Tool [deprecated] Neural Networks (OpenCV)

Integration of the OpenCV Neural Networks library.
http://docs.opencv.org/modules/ml/doc/neural_networks.html

Author: Luca Piras
Menu: Imagery|Classification

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Train INPUT	Table (input)	`TRAIN_INPUT_TABLE`	Input data to train the network.	-
	Prediction INPUT	Table (input)	`EVAL_INPUT_TABLE`	Input data for the trained network.	-
	Train INPUT	Grid list (input)	`TRAIN_INPUT_GRIDS`	Input grids to train the network.	-
	Select training areas	Shapes (input)	`TRAIN_INPUT_AREAS`	The areas used to train the neural network.	-
	Sample weights (*)	Table (optional input)	`WEIGHTS`	Optional floating-point vector of weights for each sample. Some samples may be more important than others for training.	-
	Indices (*)	Table (optional input)	`INDICES`	Optional integer vector indicating the samples (rows of inputs and outputs) that are taken into account.	-
Output	OUTPUT	Table (output)	`EVAL_OUTPUT_TABLE`	Predicted responses (probabilities) for corresponding samples.	-
	OUTPUT classes	Grid (output)	`EVAL_OUTPUT_GRID_CLASSES`	These are the winner class vectors.	-
	OUTPUT certainty (*)	Grid (optional output)	`EVAL_OUTPUT_GRID_CERTAINTY`	These are the probabilities for the winner classes given by the neural network.	-
Options	Grid system	Grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	Select training output features	Parameters	`TRAIN_FEATURES_TABLE`	Select the features that should be used as the OUTPUT features.	0 Parameters:
	Select class identifier	Table field	`TRAIN_INPUT_AREAS_CLASS_FIELD`	The class field used to classify the shape. All classes will be vectorized.	-
	Data type	Choice	`DATA_TYPE`	Choose the type of data used in this model.	Available Choices: [0] Table [1] Grid Default: 0
	Number of layers	Integer	`NNET_LAYER`	You can specify the number of layers in the network (including input and output layer).	Minimum: 1 Default: 3
	Number of neurons	Integer	`NNET_NEURONS`	You can specify the number of neurons in each layer of the network.	Minimum: 1 Default: 3
	Maximum number of iterations	Integer	`MAX_ITER`	Termination criteria of the training algorithm. You can specify the maximum number of iterations.	Default: 1000
	Error change (epsilon)	Floating point	`EPSILON`	Termination criteria of the training algorithm. You can specify how much the error could change between the iterations to make the algorithm continue (epsilon).	Default: 0.010000
	Activation function	Choice	`ACTIVATION_FUNCTION`	Choose the activation function used by the neronal network.	Available Choices: [0] Identity [1] Sigmoid [2] Gaussian Default: 1
	Alpha	Floating point	`ALPHA`	This is the alpha parameter value for the sigmoid function.	Default: 1.000000
	Beta	Floating point	`BETA`	This is the beta parameter value for the sigmoid function.	Default: 1.000000
	Training method	Choice	`TRAINING_METHOD`	Choose the training method used for the neronal network.	Available Choices: [0] Resilent propagation (RPROP) [1] Backpropagation algorithm Default: 1
	Initial update value	Floating point	`RP_DW0`	Initial value delta_0 of update-values delta_ij.	Default: 0.000000
	Increase factor	Floating point	`RP_DW_PLUS`	Increase factor n_plus. Must be > 1.	Minimum: 1.010000 Default: 1.200000
	Decrease factor	Floating point	`RP_DW_MINUS`	Decrease factor n_minus. Must be < 1.	Minimum: 0.010000 Maximum: 0.990000 Default: 0.500000
	Update values lower limit	Floating point	`RP_DW_MIN`	Corresponds to delta_min. Must be > 0	Minimum: 0.010000 Default: 0.100000
	Update values upper limit	Floating point	`RP_DW_MAX`	Corresponds to delta_max. Must be > 1	Minimum: 1.010000 Default: 1.100000
	Weight gradient term	Floating point	`BP_DW_SCALE`	Strength of the weight gradient term. The recommended value is about 0.1.	Default: 0.100000
	Moment term	Floating point	`BP_MOMENT_SCALE`	Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.	Default: 0.100000
	Update the network weights	Boolean	`UPDATE_WEIGHTS`	-	Default: 0
	No INPUT vector normalization	Boolean	`NO_INPUT_SCALE`	If false, the training algorithm normalizes each input feature independently, shifting its mean value to 0 and making the standard deviation equal to 1.	Default: 1
	No OUTPUT vector normalization	Boolean	`NO_OUTPUT_SCALE`	If false, the training algorithm normalizes each output feature independently, by transforming it to the certain range depending on the used activation function.	Default: 1
(*) optional

