ClassificationNeuralNetwork

Neural network model for classification

Since R2021a

Description

A ClassificationNeuralNetwork object is a trained neural network for classification, such as a feedforward, fully connected network. In a feedforward, fully connected network, the first fully connected layer of has a connection from the network input (predictor data X), and each subsequent layer has a connection from the previous layer. Each fully connected layer multiplies the input by a weight matrix (LayerWeights) and then adds a bias vector (LayerBiases). An activation function follows each fully connected layer (Activations and OutputLayerActivation). The final fully connected layer and the subsequent softmax activation function produce the network's output, namely classification scores (posterior probabilities) and predicted labels. For more information, see Neural Network Structure.

Creation

Create a ClassificationNeuralNetwork object by using fitcnet.

Properties

Neural Network Properties

LayerSizes — Sizes of fully connected layers

Read-only: positive integer vector

This property is read-only.

Sizes of the fully connected layers in the neural network model.

The property value depends on the method used to fit the model.

• For models fit using a dlnetwork or layer array that specifies the neural network architecture, this property is empty. In this case, to examine the neural network architecture of the model, convert the model to a dlnetwork object using the dlnetwork (Deep Learning Toolbox) function.

• Otherwise, the property is a positive integer vector, where the ith element of LayerSizes is the number of outputs in the ith fully connected layer of the neural network model. In this case, 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

LayerWeights — Learned layer weights

Read-only: cell array

This property is read-only.

Learned layer weights for the fully connected layers.

The property value depends on the method used to fit the model.

• For models fit using a dlnetwork or layer array that specifies the neural network architecture, this property is empty. In this case, to examine the learnable parameters of the model, convert the model to a dlnetwork object using the dlnetwork (Deep Learning Toolbox) function.

• Otherwise, the property is a cell array, where entry i in the cell array corresponds to the layer weights for the fully connected layer i. For example, Mdl.LayerWeights{1} returns the weights for the first fully connected layer of the model Mdl. In this case, LayerWeights includes the weights for the final fully connected layer.

Data Types: cell

LayerBiases — Learned layer biases

Read-only: cell array

This property is read-only.

Learned layer biases for the fully connected layers.

The property value depends on the method used to fit the model.

• For models fit using a dlnetwork or layer array that specifies the neural network architecture, this property is empty. In this case, to examine the learnable parameters of the model, convert the model to a dlnetwork object using the dlnetwork (Deep Learning Toolbox) function.

• Otherwise, the property is a cell array, where entry i in the cell array corresponds to the layer biases for the fully connected layer i. For example, Mdl.LayerBiases{1} returns the biases for the first fully connected layer of the model Mdl. In this case, LayerBiases includes the biases for the final fully connected layer.

Data Types: cell

Activations — Activation functions for fully connected layers

Read-only: '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.

The property value depends on the method used to fit the model.

• For models fit using a dlnetwork or layer array that specifies the neural network architecture, this property is ''. In this case, to examine the neural network architecture of the model, convert the model to a dlnetwork object using the dlnetwork (Deep Learning Toolbox) function.

• Otherwise, the property is a character vector or cell array of character vectors.

  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 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.

If Activations is a character vector or a cell array of character vectors, then the values are 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

Data Types: char | cell

OutputLayerActivation — Activation function for final fully connected layer

Read-only: 'softmax' | ''

This property is read-only.

Activation function for the final fully connected layer.

The property value depends on the method used to fit the model.

• For models fit using a dlnetwork or layer array that specifies the neural network architecture, this property is empty. In this case, to examine the neural network architecture of the model, convert the model to a dlnetwork object using the dlnetwork (Deep Learning Toolbox) function.

• Otherwise, the property is 'softmax'. The function takes each input x_i and returns the following, where K is the number of classes in the response variable:

  $$f(x_i)=\frac{\exp (x_i)}{{\displaystyle \sum _{j=1}^K\exp (x_j)}}.$$

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

ModelParameters — Parameter values used to train model

Read-only: NeuralNetworkParams object

This property is read-only.

Parameter values used to train the ClassificationNeuralNetwork model, returned as a NeuralNetworkParams object. ModelParameters contains parameter values such as the name-value arguments used to train the neural network classifier.

Access the properties of ModelParameters by using dot notation. For example, access the function used to initialize the fully connected layer weights of a model Mdl by using Mdl.ModelParameters.LayerWeightsInitializer.

Convergence Control Properties

ConvergenceInfo — Convergence information

Read-only: structure array

This property is read-only.

Convergence information, returned as a structure array.

