# CompactClassificationNeuralNetwork

Compact neural network model for classification

## Description

CompactClassificationNeuralNetwork is a compact version of a ClassificationNeuralNetwork model object. The compact model does not include the data used for training the classifier. Therefore, you cannot perform some tasks, such as cross-validation, using the compact model. Use a compact model for tasks such as predicting the labels of new data.

## Creation

Create a CompactClassificationNeuralNetwork object from a full ClassificationNeuralNetwork 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 K outputs, where K is the number of classes in the response variable.

Data Types: single | double

Learned layer weights for the 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 the 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. The activation function for the final fully connected layer is always softmax (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 the final fully connected layer, returned as 'softmax'. The function takes each input xi and returns the following, where K is the number of classes in the response variable:

$f\left({x}_{i}\right)=\frac{\mathrm{exp}\left({x}_{i}\right)}{\sum _{j=1}^{K}\mathrm{exp}\left({x}_{j}\right)}.$

The results correspond to the predicted classification scores (or posterior probabilities).

### 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

Unique class names used in training, returned as a numeric vector, categorical vector, logical vector, character array, or cell array of character vectors. ClassNames has the same data type as the class labels in the response variable used to train the model. (The software treats string arrays as cell arrays of character vectors.) ClassNames also determines the class order.

Data Types: single | double | categorical | logical | char | cell

Response variable name, returned as a character vector.

Data Types: char

### Other Classification Properties

Misclassification cost, returned as a numeric square matrix, where Cost(i,j) is the cost of classifying a point into class j if its true class is i. The cost matrix always has this form: Cost(i,j) = 1 if i ~= j, and Cost(i,j) = 0 if i = j. The rows correspond to the true class and the columns correspond to the predicted class. The order of the rows and columns of Cost corresponds to the order of the classes in ClassNames.

Data Types: double

Prior probabilities for each class, returned as a numeric vector. The order of the elements of Prior corresponds to the elements of ClassNames.

Data Types: double

Score transformation, specified as a character vector or function handle. ScoreTransform represents a built-in transformation function or a function handle for transforming predicted classification scores.

To change the score transformation function to function, for example, use dot notation.

• For a built-in function, enter a character vector.

Mdl.ScoreTransform = 'function';

This table describes the available built-in functions.

ValueDescription
'doublelogit'1/(1 + e–2x)
'invlogit'log(x / (1 – x))
'ismax'Sets the score for the class with the largest score to 1, and sets the scores for all other classes to 0
'logit'1/(1 + ex)
'none' or 'identity'x (no transformation)
'sign'–1 for x < 0
0 for x = 0
1 for x > 0
'symmetric'2x – 1
'symmetricismax'Sets the score for the class with the largest score to 1, and sets the scores for all other classes to –1
'symmetriclogit'2/(1 + ex) – 1

• For a MATLAB® function or a function that you define, enter its function handle.

Mdl.ScoreTransform = @function;

function must accept a matrix (the original scores) and return a matrix of the same size (the transformed scores).

Data Types: char | function_handle

## Object Functions

 compareHoldout Compare accuracies of two classification models using new data edge Classification edge for neural network classifier loss Classification loss for neural network classifier margin Classification margins for neural network classifier partialDependence Compute partial dependence plotPartialDependence Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots predict Classify observations using neural network classifier

## Examples

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Reduce the size of a full neural network classifier 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 Smoker variable as the response variable, and the rest of the variables as predictors.

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

Train a neural network classifier using the data. Specify the Smoker column of tbl as the response variable. Specify to standardize the numeric predictors.

Mdl = fitcnet(tbl,"Smoker","Standardize",true)
Mdl =
ClassificationNeuralNetwork
PredictorNames: {'Diastolic'  'Systolic'  'Gender'  'Height'  'Weight'  'Age'}
ResponseName: 'Smoker'
CategoricalPredictors: 3
ClassNames: [0 1]
ScoreTransform: 'none'
NumObservations: 100
LayerSizes: 10
Activations: 'relu'
OutputLayerActivation: 'softmax'
Solver: 'LBFGS'
ConvergenceInfo: [1×1 struct]
TrainingHistory: [36×7 table]

Properties, Methods

Mdl is a full ClassificationNeuralNetwork model object.

Reduce the size of the model by using compact.

compactMdl = compact(Mdl)
compactMdl =
CompactClassificationNeuralNetwork
LayerSizes: 10
Activations: 'relu'
OutputLayerActivation: 'softmax'

Properties, Methods

compactMdl is a CompactClassificationNeuralNetwork 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             18836  ClassificationNeuralNetwork
compactMdl      1x1              6663  classreg.learning.classif.CompactClassificationNeuralNetwork

The full model is larger than the compact model.