TrainVectorClassifier-ann otbcli_TrainVectorClassifier TrainVectorClassifier (ann) Learning Train a classifier based on labeled geometries and a list of features to consider. ParameterMultipleInput io.vd Input Vector Data Input geometries used for training (note : all geometries from the layer will be used) False ParameterFile io.stats Input XML image statistics file XML file containing mean and variance of each feature. True OutputFile io.confmatout Output confusion matrix Output file containing the confusion matrix (.csv format). OutputFile io.out Output model Output file containing the model estimated (.txt format). ParameterString feat Field names for training features. List of field names in the input vector data to be used as features for training. False ParameterString cfield Field containing the class id for supervision Field containing the class id for supervision. Only geometries with this field available will be taken into account. class False ParameterNumber layer Layer Index Index of the layer to use in the input vector file. 0 True ParameterMultipleInput valid.vd Validation Vector Data Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used) True ParameterNumber valid.layer Layer Index Index of the layer to use in the validation vector file. 0 True ParameterSelection classifier Classifier to use for the training Choice of the classifier to use for the training. ann 0 False ParameterSelection classifier.ann.t Train Method Type Type of training method for the multilayer perceptron (MLP) neural network. reg back 0 False ParameterString classifier.ann.sizes Number of neurons in each intermediate layer The number of neurons in each intermediate layer (excluding input and output layers). False ParameterSelection classifier.ann.f Neuron activation function type Neuron activation function. ident sig gau 1 False ParameterNumber classifier.ann.a Alpha parameter of the activation function Alpha parameter of the activation function (used only with sigmoid and gaussian functions). 1 False ParameterNumber classifier.ann.b Beta parameter of the activation function Beta parameter of the activation function (used only with sigmoid and gaussian functions). 1 False ParameterNumber classifier.ann.bpdw Strength of the weight gradient term in the BACKPROP method Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. 0.1 False ParameterNumber classifier.ann.bpms Strength of the momentum term (the difference between weights on the 2 previous iterations) 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. 0.1 False ParameterNumber classifier.ann.rdw Initial value Delta_0 of update-values Delta_{ij} in RPROP method Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). 0.1 False ParameterNumber classifier.ann.rdwm Update-values lower limit Delta_{min} in RPROP method Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). 1e-07 False ParameterSelection classifier.ann.term Termination criteria Termination criteria. iter eps all 2 False ParameterNumber classifier.ann.eps Epsilon value used in the Termination criteria Epsilon value used in the Termination criteria. 0.01 False ParameterNumber classifier.ann.iter Maximum number of iterations used in the Termination criteria Maximum number of iterations used in the Termination criteria. 1000 False ParameterNumber rand set user defined seed Set specific seed. with integer value. 0 True