CompactClassificationNeuralNetwork
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
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 K outputs, where
K is the number of classes in the response variable.
Data Types: single
| double
LayerWeights
— Learned layer weights
cell array
This property is read-only.
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
LayerBiases
— Learned layer biases
cell array
This property is read-only.
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
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, |
"tanh" | Hyperbolic tangent (tanh) function — Applies the |
"sigmoid" | Sigmoid function — Performs the following operation on each input element: |
"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
OutputLayerActivation
— Activation function for final fully connected layer
'softmax'
This property is read-only.
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:
The results correspond to the predicted classification scores (or posterior probabilities).
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
ClassNames
— Unique class names
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 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
Mu
— Predictor means
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
ResponseName
— Response variable name
character vector
This property is read-only.
Response variable name, returned as a character vector.
Data Types: char
Sigma
— Predictor standard deviations
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
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
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 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
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 Compact Classification Neural Network Model
gather | Gather properties of Statistics and Machine Learning Toolbox object from GPU |
Examples
Reduce Size of Neural Network Classifier
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.
load patients
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: [1x1 struct] TrainingHistory: [36x7 table]
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'
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 18790 ClassificationNeuralNetwork compactMdl 1x1 6527 classreg.learning.classif.CompactClassificationNeuralNetwork
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.
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:
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 R2021aR2024b: Specify GPU arrays (requires Parallel Computing Toolbox)
You can fit a CompactClassificationNeuralNetwork
object with GPU arrays by using fitcnet
to fit a
ClassificationNeuralNetwork
object to gpuArray
data, and then
passing the object to compact
. Most
CompactClassificationNeuralNetwork
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 CompactClassificationNeuralNetwork
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 CompactClassificationNeuralNetwork
object and retrain it using the trainnet
(Deep Learning Toolbox)
function or a custom training loop.
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];
See Also
fitcnet
| predict
| loss
| margin
| edge
| ClassificationPartitionedModel
| ClassificationNeuralNetwork
| compact
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