Load Pretrained Networks for Code Generation - MATLAB & Simulink (original) (raw)
You can load a pretrained deep learning network into MATLAB® and Simulink® for code generation. To generate code from a pretrained network, you load the network using these functions:
- coder.loadDeepLearningNetwork
- imagePretrainedNetwork (Deep Learning Toolbox)
- coder.load
You can also pass the dlnetwork objects:
- Directly to an entry-point function.
- As constant objects to an entry-point function as argument by wrapping it using coder.Constant.
Load Pretrained Networks
You can load a pretrained network in several different ways.
Load a Network Saved in a MAT File by Using coder.loadDeepLearningNetwork
You can load a dlnetwork object from any MAT file for code generation. Save thedlnetwork into a MAT file and specify this MAT file incoder.loadDeepLearningNetwork to load the network. The MAT file must contain only the network that you want to load.
For example, this command loads a MAT file named myNet.
net = coder.loadDeepLearningNetwork('myNet.mat');
Pass the Function Name to coder.loadDeepLearningNetwork to Load a Network
You can use coder.loadDeepLearningNetwork to load a pretrained network by passing the function name that returns adlnetwork or an object detector. For more information on supported classes, see Supported Classes.
For example, write a function getNetwork that loads the Squeeze Network by using imagePretrainedNetwork.
function dlnet = getNetwork()
dlnet = imagePretrainedNetwork("squeezenet");
end
You can load a network object use this code:
net = coder.loadDeepLearningNetwork('getNetwork');
Load a Pretrained Network by Using imagePretrainedNetwork
You can use the imagePretrainedNetwork (Deep Learning Toolbox) function to load the pretrained networks, available in Deep Learning Toolbox™ for code generation.
net = imagePretrainedNetwork("googlenet")
You must download and install the required support package for a pretrained neural network such as googlenet. TheimagePretrainedNetwork function provides a download link. For more information, see Pretrained Deep Neural Networks (Deep Learning Toolbox).
Load a Pretrained Network by Using the coder.load Function
If your pretrained network is adlnetwork object, you can load the network by using thecoder.load function in the entry-point function. You can only use this method to generate generic C/C++ or plain CUDA code. (since R2025a)
For example, this function loads the MAT file as a structure that contains the network:
function out = myNet_predict(matfile, varName, in)
S = coder.load(matfile); dlnet = S.(varName); out = predict(dlnet, in);
end
Generate code for the entry-point function by using the codegen function.
cfg = coder.config('mex'); cfg.DeepLearningConfig = coder.DeepLearningConfig('none');
dlnet = imagePretrainedNetwork('squeezenet'); save('myNet.mat','dlnet');
args = {coder.Constant('dlnet.mat'),coder.Constant('dlnet'), dlarray(ones(224,224,3,'single'), 'SSC')};
codegen -args args -config cfg myNet_predict
Load a Pretrained Network from a Compile-Time Extrinsic Function
You can load a pretrained network by declaring the getNetwork function as an extrinsic function and loading the network as a compile-time constant by using the coder.const function. You can only use this method to generate generic C/C++ or plain CUDA code. (since R2025a)
For example, write a function getNetwork that loads the Squeeze Network by using imagePretrainedNetwork.
function dlnet = getNetwork()
dlnet = imagePretrainedNetwork("squeezenet");
end
Then declare getNetwork as extrinsic function by usingcoder.extrinsic and callgetNetwork in a coder.const statement.
function out = myNet_predict(in)
coder.extrinsic('getNetwork'); dlnet = coder.const(getNetwork()); out = predict(dlnet, in);
end
Generate code for the entry-point function by using the codegen function.
cfg = coder.gpuConfig('mex'); cfg.DeepLearningConfig = coder.DeepLearningConfig('none');
codegen -args {dlarray(ones(224,224,3,'single'), 'SSC')} -config cfg myNet_predict
Pass a Pretrained Network to the Entry-Point Function
If your pretrained network is adlnetwork object, you can load the network by passing the object directly to the entry-point function. You can only use this method to generate generic C/C++ or plain CUDA code. (since R2025a)
Passing a pretrained network directly to an entry-point function is convenient for prototyping, but it is not recommended for generating standalone executables.
For example, this code pass the dlnetwork object as an input to the entry-point function:
function out = myNet_predict(dlnet,in)
out = predict(dlnet, in);
end
Generate code for the entry-point function by using the codegen function.
cfg = coder.gpuConfig('mex'); cfg.DeepLearningConfig = coder.DeepLearningConfig('none'); dlnet = imagePretrainedNetwork('squeezenet'); codegen -args {dlnet, dlarray(ones(224,224,3,'single'), 'SSC')} -config cfg myNet_predict
Limitations
coder.loadDeepLearningNetworkdoes not support loading MAT files that have multiple networks or multiple object detectors.- The locale setting determines the 8-bit ASCII codeset that the code generator uses to represent characters. Therefore, the use of non-ASCII characters in file, folder, or network names can result in errors. For more information, see Encoding of Characters in Code Generation.
See Also
Functions
- codegen | coder.loadDeepLearningNetwork | imagePretrainedNetwork (Deep Learning Toolbox) | coder.load | coder.Constant