Generate Generic C/C++ Code for Deep Learning Networks

With MATLAB^® Coder™, you can generate generic C or C++ code for prediction from an already trained neural network. The generated C/C++ code does not depend on any third-party libraries. The generated code implements a neural network with the architecture, layers, and parameters specified in the input SeriesNetwork (Deep Learning Toolbox) or DAGNetwork (Deep Learning Toolbox) network object. See Networks and Layers Supported for Code Generation.

Generate code by using one of these methods:

• The standard codegen command for C/C++ code generation from MATLAB code.

• The MATLAB Coder app.

Requirements

• On Windows^®, code generation for deep learning networks with the codegen function requires Microsoft^® Visual Studio^® or the MinGW^® compiler.

• MATLAB Coder Interface for Deep Learning. To install this support package, select it from the MATLAB Add-Ons menu.

• Deep Learning Toolbox™.

Code Generation by Using codegen

1. Write an entry-point function in MATLAB that:

   • Uses the coder.loadDeepLearningNetwork function to construct and set up a network object. For more information, see Load Pretrained Networks for Code Generation.

   • Calls the predict (Deep Learning Toolbox) method of the network on the entry-point function input.

   • Specifies a MiniBatchSize in the predict method to manage memory usage for prediction on multiple input images or observations.

   For example:

   function out = my_predict(in) %#codegen
   
   % A persistent object mynet is used to load the series network object.
   % At the first call to this function, the persistent object is constructed and
   % setup. When the function is called subsequent times, the same object is reused 
   % to call predict on inputs, thus avoiding reconstructing and reloading the
   % network object.
   
   persistent mynet;
   
   if isempty(mynet)
       mynet = coder.loadDeepLearningNetwork('myNetwork.mat');
   end
   
   % pass in input   
   out = predict(mynet,in,'MiniBatchSize',2); 

2. Create a deep learning configuration object dlconfig that is configured for generating generic C/C++ code by using the coder.DeepLearningConfig function.

   dlconfig = coder.DeepLearningConfig(TargetLibrary='none');

   Create a code generation configuration object for MEX or for a static or dynamically linked library. By default, the code generator produces generic C code. To produce generic C++ code, in your code generation configuration object, set the TargetLang parameter to 'C++'. Set the DeepLearningConfig parameter to the previously created object dlconfig.

   cfg = coder.config('lib');
   cfg.TargetLang = 'C++';
   cfg.DeepLearningConfig = dlconfig;

3. Run the codegen command. Use the -config option to specify the configuration object. Use the -args option to specify the input type.

   codegen -config cfg my_predict -args {myInput} -report

   Note

   You can specify half-precision inputs for code generation. However, the code generator type casts the inputs to single-precision. The Deep Learning Toolbox uses single-precision, floating-point arithmetic for all computations in MATLAB.

Code Generation by Using the MATLAB Coder App

1. Follow the usual steps for specifying the entry-point function and specifying input types. See Generate C Code by Using the MATLAB Coder App.

2. In the Generate Code step:

   • Set Language to either C or C++.

   • Click More Settings. In the Deep Learning pane, set Target library to None.

     

3. Generate code.