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CompactRegressionNeuralNetwork

Compact neural network model for regression

Since R2021a

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    Description

    CompactRegressionNeuralNetwork is a compact version of a RegressionNeuralNetwork model object. The compact model does not include the data used for training the regression model. Therefore, you cannot perform some tasks, such as cross-validation, using the compact model. Use a compact model for tasks such as predicting the response values of new data.

    Creation

    Create a CompactRegressionNeuralNetwork object from a full RegressionNeuralNetwork model object by using compact.

    Properties

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    Neural Network Properties

    This property is read-only.

    Sizes of the fully connected layers in the neural network model, returned as a positive integer vector. The ith element of LayerSizes is the number of outputs in the ith fully connected layer of the neural network model.

    LayerSizes does not include the size of the final fully connected layer. This layer always has one output.

    Data Types: single | double

    This property is read-only.

    Learned layer weights for fully connected layers, returned as a cell array. The ith entry in the cell array corresponds to the layer weights for the ith fully connected layer. For example, Mdl.LayerWeights{1} returns the weights for the first fully connected layer of the model Mdl.

    LayerWeights includes the weights for the final fully connected layer.

    Data Types: cell

    This property is read-only.

    Learned layer biases for fully connected layers, returned as a cell array. The ith entry in the cell array corresponds to the layer biases for the ith fully connected layer. For example, Mdl.LayerBiases{1} returns the biases for the first fully connected layer of the model Mdl.

    LayerBiases includes the biases for the final fully connected layer.

    Data Types: cell

    This property is read-only.

    Activation functions for the fully connected layers of the neural network model, returned as a character vector or cell array of character vectors with values from this table.

    ValueDescription
    'relu'

    Rectified linear unit (ReLU) function — Performs a threshold operation on each element of the input, where any value less than zero is set to zero, that is,

    f(x)={x,x≥00,x<0

    'tanh'

    Hyperbolic tangent (tanh) function — Applies the tanh function to each input element

    'sigmoid'

    Sigmoid function — Performs the following operation on each input element:

    f(x)=11+e−x

    'none'

    Identity function — Returns each input element without performing any transformation, that is, f(x) = x

    • If Activations contains only one activation function, then it is the activation function for every fully connected layer of the neural network model, excluding the final fully connected layer, which does not have an activation function (OutputLayerActivation).

    • If Activations is an array of activation functions, then the ith element is the activation function for the ith layer of the neural network model.

    Data Types: char | cell

    This property is read-only.

    Activation function for final fully connected layer, returned as 'none'.

    Data Properties

    This property is read-only.

    Predictor variable names, returned as a cell array of character vectors. The order of the elements of PredictorNames corresponds to the order in which the predictor names appear in the training data.

    Data Types: cell

    This property is read-only.

    Categorical predictor indices, returned as a vector of positive integers. Assuming that the predictor data contains observations in rows, CategoricalPredictors contains index values corresponding to the columns of the predictor data that contain categorical predictors. If none of the predictors are categorical, then this property is empty ([]).

    Data Types: double

    This property is read-only.

    Expanded predictor names, returned as a cell array of character vectors. If the model uses encoding for categorical variables, then ExpandedPredictorNames includes the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is the same as PredictorNames.

    Data Types: cell

    Since R2023b

    This property is read-only.

    Predictor means, returned as a numeric vector. If you set Standardize to 1 or true when you train the neural network model, then the length of the Mu vector is equal to the number of expanded predictors (see ExpandedPredictorNames). The vector contains 0 values for dummy variables corresponding to expanded categorical predictors.

    If you set Standardize to 0 or false when you train the neural network model, then the Mu value is an empty vector ([]).

    Data Types: double

    This property is read-only.

    Response variable name, returned as a character vector.

    Data Types: char

    Response transformation function, specified as 'none' or a function handle. ResponseTransform describes how the software transforms raw response values.

    For a MATLAB® function or a function that you define, enter its function handle. For example, you can enter Mdl.ResponseTransform = @function, where function accepts a numeric vector of the original responses and returns a numeric vector of the same size containing the transformed responses.

    Data Types: char | function_handle

    Since R2023b

    This property is read-only.

    Predictor standard deviations, returned as a numeric vector. If you set Standardize to 1 or true when you train the neural network model, then the length of the Sigma vector is equal to the number of expanded predictors (see ExpandedPredictorNames). The vector contains 1 values for dummy variables corresponding to expanded categorical predictors.

    If you set Standardize to 0 or false when you train the neural network model, then the Sigma value is an empty vector ([]).

    Data Types: double

    Object Functions

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    limeLocal interpretable model-agnostic explanations (LIME)
    partialDependenceCompute partial dependence
    plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
    shapleyShapley values
    lossLoss for regression neural network
    predictPredict responses using regression neural network

    Examples

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    Open Live Script

    Reduce the size of a full regression neural network model by removing the training data from the model. You can use a compact model to improve memory efficiency.

    Load the patients data set. Create a table from the data set. Each row corresponds to one patient, and each column corresponds to a diagnostic variable. Use the Systolic variable as the response variable, and the rest of the variables as predictors.

    load patients
tbl = table(Age,Diastolic,Gender,Height,Smoker,Weight,Systolic);

    Train a regression neural network model using the data. Specify the Systolic column of tblTrain as the response variable. Specify to standardize the numeric predictors.

    Mdl = fitrnet(tbl,"Systolic","Standardize",true)
    Mdl = 
  RegressionNeuralNetwork
           PredictorNames: {'Age'  'Diastolic'  'Gender'  'Height'  'Smoker'  'Weight'}
             ResponseName: 'Systolic'
    CategoricalPredictors: [3 5]
        ResponseTransform: 'none'
          NumObservations: 100
               LayerSizes: 10
              Activations: 'relu'
    OutputLayerActivation: 'none'
                   Solver: 'LBFGS'
          ConvergenceInfo: [1x1 struct]
          TrainingHistory: [619x7 table]

    Mdl is a full RegressionNeuralNetwork model object.

    Reduce the size of the model by using compact.

    compactMdl = compact(Mdl)
    compactMdl = 
  CompactRegressionNeuralNetwork
               LayerSizes: 10
              Activations: 'relu'
    OutputLayerActivation: 'none'

    compactMdl is a CompactRegressionNeuralNetwork model object. compactMdl contains fewer properties than the full model Mdl.

    Display the amount of memory used by each neural network model.

    whos("Mdl","compactMdl")
      Name            Size            Bytes  Class                                                    Attributes

  Mdl             1x1             52007  RegressionNeuralNetwork                                            
  compactMdl      1x1              6512  classreg.learning.regr.CompactRegressionNeuralNetwork

    The full model is larger than the compact model.

    Extended Capabilities

    Version History

    Introduced in R2021a

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    See Also

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