, stop = NULL, report_freq = 0, report_action = NULL, u = 1.2, d = 0.5, gmax = 50, gmin = 1e-06)

Arguments

net

an object of mlp_net class

obj_func

function taking an object of mlp_class class as a single argument returning objective to be minimised

gradient

function taking an object of mlp_class class as a single argument returning gradient of the objective

epochs

integer value, number of epochs (iterations)

stop

function (or NULL), a function taking objective history to date and returning Boolean value (if TRUE is returned, algorithm stops) (the default is not to stop until all iterations are performed)

report_freq

integer value, progress report frequency, if set to 0 no information is printed on the console (this is the default)

report_action

function (or NULL), additional action to be taken while printing progress reports, this should be a function taking network as a single argument (default NULL)

u

numeric value, Rprop algorithm parameter (default 1.2)

d

numeric value, Rprop algorithm parameter (default 0.5)

gmax

numeric value, Rprop algorithm parameter (default 50)

gmin

numeric value, Rprop algorithm parameter (default 1e-6)

Value

Two-element list, the first field (net) contains the trained network, the second (obj) - the learning history (value of the objective function in consecutive epochs).

References

M. Riedmiller. Rprop - Description and Implementation Details: Technical Report. Inst. f. Logik, Komplexitat u. Deduktionssysteme, 1994.

Examples

## Not run: 
# # set up XOR problem
# inp <- c(0, 0, 1, 1, 0, 1, 0, 1)
# dim(inp) <- c(4, 2)
# outp <- c(0, 1, 1, 0)
# dim(outp) <- c(4, 1)
# # objective
# obj <- function(net)
# {
#     return(mlp_mse(net, inp, outp))
# }
# # gradient
# grad <- function(net)
# {
#     return(mlp_grad(net, inp, outp)$grad)
# }
# # stopping citerion
# tol <- function(oh) {
#     if (oh[length(oh)] <= 5e-5) { return(TRUE); }
#     return(FALSE)
# }
# # create a 2-6-1 network
# net <- mlp_net(c(2, 6, 1))
# # set activation function in all layers
# net <- mlp_set_activation(net, layer = "a", "sigmoid")
# # randomise weights
# net <- mlp_rnd_weights(net)
# # teach
# netobj <- mlp_teach_grprop(net, obj, grad, epochs = 500,
#                            stop = tol,
#                            report_freq = 1)
# # plot learning history
# plot(netobj$obj, type = 'l')
# ## End(Not run)