CompactRegressionNeuralNetwork

Compact neural network model for regression

Since R2021a

Description

CompactRegresionNeuralNetwork 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

Neural Network Properties

LayerSizes — Sizes of fully connected layers

positive integer vector

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

LayerWeights — Learned layer weights

cell array

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

LayerBiases — Learned layer biases

cell array

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

Activations — Activation functions for fully connected layers

'relu' | 'tanh' | 'sigmoid' | 'none' | cell array of character vectors

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.

Value	Description
'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, $$f\left(x\right)=\{\begin{array}{cc}x,& x\ge 0\\ 0,& x<0\end{array}$$
'tanh'	Hyperbolic tangent (tanh) function — Applies the tanh function to each input element
'sigmoid'	Sigmoid function — Performs the following operation on each input element: $$f(x)=\frac{1}{1+e^{-x}}$$
'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

OutputLayerActivation — Activation function for final fully connected layer

'none'

This property is read-only.

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

Data Properties

PredictorNames — Predictor variable names

cell array of character vectors

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

CategoricalPredictors — Categorical predictor indices

vector of positive integers | []

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

ExpandedPredictorNames — Expanded predictor names

cell array of character vectors

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

ResponseName — Response variable name

character vector

This property is read-only.

Response variable name, returned as a character vector.

Data Types: char

ResponseTransform — Response transformation function

'none'

This property is read-only.

Response transformation function, returned as 'none'. The software does not transform the raw response values.

Object Functions

Interpret Prediction

lime	Local interpretable model-agnostic explanations (LIME)
partialDependence	Compute partial dependence
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapley	Shapley values

Assess Predictive Performance on New Observations

loss	Loss for regression neural network
predict	Predict responses using regression neural network

Examples

Reduce Size of Regression Neural Network Model

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: [648x7 table]


  Properties, Methods

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'


  Properties, Methods

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             53110  RegressionNeuralNetwork                                            
  compactMdl      1x1              5991  classreg.learning.regr.CompactRegressionNeuralNetwork              

The full model is larger than the compact model.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• The predict object function supports code generation.

For more information, see Introduction to Code Generation.

Version History

Introduced in R2021a