# CompactRegressionNeuralNetwork

Compact neural network model for regression

## 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

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### Neural Network Properties

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

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

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

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,

$f\left(x\right)=\left\{\begin{array}{cc}x,& x\ge 0\\ 0,& x<0\end{array}$

'tanh'

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

'sigmoid'

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

$f\left(x\right)=\frac{1}{1+{e}^{-x}}$

'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

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

### Data Properties

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

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

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

Response variable name, returned as a character vector.

Data Types: char

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

## Object Functions

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

## Examples

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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.

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: {1x6 cell}
ResponseName: 'Systolic'
CategoricalPredictors: [3 5]
ResponseTransform: 'none'
NumObservations: 100
LayerSizes: 10
Activations: 'relu'
OutputLayerActivation: 'none'
Solver: 'LBFGS'
ConvergenceInfo: [1x1 struct]
TrainingHistory: [619x7 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             51486  RegressionNeuralNetwork
compactMdl      1x1              5991  classreg.learning.regr.CompactRegressionNeuralNetwork

The full model is larger than the compact model.

## Version History

Introduced in R2021a