Field	Description
Iterations	Number of training iterations used to train the neural network model
TrainingLoss	Training cross-entropy loss for the returned model, or resubLoss(Mdl,'LossFun','crossentropy') for model Mdl
Gradient	Gradient of the loss function with respect to the weights and biases at the iteration corresponding to the returned model
Step	Step size at the iteration corresponding to the returned model
Time	Total time spent across all iterations (in seconds)
ValidationLoss	Validation cross-entropy loss for the returned model
ValidationChecks	Maximum number of times in a row that the validation loss was greater than or equal to the minimum validation loss
ConvergenceCriterion	Criterion for convergence
History	See TrainingHistory

For models fit using a dlnetwork or layer array that specifies the neural network architecture, the value for the ValidationChecks is always NaN.

Data Types: struct

TrainingHistory — Training history

Read-only: table

This property is read-only.

Training history, returned as a table.

Column	Description
Iteration	Training iteration
TrainingLoss	Training cross-entropy loss for the model at this iteration
Gradient	Gradient of the loss function with respect to the weights and biases at this iteration
Step	Step size at this iteration
Time	Time spent during this iteration (in seconds)
ValidationLoss	Validation cross-entropy loss for the model at this iteration
ValidationChecks	Running total of times that the validation loss is greater than or equal to the minimum validation loss

For models fit using a dlnetwork or layer array that specifies the neural network architecture, the table does not include the Time and ValidationChecks columns.

Data Types: table

Solver — Solver used to train neural network model

Read-only: 'LBFGS'

This property is read-only.

Solver used to train the neural network model, returned as 'LBFGS'. To create a ClassificationNeuralNetwork model, fitcnet uses a limited-memory Broyden-Fletcher-Goldfarb-Shanno quasi-Newton algorithm (LBFGS) as its loss function minimization technique, where the software minimizes the cross-entropy loss.

Predictor Properties

PredictorNames — Predictor variable names

Read-only: 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

Read-only: 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

Read-only: 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

Mu — Predictor means

Read-only: numeric vector | []

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

Sigma — Predictor standard deviations

Read-only: numeric vector | []

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

X — Unstandardized predictors

Read-only: numeric matrix | table

This property is read-only.

Unstandardized predictors used to train the neural network model, returned as a numeric matrix or table. X retains its original orientation, with observations in rows or columns depending on the value of the ObservationsIn name-value argument in the call to fitcnet.

Data Types: single | double | table

Response Properties

ClassNames — Unique class names

Read-only: numeric vector | categorical vector | logical vector | character array | cell array of character vectors

This property is read-only.

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 Y. (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

ResponseName — Response variable name

Read-only: character vector

This property is read-only.

Response variable name, returned as a character vector.

Data Types: char

Y — Class labels

Read-only: numeric vector | categorical vector | logical vector | character array | cell array of character vectors

This property is read-only.

Class labels used to train the model, returned as a numeric vector, categorical vector, logical vector, character array, or cell array of character vectors. Y has the same data type as the response variable used to train the model. (The software treats string arrays as cell arrays of character vectors.)

Each row of Y represents the classification of the corresponding observation in X.

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

Other Data Properties

HyperparameterOptimizationResults — Cross-validation optimization of hyperparameters

Read-only: BayesianOptimization object | table

This property is read-only.

Cross-validation optimization of hyperparameters, specified as a BayesianOptimization object or a table of hyperparameters and associated values. This property is nonempty if the 'OptimizeHyperparameters' name-value pair argument is nonempty when you create the model. The value of HyperparameterOptimizationResults depends on the setting of the Optimizer field in the HyperparameterOptimizationOptions structure when you create the model.

Value of Optimizer Option	Value of HyperparameterOptimizationResults
"bayesopt" (default)	Object of class BayesianOptimization
"gridsearch" or "randomsearch"	Table of hyperparameters used, observed objective function values (cross-validation loss), and rank of observations from lowest (best) to highest (worst)

NumObservations — Number of observations

Read-only: positive numeric scalar

This property is read-only.

Number of observations in the training data stored in X and Y, returned as a positive numeric scalar.

Data Types: double

RowsUsed — Observations of original training data stored

Read-only: logical vector | []

This property is read-only.

Observations of the original training data stored in the model, returned as a logical vector. This property is empty if all observations are stored in X and Y.

Data Types: logical

W — Observation weights

Read-only: numeric vector

This property is read-only.

Observation weights used to train the model, returned as an n-by-1 numeric vector. n is the number of observations (NumObservations).

The software normalizes the observation weights specified in the Weights name-value argument so that the elements of W within a particular class sum up to the prior probability of that class.

Data Types: single | double

Other Classification Properties

Cost — Misclassification cost

numeric square matrix

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.

The software uses the Cost value for prediction, but not training. You can change the Cost property value of the trained model by using dot notation.

Data Types: double

Prior — Prior class probabilities

Read-only: numeric vector

This property is read-only.

