training.report: Neural network training report generator function.
In AMORE: Artificial Neural Network Training and Simulating
Description
Usage
Arguments
Value
Author(s)
References
See Also
Description
Function in charge of reporting the behavior of the network training. The users should modify this function according to their needs.
Usage
1
training.report(net,P,T, idx.show, error.criterium)
Arguments
net
Neural Network to train.
P
Training set input values.
T
Training set output values
idx.show
Current show index.
error.criterium
Criterium used to measure the goodness of fit.
Value
This function does not return any value. Just useful for printing and plotting.
Author(s)
Manuel Castejón Limas. manuel.castejon@gmail.com
Joaquin Ordieres Meré. j.ordieres@upm.es
Ana González Marcos. ana.gonzalez@unirioja.es
Alpha V. Pernía Espinoza. alpha.pernia@unirioja.es
Francisco Javier Martinez de Pisón. fjmartin@unirioja.es
Fernando Alba Elías. fernando.alba@unavarra.es
References
Simon Haykin. Neural Networks – a Comprehensive Foundation. Prentice Hall, New Jersey, 2nd edition, 1999. ISBN 0-13-273350-1.
See Also
AMORE documentation built on Feb. 12, 2020, 9:07 a.m.
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Description Usage Arguments Value Author(s) References See Also
Description
Function in charge of reporting the behavior of the network training. The users should modify this function according to their needs.
Usage
1 | training.report(net,P,T, idx.show, error.criterium)
|
Arguments
net |
Neural Network to train. |
P |
Training set input values. |
T |
Training set output values |
idx.show |
Current show index. |
error.criterium |
Criterium used to measure the goodness of fit. |
Value
This function does not return any value. Just useful for printing and plotting.
Author(s)
Manuel Castejón Limas. manuel.castejon@gmail.com
Joaquin Ordieres Meré. j.ordieres@upm.es
Ana González Marcos. ana.gonzalez@unirioja.es
Alpha V. Pernía Espinoza. alpha.pernia@unirioja.es
Francisco Javier Martinez de Pisón. fjmartin@unirioja.es
Fernando Alba Elías. fernando.alba@unavarra.es
References
Simon Haykin. Neural Networks – a Comprehensive Foundation. Prentice Hall, New Jersey, 2nd edition, 1999. ISBN 0-13-273350-1.
See Also
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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