mlp_teach_rprop: Rprop teaching

In FCNN4R: Fast Compressed Neural Networks for R

Description

Rprop is a fast and robust adaptive step method based on backpropagation. For details, please refer to the original paper given in References section.

Usage

mlp_teach_rprop(net, input, output, tol_level, max_epochs, l2reg = 0,
  u = 1.2, d = 0.5, gmax = 50, gmin = 1e-06, report_freq = 0)

Arguments

net	an object of mlp_net class
input	numeric matrix, each row corresponds to one input vector, the number of columns must be equal to the number of neurons in the network input layer
output	numeric matrix with rows corresponding to expected outputs, the number of columns must be equal to the number of neurons in the network output layer, the number of rows must be equal to the number of input rows
tol_level	numeric value, error (MSE) tolerance level
max_epochs	integer value, maximal number of epochs (iterations)
l2reg	numeric value, L2 regularization parameter (default 0)
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)
report_freq	integer value, progress report frequency, if set to 0 no information is printed on the console (this is the default)

Value

Two-element list, the first field (net) contains the trained network, the second (mse) - the learning history (MSE in consecutive epochs).

References

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

FCNN4R documentation built on May 29, 2017, 4:26 p.m.