A numeric matrix containing the unstandardized predictor data. Each column of X represents one predictor (variable), and each row represents one observation. This property is read-only.

Specified as a logical or numeric column vector, or as a character array or a cell array of character vectors with the same number of rows as the predictor data. Each row in Y is the observed class label for the corresponding row in X. This property is read-only.

A positive integer value specifying the number of observations in the training dataset used for training the ClassificationNeuralNetwork model. This property is read-only.

A logical column vector with the same length as the observations in the original predictor data X specifying which rows have been used for fitting the ClassificationNeuralNetwork model. This property is read-only.

A positive integer value specifying the number of predictors in the training dataset used for training the ClassificationNeuralNetwork model. This property is read-only.

A cell array of character vectors specifying the names of the predictor variables. The names are in the order in which the appear in the training dataset. This property is read-only.

A character vector specifying the name of the response variable Y. This property is read-only.

An array of unique values of the response variable Y, which has the same data types as the data in Y. This property is read-only. ClassNames can have any of the following datatypes:

• Cell array of character vectors
• Character array
• Logical vector
• Numeric vector

Specified as a function handle for transforming the classification scores. Add or change the ScoreTransform property using dot notation as in:

• obj.ScoreTransform = 'function_name'
• obj.ScoreTransform = @function_handle

When specified as a character vector, it can be any of the following built-in functions. Nevertherless, the ScoreTransform property always stores their function handle equivalent.

A boolean flag indicating whether the predictor data has been standardized prior to training. When true, the predictors are centered and scaled to have zero mean and unit variance. This property is read-only.

A numeric vector containing the standard deviations of the predictors used for standardization. Empty if Standardize is false. This property is read-only.

A numeric vector containing the means of the predictors used for standardization. Empty if Standardize is false. This property is read-only.

A positive integer vector specifying the sizes of the fully connected layers in the neural network model. The i-th element of LayerSizes is the number of outputs in the i-th 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 Y. This property is read-only.

A character vector or cell array of character vectors specifying the activation functions used in the hidden layers of the neural network. Supported activation functions include: "linear", "sigmoid", "relu", "tanh", "softmax", "lrelu", "prelu", "elu", and "gelu". This property is read-only.

A character vector specifying the activation function of the output layer of the neural network. Supported activation functions are the same as for the Activations property. This property is read-only.

A positive scalar value defining the learning rate used by the gradient descent algorithm during training. This property is read-only.

A positive integer value defining the maximum number of epochs for training the model. This property is read-only.

A structure containing the parameters used to train the neural network classifier model, including layer weights and activations as generated by the fcnntrain function. This property is read-only.

A structure containing convergence information of the neural network classifier model with the following fields:

• Accuracy - The prediction accuracy at each iteration during training
• TrainingLoss - The loss value recorded at each iteration during training
• Time - The cumulative time taken for all iterations in seconds

This property is read-only.

A boolean flag indicating whether to print information during training. This property is read-only.

A character vector specifying the solver algorithm used to train the neural network model. Currently only "Gradient Descend" is supported. This property is read-only.

statistics: obj = ClassificationNeuralNetwork (X, Y)

statistics: obj = ClassificationNeuralNetwork (…, name, value)

obj = ClassificationNeuralNetwork (X, Y) returns a ClassificationNeuralNetwork object, with X as the predictor data and Y containing the class labels of observations in X.

• X must be a $N\times P$ numeric matrix of input data where rows correspond to observations and columns correspond to features or variables. X will be used to train the neural network model.
• Y is $N\times 1$ matrix or cell matrix containing the class labels of corresponding predictor data in X. Y can contain any type of categorical data. Y must have the same number of rows as X.

obj = ClassificationNeuralNetwork (…, name, value) returns a ClassificationNeuralNetwork object with parameters specified by the following name, value paired input arguments:

See also: fitcnet

ClassificationNeuralNetwork: label = predict (obj, XC)

ClassificationNeuralNetwork: [label, score] = predict (obj, XC)

label = predict (obj, XC) returns the vector of labels predicted for the corresponding instances in XC, using the predictor data in obj.X and corresponding labels, obj.Y, stored in the ClassificationNeuralNetwork model, obj.

• obj must be a ClassificationNeuralNetwork class object.
• XC must be an $M\times P$ numeric matrix with the same number of features $P$ as the corresponding predictors of the neural network model in obj.

[label, score] = predict (obj, XC) also returns score, which contains the predicted class scores or posterior probabilities for each instance of the corresponding unique classes.

The score matrix contains the classification scores for each class. For each observation in XC, the predicted class label is the one with the highest score among all classes. If the ScoreTransform property is set to a transformation function, the scores are transformed accordingly before being returned.

See also: ClassificationNeuralNetwork, fitcnet

ClassificationNeuralNetwork: label = resubPredict (obj)

ClassificationNeuralNetwork: [label, score] = resubPredict (obj)

label = resubPredict (obj) returns the vector of labels predicted for the corresponding instances in the training data, using the predictor data in obj.X and corresponding labels, obj.Y, stored in the neural network classification model, obj.

• obj must be a ClassificationNeuralNetwork class object.

[label, score] = resubPredict (obj) also returns score, which contains the predicted class scores or posterior probabilities for each instance of the corresponding unique classes.

See also: ClassificationNeuralNetwork, fitcnet

ClassificationNeuralNetwork: CVMdl = crossval (obj)

ClassificationNeuralNetwork: CVMdl = crossval (…, Name, Value)

CVMdl = crossval (obj) returns a cross-validated model object, CVMdl, from a trained model, obj, using 10-fold cross-validation by default.

CVMdl = crossval (obj, name, value) specifies additional name-value pair arguments to customize the cross-validation process.

See also: fitcnet, ClassificationNeuralNetwork, cvpartition, ClassificationPartitionedModel

ClassificationNeuralNetwork: CVMdl = compact (obj)

CVMdl = compact (obj) creates a compact version of the ClassificationNeuralNetwork object, obj.

See also: fitcnet, ClassificationNeuralNetwork, CompactClassificationNeuralNetwork

ClassificationNeuralNetwork: savemodel (obj, filename)

savemodel (obj, filename) saves each property of a ClassificationNeuralNetwork object into an Octave binary file, the name of which is specified in filename, along with an extra variable, which defines the type classification object these variables constitute. Use loadmodel in order to load a classification object into Octave’s workspace.

See also: loadmodel, fitcnet, ClassificationNeuralNetwork