This function restores an ENVITensorFlowModel object, which specifies the TensorFlow model used for deep learning.
This routine is part of ENVI Deep Learning, which requires a separate license and installation.
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.
e = ENVI(/HEADLESS)
ModelFile = 'C:\MyTutorialFiles\TrainedModel.h5'
Model = ENVITensorFlowModel(ModelFile)
Print, Model, /IMPLIED_PRINT
Result = ENVITensorFlowModel(Input_File [, Properties=value] [, ERROR=value])
This routine returns a reference to an ENVITensorFlowModel object.
Specify a fully qualified filename and path to an ENVITensorFlowModel file in HDF5 format.
Properties marked as "Set" are those that you can set to specific values. You can also retrieve their current values any time. Properties marked as "Get" are those whose values you can retrieve but not set.
A description of the model's capabilities.
A Boolean value that indicates whether or not the model has been trained.
The name of the model.
The number of bands in the training rasters (minus the mask band).
The number of classes (minus the background class).
The edge length (in pixels) of the square patches used for training.
A string that is a fully qualified raster file path.
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.
Deep Learning 1.0
Deep Learning 1.1
Added NCLASSES property
TrainTensorFlowMaskModel Task, TensorFlowMaskClassification Task