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ENVIIterativeTrainer::Train

ENVIIterativeTrainer::Train

The Train method uses an iterative loop to train a classifier that knows how to update its own weights. The trainer iterates until the classifier's change in loss falls below a specified convergence criterion or it reaches a specified maximum number of iterations. It can also return a loss profile, which can be plotted to examine the convergence of the loss with respect to the training iterations.

See Define and Train the Classifier for more information.

Syntax


ENVIIterativeTrainer.Train, Classifier, Examples [, Keywords=value]

Arguments


Classifier

Specify an ENVISVMClassifier object.

Examples

Specify an ENVIExamples object.

Keywords


LOSS_PROFILE (optional)

Specify a variable name that will contain the output loss profile. The result is an array of loss values, with one initial value plus one value per iteration.

Loss is a measure of how closely the classifier algorithm fits the examples. In general, the loss decreases with each iteration until it stops changing. A loss of zero would indicate a perfect fit, but a perfect fit is not necessarily desired. The goal of training is to predict new examples correctly. A fit that is too good might result in overfitting and in a poorer classifier.

ERROR (optional)

Set this keyword to a named variable that will contain any error message issued during execution of this routine. If no error occurs, the ERROR variable will be set to a null string (''). If an error occurs and the routine is a function, then the function result will be undefined.

When this keyword is not set and an error occurs, ENVI returns to the caller and execution halts. In this case, the error message is contained within !ERROR_STATE and can be caught using IDL's CATCH routine. See IDL Help for more information on !ERROR_STATE and CATCH.

See Manage Errors for more information on error handling in ENVI programming.

Version History


ENVI 5.4

Introduced

API Version


3.3

See Also


ENVIIterativeTrainer



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