coder.DeepLearningCodeConfig

Create an entry-point function resnet50 that uses the imagePretrainedNetwork function to load the dlnetwork object that contains the ResNet-50 network. For more information, see Code Generation for dlarray.

function out = resnet_predict(in)

dlIn = dlarray(in, 'SSCB');
persistent dlnet;
if isempty(dlnet)
    dlnet = imagePretrainedNetwork('resnet50');
end

dlOut = predict(dlnet, dlIn);
out = extractdata(dlOut);

Create a coder.config configuration object for MEX code generation.

cfg = coder.config('mex');

Set the target language to C++.

cfg.TargetLang = 'C++';

Create a coder.DeepLearningCodeConfig deep learning configuration object. Assign it to the DeepLearningConfig property of the cfg configuration object.

dlcfg = coder.DeepLearningConfig(TargetLibrary = 'none');
cfg.DeepLearningConfig = dlcfg;

Use the -config option of the codegen function to specify the cfg configuration object. The codegen function must determine the size, class, and complexity of MATLAB^® function inputs. Use the -args option to specify the size of the input to the entry-point function.

codegen -args {ones(224,224,3,'single')} -config cfg resnet_predict

The codegen command places the generated files in the codegen folder. This folder contains the C++ code for the entry-point function resnet_predict.cpp, the header, and the source files that contain the C++ class definitions for the neural network, weight, and bias files.