saveCompactModel
(Removed) Save model object in file for code generation
saveCompactModel
has been removed. Use saveLearnerForCoder
instead. To update your code, simply replace instances of
saveCompactModel
with
saveLearnerForCoder
.
Description
To generate C/C++ code for the object functions
(predict
, random
,
knnsearch
, or rangesearch
)
of machine learning models, use saveCompactModel
,
loadCompactModel
, and codegen
(MATLAB Coder). After training
a machine learning model, save the model by using
saveCompactModel
. Define an entry-point
function that loads the model by using loadCompactModel
and calls an object function. Then use codegen
or the
MATLAB®
Coder™ app to generate C/C++ code. Generating C/C++ code requires
MATLAB
Coder.
This flow chart shows the code generation workflow for the object functions of
machine learning models. Use saveCompactModel
for the
highlighted step.
saveCompactModel(
prepares a classification model, regression model, or nearest
neighbor searcher (Mdl
,filename
)Mdl
) for code generation and
saves it in the MATLAB formatted binary file (MAT-file) named
filename
. You can pass
filename
to loadCompactModel
to reconstruct the model object
from the filename
file.
Examples
Input Arguments
Algorithms
saveCompactModel
prepares a machine learning model (Mdl
) for code
generation. The function removes some properties that are not required for prediction.
For a model that has a corresponding compact model, the
saveCompactModel
function applies the appropriatecompact
function to the model before saving it.For a model that does not have a corresponding compact model, such as
ClassificationKNN
,ClassificationLinear
,RegressionLinear
,ExhaustiveSearcher
, andKDTreeSearcher
, thesaveCompactModel
function removes properties such as hyperparameter optimization properties, training solver information, and others.
loadCompactModel
loads the model saved
by saveCompactModel
.
Alternative Functionality
Use a coder configurer created by
learnerCoderConfigurer
for the models listed in this table.Model Coder Configurer Object Binary decision tree for multiclass classification ClassificationTreeCoderConfigurer
SVM for one-class and binary classification ClassificationSVMCoderConfigurer
Linear model for binary classification ClassificationLinearCoderConfigurer
Multiclass model for SVMs and linear models ClassificationECOCCoderConfigurer
Binary decision tree for regression RegressionTreeCoderConfigurer
Support vector machine (SVM) regression RegressionSVMCoderConfigurer
Linear regression RegressionLinearCoderConfigurer
After training a machine learning model, create a coder configurer of the model. Use the object functions and properties of the configurer to configure code generation options and to generate code for the
predict
andupdate
functions of the model. If you generate code using a coder configurer, you can update model parameters in the generated code without having to regenerate the code. For details, see Code Generation for Prediction and Update Using Coder Configurer.
Version History
Introduced in R2016bSee Also
loadCompactModel
| codegen
(MATLAB Coder) | saveLearnerForCoder