Command-line

Usage: saga_cmd imagery_opencv 3 [-TRAIN_INPUT_TABLE <str>] [-EVAL_INPUT_TABLE <str>] [-EVAL_OUTPUT_TABLE <str>] [-TRAIN_INPUT_GRIDS <str>] [-TRAIN_INPUT_AREAS <str>] [-TRAIN_INPUT_AREAS_CLASS_FIELD <str>] [-EVAL_OUTPUT_GRID_CLASSES <str>] [-EVAL_OUTPUT_GRID_CERTAINTY <str>] [-DATA_TYPE <str>] [-NNET_LAYER <num>] [-NNET_NEURONS <num>] [-MAX_ITER <num>] [-EPSILON <double>] [-ACTIVATION_FUNCTION <str>] [-ALPHA <double>] [-BETA <double>] [-TRAINING_METHOD <str>] [-RP_DW0 <double>] [-RP_DW_PLUS <double>] [-RP_DW_MINUS <double>] [-RP_DW_MIN <double>] [-RP_DW_MAX <double>] [-BP_DW_SCALE <double>] [-BP_MOMENT_SCALE <double>] [-WEIGHTS <str>] [-INDICES <str>] [-UPDATE_WEIGHTS <str>] [-NO_INPUT_SCALE <str>] [-NO_OUTPUT_SCALE <str>]
  -TRAIN_INPUT_TABLE:<str>            	Train INPUT
	Table (input)
  -EVAL_INPUT_TABLE:<str>             	Prediction INPUT
	Table (input)
  -EVAL_OUTPUT_TABLE:<str>            	OUTPUT
	Table (output)
  -TRAIN_INPUT_GRIDS:<str>            	Train INPUT
	Grid list (input)
  -TRAIN_INPUT_AREAS:<str>            	Select training areas
	Shapes (input)
  -TRAIN_INPUT_AREAS_CLASS_FIELD:<str>	Select class identifier
	Table field
  -EVAL_OUTPUT_GRID_CLASSES:<str>     	OUTPUT classes
	Grid (output)
  -EVAL_OUTPUT_GRID_CERTAINTY:<str>   	OUTPUT certainty
	Grid (optional output)
  -DATA_TYPE:<str>                    	Data type
	Choice
	Available Choices:
	[0] Table
	[1] Grid
	Default: 0
  -NNET_LAYER:<num>                   	Number of layers
	Integer
	Minimum: 1
	Default: 3
  -NNET_NEURONS:<num>                 	Number of neurons
	Integer
	Minimum: 1
	Default: 3
  -MAX_ITER:<num>                     	Maximum number of iterations
	Integer
	Default: 1000
  -EPSILON:<double>                   	Error change (epsilon)
	Floating point
	Default: 0.010000
  -ACTIVATION_FUNCTION:<str>          	Activation function
	Choice
	Available Choices:
	[0] Identity
	[1] Sigmoid
	[2] Gaussian
	Default: 1
  -ALPHA:<double>                     	Alpha
	Floating point
	Default: 1.000000
  -BETA:<double>                      	Beta
	Floating point
	Default: 1.000000
  -TRAINING_METHOD:<str>              	Training method
	Choice
	Available Choices:
	[0] Resilent propagation (RPROP)
	[1] Backpropagation algorithm
	Default: 1
  -RP_DW0:<double>                    	Initial update value
	Floating point
	Default: 0.000000
  -RP_DW_PLUS:<double>                	Increase factor
	Floating point
	Minimum: 1.010000
	Default: 1.200000
  -RP_DW_MINUS:<double>               	Decrease factor
	Floating point
	Minimum: 0.010000
	Maximum: 0.990000
	Default: 0.500000
  -RP_DW_MIN:<double>                 	Update values lower limit
	Floating point
	Minimum: 0.010000
	Default: 0.100000
  -RP_DW_MAX:<double>                 	Update values upper limit
	Floating point
	Minimum: 1.010000
	Default: 1.100000
  -BP_DW_SCALE:<double>               	Weight gradient term
	Floating point
	Default: 0.100000
  -BP_MOMENT_SCALE:<double>           	Moment term
	Floating point
	Default: 0.100000
  -WEIGHTS:<str>                      	Sample weights
	Table (optional input)
  -INDICES:<str>                      	Indices
	Table (optional input)
  -UPDATE_WEIGHTS:<str>               	Update the network weights
	Boolean
	Default: 0
  -NO_INPUT_SCALE:<str>               	No INPUT vector normalization
	Boolean
	Default: 1
  -NO_OUTPUT_SCALE:<str>              	No OUTPUT vector normalization
	Boolean
	Default: 1