The Dehydrate function method returns a hash describing this object. You can use this information in a later ENVI session to restore the object using the object's static ::Hydrate method or the ENVIHydrate function. See the ENVIHydrate topic in ENVI Help.

This method is part of ENVI Deep Learning, which requires a separate license and installation.

Example


Sample data files are available on our ENVI Tutorials web page. Click the "Deep Learning" link in the ENVI Tutorial Data section to download a .zip file containing the data. Extract the contents to a local directory. The file TrainedModel.h5 is in the tornado directory.

; Start the application
e = ENVI(/HEADLESS)
 
; Update the following line with the correct path
; to the tutorial data files
ModelFile = 'C:\MyTutorialFiles\TrainedModel.h5'
 
; Create an ENVITensorFlowModel object
Model = ENVITensorFlowModel(ModelFile)
Result = Model.Dehydrate()
Print, Result, /IMPLIED_PRINT

Syntax


Result = ENVITensorFlowModel.Dehydrate(ERROR=value)

Return Value


This function method returns a hash containing the key/value pairs representing the current object state. You can build your own hash without instantiating an object. To see the required key/value pairs for the object, refer to the Hydrate help topic for that object in ENVI Help.

Arguments


None

Keywords


ERROR

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 the Manage Errors topic in ENVI Help for more information on error handling.

Version History


Deep Learning 1.0

Introduced

See Also


ENVITensorFlowModel