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

Data Types: double

ScoreTransform — Score transformation

character vector | function handle

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.

  Value	Description
  '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 + e–x)
  '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 + e–x) – 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

Create CompactClassificationNeuralNetwork

compact

Reduce size of machine learning model

Create ClassificationPartitionedModel

crossval

Cross-validate machine learning model

Create dlnetwork

dlnetwork (Deep Learning Toolbox)

Deep learning neural network

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

edge	Classification edge for neural network classifier
loss	Classification loss for neural network classifier
margin	Classification margins for neural network classifier
predict	Classify observations using neural network classifier

Assess Predictive Performance on Training Data

resubEdge	Resubstitution classification edge
resubLoss	Resubstitution classification loss
resubMargin	Resubstitution classification margin
resubPredict	Classify training data using trained classifier

Compare Accuracies

compareHoldout	Compare accuracies of two classification models using new data
testckfold	Compare accuracies of two classification models by repeated cross-validation

Gather Properties of Classification Neural Network Model

gather

Gather properties of Statistics and Machine Learning Toolbox object from GPU

Examples

Train Neural Network Classifier

Train a neural network classifier, and assess the performance of the classifier on a test set.

Read the sample file CreditRating_Historical.dat into a table. The predictor data consists of financial ratios and industry sector information for a list of corporate customers. The response variable consists of credit ratings assigned by a rating agency. Preview the first few rows of the data set.

creditrating = readtable("CreditRating_Historical.dat");
head(creditrating)

     ID      WC_TA     RE_TA     EBIT_TA    MVE_BVTD    S_TA     Industry    Rating 
    _____    ______    ______    _______    ________    _____    ________    _______

    62394     0.013     0.104     0.036      0.447      0.142        3       {'BB' }
    48608     0.232     0.335     0.062      1.969      0.281        8       {'A'  }
    42444     0.311     0.367     0.074      1.935      0.366        1       {'A'  }
    48631     0.194     0.263     0.062      1.017      0.228        4       {'BBB'}
    43768     0.121     0.413     0.057      3.647      0.466       12       {'AAA'}
    39255    -0.117    -0.799      0.01      0.179      0.082        4       {'CCC'}
    62236     0.087     0.158     0.049      0.816      0.324        2       {'BBB'}
    39354     0.005     0.181     0.034      2.597      0.388        7       {'AA' }

Because each value in the ID variable is a unique customer ID, that is, length(unique(creditrating.ID)) is equal to the number of observations in creditrating, the ID variable is a poor predictor. Remove the ID variable from the table, and convert the Industry variable to a categorical variable.

creditrating = removevars(creditrating,"ID");
creditrating.Industry = categorical(creditrating.Industry);

Convert the Rating response variable to a categorical variable.

creditrating.Rating = categorical(creditrating.Rating, ...
    ["AAA","AA","A","BBB","BB","B","CCC"]);

Partition the data into training and test sets. Use approximately 80% of the observations to train a neural network model, and 20% of the observations to test the performance of the trained model on new data. Use cvpartition to partition the data.

rng("default") % For reproducibility of the partition
c = cvpartition(creditrating.Rating,"Holdout",0.20);
trainingIndices = training(c); % Indices for the training set
testIndices = test(c); % Indices for the test set
creditTrain = creditrating(trainingIndices,:);
creditTest = creditrating(testIndices,:);

Train a neural network classifier by passing the training data creditTrain to the fitcnet function.

Mdl = fitcnet(creditTrain,"Rating")

Mdl = 
  ClassificationNeuralNetwork
           PredictorNames: {'WC_TA'  'RE_TA'  'EBIT_TA'  'MVE_BVTD'  'S_TA'  'Industry'}
             ResponseName: 'Rating'
    CategoricalPredictors: 6
               ClassNames: [AAA    AA    A    BBB    BB    B    CCC]
           ScoreTransform: 'none'
          NumObservations: 3146
               LayerSizes: 10
              Activations: 'relu'
    OutputLayerActivation: 'softmax'
                   Solver: 'LBFGS'
          ConvergenceInfo: [1×1 struct]
          TrainingHistory: [1000×7 table]


  Properties, Methods

Mdl is a trained ClassificationNeuralNetwork classifier. You can use dot notation to access the properties of Mdl. For example, you can specify Mdl.TrainingHistory to get more information about the training history of the neural network model.

Evaluate the performance of the classifier on the test set by computing the test set classification error. Visualize the results by using a confusion matrix.

testAccuracy = 1 - loss(Mdl,creditTest,"Rating", ...
    "LossFun","classiferror")

testAccuracy = 
0.7977

confusionchart(creditTest.Rating,predict(Mdl,creditTest))

Specify Neural Network Classifier Architecture

Specify a custom neural network architecture using Deep Learning Toolbox™.

