This is a reference to an ENVIDeepLearningKerasModel object, which specifies the Deep Learning Keras model used for deep learning.
Example
e = ENVI(/HEADLESS)
ModelFile = 'C:\MyTutorialFiles\ObjectDetectionModel_HandicapSpots.h5'
Model = ENVIDeepLearningKerasModel(ModelFile)
Print, Model, /IMPLIED_PRINT
Syntax
Result = ENVIDeepLearningKerasModel(Input_File [, Properties=value] [, ERROR=value])
Return Value
This routine returns a reference to an ENVIDeepLearningKerasModel object.
Arguments
Input_File
Specify a fully qualified filename and path to an ENVIDeepLearningKerasModel file in HDF5 format.
Methods
Close
Dehydrate
Hydrate
Properties
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.
CLASS_COLORS (Get, Set)
An array of RGB triplets, where each triplet defines the color representation for a corresponding class label in CLASS_NAMES. These colors are used for visualization purposes, such as rendering segmentation masks or labeling detected objects.
CLASS_NAMES (Get, Set)
An array of class labels that the Keras model was previously trained to recognize. Each label corresponds to a specific feature or category in the dataset used for training.
IMAGE_HEIGHT (Get, Set)
The height (in pixels) of the input image data required by the model. This parameter ensures that the input data dimensions are compatible with the model's architecture and preprocessing pipeline.
IMAGE_WIDTH (Get, Set)
The width (in pixels) of the input image data required by the model. This ensures that the input data dimensions are compatible with the model's architecture and preprocessing pipeline.
METRICS (Get)
A Hash of training and validation metrics collected during model training. Keys include standard performance indicators such as loss, val_loss, accuracy, val_accuracy, and other learning metrics.
MODEL_ARCHITECTURE (Get)
An informal string that provides a general description of the architecture used to train the Keras model.
MODEL_AUTHOR (Get, Set)
The name of the individual or organization responsible for developing or training the Keras model.
MODEL_DATE (Get, Set)
The date the Keras model was created, trained, or finalized.
MODEL_DESCRIPTION (Get, Set)
A description of the model's capabilities.
MODEL_LICENSE (Get, Set)
The license under which the Keras model is distributed. This ensures compliance with the legal and permitted use requirements associated with the model.
MODEL_NAME (Get, Set)
The name of the Keras model
MODEL_VERSION (Get, Set)
The version of the Keras model (example: 1.0.0).
NAME (Get, Set)
The name of the model.
NUMBER_OF_BANDS (Get, Set)
The number of spectral or data bands present in the input data. This defines the dimensionality of the input data.
TILE_OVERLAP (Get, Set)
The number of pixels that will define a margin on all sides of each detection patch, forming a center-focused field of view (FOV).
UID (Get, Set)
A string that is a fully qualified raster file path.
WAVELENGTH_UNITS (Get, Set)
Specify the unit of measurement for the WAVELENGTHS parameter. Valid options are Micrometers and Nanometers, which correspond to the spectral units used to define input band wavelengths.
WAVELENGTHS (Get, Set)
An optional raster-specific parameter that defines the spectral wavelengths corresponding to each input band. This allows the classifier to align incoming data with the spectral configuration used during model training. When set, it enables wavelength-aware band selection, allowing the model to automatically choose the most relevant bands for classification based on its learned spectral preferences.
Keywords
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 the Manage Errors topic in ENVI Help for more information on error handling.
Version History
|
Deep Learning 4.0
|
Introduced |
See Also
ENVIDeepLearningOnnxModel