This task generates a number of randomized sets of parameter values to train a pixel-based TensorFlow model.
This task is part of ENVI Deep Learning, which requires a separate license and installation.
Example
e = ENVI(/HEADLESS)
RandomizeModelTask = ENVITask('RandomizeTrainTensorFlowPixelModel')
RandomizeModelTask.ITERATIONS = 5
RandomizeModelTask.Execute
Print, 'Blur Distance: ', RandomizeModelTask.OUTPUT_BLUR_DISTANCE
Print, 'Class Weight: ', RandomizeModelTask.OUTPUT_CLASS_WEIGHT
Print, 'Loss Weight: ', RandomizeModelTask.OUTPUT_LOSS_WEIGHT
Print, 'Solid Distance: ', RandomizeModelTask.OUTPUT_SOLID_DISTANCE
Syntax
Result = ENVITask('RandomizeTrainTensorFlowPixelModel')
Input properties (Set, Get): BLUR_DISTANCE, CLASS_WEIGHT, ITERATION, LOSS_WEIGHT, METHOD, SOLID_DISTANCE
Output properties (Get only): OUTPUT_BLUR_DISTANCE, OUTPUT_CLASS_WEIGHT, OUTPUT_LOSS_WEIGHT, OUTPUT_SOLID_DISTANCE
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.
Methods
This task inherits the following methods from ENVITask. See the ENVITask topic in ENVI Help.
- AddParameter
- Execute
- Parameter
- ParameterNames
- RemoveParameters
Properties
This task inherits the following properties from ENVITask:
COMMUTE_ON_DOWNSAMPLE
COMMUTE_ON_SUBSET
DESCRIPTION
DISPLAY_NAME
NAME
REVISION
See the ENVITask topic in ENVI Help for details.
This task also contains the following properties:
BLUR_DISTANCE (optional)
Specify a double-precision array with the minimum and maximum distance in pixels to expand positive (non-background) areas beyond the solid distance expansion. The area expanded is blurred, diminishing from the edges of the feature areas to the blur distance. The maximum blur distance is used at the beginning of training and decreased to the minimum blur distance at the end of training. If this value is not set, OUTPUT_BLUR_DISTANCE will be a random value within normal range.
CLASS_WEIGHT (optional)
Specify a double-precision array with the minimum and maximum weights for having a more even balance of classes (including background) during patch selection. Balancing of patch selection is weighted by the maximum value at the beginning of training and decreased to the minimum value at the end of training. In general, set the maximum higher for sparser training sets. The useful range for the maximum value is between 0.0 and 3.0. If this value is not set, OUTPUT_CLASS_WEIGHT will be a random value within normal range.
ITERATION (optional)
Specify the number of sets of parameter values to generate.
LOSS_WEIGHT (optional)
Specify the weight of positive (non-background) pixels when calculating how well the model is fitting the training data. If this value is not set, OUTPUT_LOSS_WEIGHT will be a random value within normal range.
METHOD (required)
Specify a string indicating the method used to randomize the values. The choices are:
- Random Uniform: produces a uniform distribution of numbers
- Sobol Sequence (default): produces a non-random list of numbers that does a better job exploring the range of possible values
OUTPUT_BLUR_DISTANCE
A double-precision array with the minimum and maximum distance in pixels to expand positive (non-background) areas beyond the solid distance expansion.
OUTPUT_CLASS_WEIGHT
The double-precision array with the minimum and maximum weights for having a more even balance of classes (including background) when sampling.
OUTPUT_LOSS_WEIGHT
The weight of positive (non-background) pixels when calculating how well the model is fitting the training data.
OUTPUT_SOLID_DISTANCE
The distance in pixels to expand positive (non-background) areas.
SOLID_DISTANCE (optional)
Specify the distance in pixels to expand positive (non-background) areas. If this value is not set, OUTPUT_SOLID_DISTANCE will be a random value within normal range.
Version History
See Also
ENVITensorFlowModel, TrainTensorFlowPixelModel Task