FCNN4R (version 0.6.2)

mlp_teach_sa: Teaching networks using Simulated Annealing

Description

This function can be used to teach an ANN to minimise arbitrary objective function.

Usage

mlp_teach_sa(net, obj_func, Tinit = 1, epochs = 1000, report_freq = 0, report_action = NULL)

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

Tinit

numeric value, initial temperature (default is 1)

epochs

integer value, number of epochs (iterations) (default is 1000)

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)

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).

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))
# }
# # 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")
# # teach
# netobj <- mlp_teach_sa(net, obj, Tinit = 1, epochs = 1000,
#                        report_freq = 1)
# # plot learning history
# plot(netobj$obj, type = 'l')
# ## End(Not run)

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