Use MATLAB® Coder™ or Simulink® Coder together with Deep Learning Toolbox™ to generate MEX or standalone CPU code that runs on desktop or embedded targets. You can deploy the generated standalone code that uses the Intel® MKL-DNN library or the ARM® Compute library. Alternatively, you can generate generic CPU code that does not call third-party library functions.
|Generate C/C++ code from MATLAB code|
|Get the list of layers supported for code generation for a specific deep learning library|
|Load deep learning network model|
|Create deep learning code generation configuration objects|
|MATLAB Coder||Generate C code or MEX function from MATLAB code|
Networks and Layers Supported for Code Generation (MATLAB Coder)
Choose a convolutional neural network that is supported for your target processor.
Load Pretrained Networks for Code Generation (MATLAB Coder)
dlnetwork object for code generation.
Code Generation for dlarray (MATLAB Coder)
Use deep learning arrays in MATLAB code intended for code generation.
This example shows how to generate and deploy code for prediction on an ARM®-based device without using a hardware support package.
This example shows how to use
codegen to generate code for a Logo classification application that uses deep learning on ARM® processors.
This example shows how to use the
codegen command to generate code for an image classification application that uses deep learning on Intel® processors.
Generate code for a trained VAE dlnetwork to generate hand-drawn digits.
This example shows how to generate C++ code for the YOLO v2 Object detection network on an Intel® processor.
This example shows the workflow to classify human electrocardiogram (ECG) signals using the Continuous Wavelet Transform (CWT) and a deep convolutional neural network (CNN).
Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi™.
This example shows how to generate and deploy C++ code that uses the MobileNet-v2 pretrained network for object prediction.
Generate a MEX function that performs image segmentation by using the deep learning network U-Net on Intel CPUs.
Generate a static library that performs image segmentation by using the deep learning network U-Net on ARM targets.
Generate code for a pretrained long short-term memory network to predict Remaining Useful Life (RUI) of a machine.
Generate code for a pretrained LSTM network that makes predictions for each step of an input timeseries.
Generate library or executable code on host computer for deployment on ARM hardware target.
Generate code for deep learning network that performs inference computations in 8-bit integers.
Generate C/C++ code for a trained CNN that does not depend on any third-party libraries.
Generate Code for LSTM Network and Deploy on Cortex-M Target (MATLAB Coder)
Generate a Processor-In-the-Loop (PIL) executable that runs on an STM32F746G-Discovery board.