Load the ionosphere data set, which includes radar signal data. X contains the predictor data, and Y is the response variable, whose values represent either good ("g") or bad ("b") radar signals.

load ionosphere

Separate the data into training data (XTrain and YTrain) and test data (XTest and YTest) by using a stratified holdout partition. Reserve approximately 30% of the observations for testing, and use the rest of the observations for training.

rng("default") % For reproducibility of the partition
cvp = cvpartition(Y,Holdout=0.3);
XTrain = X(training(cvp),:);
YTrain = Y(training(cvp));
XTest = X(test(cvp),:);
YTest = Y(test(cvp));

Define a neural network architecture with these characteristics:

• A feature input layer with an input size that matches the number of predictors.

• Three fully connected layers followed by leaky ReLU layers, connected in series, where the fully connected layers have output sizes of 16, and addition layers after the second and third fully connected layers.

• Skip connections around the second and third fully connected layers using the addition layers.

• A final fully connected layer with an output size that matches the number of classes followed by a softmax layer.

inputSize = size(XTrain,2);
outputSize = numel(unique(YTrain));

net = dlnetwork;

layers = [
    featureInputLayer(inputSize)
    fullyConnectedLayer(30)
    leakyReluLayer(Name="lrelu1")

    fullyConnectedLayer(30)

    additionLayer(2,Name="add2")
    leakyReluLayer(Name="lrelu2")

    fullyConnectedLayer(30)
    additionLayer(2,Name="add3")
    leakyReluLayer

    fullyConnectedLayer(outputSize)
    softmaxLayer];

net = addLayers(net,layers);
net = connectLayers(net,"lrelu1","add2/in2");
net = connectLayers(net,"lrelu2","add3/in2");

Visualize the neural network architecture in a plot.

figure
plot(net)

Train a neural network classifier.

Mdl = fitcnet(XTrain,YTrain,Network=net,Standardize=true)

Mdl = 
  ClassificationNeuralNetwork
             ResponseName: 'Y'
    CategoricalPredictors: []
               ClassNames: {'b'  'g'}
           ScoreTransform: 'none'
          NumObservations: 246
               LayerSizes: []
              Activations: ''
    OutputLayerActivation: ''
                   Solver: 'LBFGS'
          ConvergenceInfo: [1×1 struct]
          TrainingHistory: [30×5 table]

  View network information using dlnetwork.


  Properties, Methods

To estimate the performance of the trained classifier, compute the test set classification error.

testError = loss(Mdl,XTest,YTest, ...
    LossFun="classiferror")

testError = 
0.0774

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 models not fit using a dlnetwork or layer array that specifies the neural network architecture.

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™. (since R2024b)

Usage notes and limitations:

• The following object functions fully support GPU arrays:

  • compact

  • crossval

  • edge

  • gather

  • loss

  • margin

  • partialDependence

  • plotPartialDependence

  • predict

  • resubEdge

  • resubMargin

  • resubLoss

  • resubPredict

  • compareHoldout

  • testckfold

• The object functions execute on a GPU if at least one of the following applies:

  • The model was fitted with GPU arrays.

  • The predictor data that you pass to the object function is a GPU array.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2021a

R2024b: Specify GPU arrays (requires Parallel Computing Toolbox)

You can fit a ClassificationNeuralNetwork object with GPU arrays by using fitcnet. Most ClassificationNeuralNetwork object functions now support GPU array input arguments so that the functions can execute on a GPU. The object functions that do not support GPU array inputs are lime and shapley.

R2024b: Convert to dlnetwork

Convert a ClassificationNeuralNetwork object to a dlnetwork (Deep Learning Toolbox) object using the dlnetwork function. Use dlnetwork objects to make further edits and customize the underlying neural network of a ClassificationNeuralNetwork object and retrain it using the trainnet (Deep Learning Toolbox) function or a custom training loop.

R2023b: Model stores observations with missing predictor values

Starting in R2023b, training observations with missing predictor values are included in the X, Y, and W data properties. The RowsUsed property indicates the training observations stored in the model, rather than those used for training. Observations with missing predictor values continue to be omitted from the model training process.

In previous releases, the software omitted training observations that contained missing predictor values from the data properties of the model.

R2023b: Neural network models include standardization properties

Neural network models include Mu and Sigma properties that contain the means and standard deviations, respectively, used to standardize the predictors before training. The properties are empty when the fitting function does not perform any standardization.

R2023a: Neural network classifiers support misclassification costs and prior probabilities

fitcnet supports misclassification costs and prior probabilities for neural network classifiers. Specify the Cost and Prior name-value arguments when you create a model. Alternatively, you can specify misclassification costs after training a model by using dot notation to change the Cost property value of the model.

Mdl.Cost = [0 2; 1 0];