Save model object in file for code generation
To generate C/C++ code for the object functions of machine learning models (including predict
, random
, knnsearch
, rangesearch
, and incremental learning functions), use saveLearnerForCoder
, loadLearnerForCoder
, and codegen
(MATLAB Coder). After training a machine learning model, save the model by using saveLearnerForCoder
. Define an entry-point function that loads the model by using loadLearnerForCoder
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 saveLearnerForCoder
for the highlighted step.
Fixed-point C/C++ code generation requires an additional step that defines the fixed-point data types of the variables required for prediction. Create a fixed-point data type structure by using the data type function generated by generateLearnerDataTypeFcn
, and use the structure as an input argument of loadLearnerForCoder
in an entry-point function. Generating fixed-point C/C++ code requires MATLAB
Coder and Fixed-Point Designer™.
This flow chart shows the fixed-point code generation workflow for the predict
function of a machine learning model. Use saveLearnerForCoder
for the highlighted step.
saveLearnerForCoder(
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 loadLearnerForCoder
to reconstruct the model object from the filename
file.
saveLearnerForCoder
prepares a machine learning model (Mdl
) for code generation. The function removes some unnecessary properties.
For a model that has a corresponding compact model, the saveLearnerForCoder
function applies the appropriate compact
function to the model before saving it.
For a model that does not have a corresponding compact model, such as ClassificationKNN
, ClassificationLinear
, RegressionLinear
, ExhaustiveSearcher
, and KDTreeSearcher
, the saveLearnerForCoder
function removes properties such as hyperparameter optimization properties, training solver information, and others.
loadLearnerForCoder
loads the model saved by saveLearnerForCoder
.
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
and update
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.
generateLearnerDataTypeFcn
| loadLearnerForCoder
| codegen
(MATLAB Coder)