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Class Definition: CompactClassificationNeuralNetwork

statistics: CompactClassificationNeuralNetwork

A CompactClassificationNeuralNetwork object is a compact version of the Neural Network classification model, ClassificationNeuralNetwork.

The CompactClassificationNeuralNetwork does not include the training data resulting to a smaller classifier size, which can be used for making predictions from new data, but not for tasks such as cross validation. It can only be created from a ClassificationNeuralNetwork model by using the compact object method.

The available methods for a CompactClassificationNeuralNetwork object are:

  • predict
  • savemodel

See also: fitcnet, ClassificationNeuralNetwork

Source Code: CompactClassificationNeuralNetwork

Properties

CompactClassificationNeuralNetwork.NumPredictors is not documented.
CompactClassificationNeuralNetwork.PredictorNames is not documented.
CompactClassificationNeuralNetwork.ResponseName is not documented.
CompactClassificationNeuralNetwork.ClassNames is not documented.
CompactClassificationNeuralNetwork.ScoreTransform is not documented.
CompactClassificationNeuralNetwork.Standardize is not documented.
CompactClassificationNeuralNetwork.Sigma is not documented.
CompactClassificationNeuralNetwork.Mu is not documented.
CompactClassificationNeuralNetwork.LayerSizes is not documented.
CompactClassificationNeuralNetwork.Activations is not documented.
CompactClassificationNeuralNetwork.OutputLayerActivation is not documented.
CompactClassificationNeuralNetwork.LearningRate is not documented.
CompactClassificationNeuralNetwork.IterationLimit is not documented.
CompactClassificationNeuralNetwork.ModelParameters is not documented.
CompactClassificationNeuralNetwork.ConvergenceInfo is not documented.
CompactClassificationNeuralNetwork.DisplayInfo is not documented.
CompactClassificationNeuralNetwork.Solver is not documented.

Methods

CompactClassificationNeuralNetwork: labels = predict (obj, XC)
CompactClassificationNeuralNetwork: [labels, scores] = predict (obj, XC)

labels = predict (obj, XC) returns the vector of labels predicted for the corresponding instances in XC, using the trained neural network classification compact model in obj.

  • obj must be a CompactClassificationNeuralNetwork class object.
  • X must be an M×P numeric matrix with the same number of predictors P as the corresponding predictors of the trained neural network compact model in obj.

[labels, scores] = predict (obj, XC) also returns scores, which represent the probability of each label belonging to a specific class. For each observation in X, the predicted class label is the one with the highest score among all classes. Alternatively, scores can contain the posterior probabilities if the ScoreTransform has been previously set.

See also: fitcnet, ClassificationNeuralNetwork, CompactClassificationNeuralNetwork

ClassificationNeuralNetwork: savemodel (obj, filename)

savemodel (obj, filename) saves each property of a CompactClassificationNeuralNetwork object into an Octave binary file, the name of which is specified in filename, along with an extra variable, which defines the type classification object these variables constitute. Use loadmodel in order to load a classification object into Octave’s workspace.

See also: loadmodel, fitcnet, ClassificationNeuralNetwork, cvpartition, ClassificationPartitionedModel

Examples

  
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl)
  
Mdl =

  ClassificationNeuralNetwork

             ResponseName: 'Y'
               ClassNames: {'setosa' 'versicolor' 'virginica'}
           ScoreTransform: 'none'
          NumObservations: 150
            NumPredictors: 4
               LayerSizes: [10]
              Activations: 'sigmoid'
    OutputLayerActivation: 'sigmoid'
                   Solver: 'Gradient Descend'
CMdl =

  ClassificationPartitionedModel object with properties:

                   BinEdges: []
      CategoricalPredictors: []
                          X: [5.1000, 3.5000, 1.4000, 0.2000; 4.9000, 3, 1.4000, 0.2000; 4.7000, 3.2000, ...]
                          Y: [150x1 cell]
                 ClassNames: [3x1 cell]
                       Cost: []
        CrossValidatedModel: 'ClassificationNeuralNetwork'
                      KFold: 10
            ModelParameters: [1x1 struct]
            NumObservations: 150
                  Partition: [1x1 cvpartition]
             PredictorNames: [1x4 cell]
                      Prior: []
               ResponseName: "Y"
             ScoreTransform: 'none'
                Standardize: 0
                    Trained: [10x1 cell]