mlp_teach_grprop: Rprop teaching - minimising arbitrary objective function
In FCNN4R: Fast Compressed Neural Networks for R

Description Usage Arguments Value References Examples

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.

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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)

`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)

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).

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

## 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)