Workflow for Deep Learning Code Generation with MATLAB Coder - MATLAB & Simulink (original) (raw)

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With MATLAB® Coder™, you can generate code for prediction from a pretrained neural network, targeting an embedded platform that uses an Intel® processor or an ARM® processor. The generated code calls the Intel MKL-DNN or ARM Compute Library to apply high performance.

You can also use MATLAB Coder to generate generic C or C++ code for deep learning networks. Such C or C++ code does not depend on third-party libraries.

1. Get a trained network by using Deep Learning Toolbox™. Construct and train the network or use a pretrained network. For more information, see:
   • Deep Learning in MATLAB (Deep Learning Toolbox).
   • Pretrained Deep Neural Networks (Deep Learning Toolbox).
     The network must be supported for code generation. See Networks and Layers Supported for Code Generation.
2. Load a network object from the trained network.
   See Load Pretrained Networks for Code Generation.
3. Generate C++ code for the trained network by using codegen or the MATLAB Coder app. See:
   • Code Generation for Deep Learning Networks with MKL-DNN
   • Code Generation for Deep Learning Networks with ARM Compute Library
   • Generate Generic C/C++ Code for Deep Learning Networks

Related Topics

• Deep Learning in MATLAB (Deep Learning Toolbox)
• Learn About Convolutional Neural Networks (Deep Learning Toolbox)
• Prerequisites for Deep Learning with MATLAB Coder
• Code Generation for Deep Learning Networks with MKL-DNN
• Generate Code for a Deep Learning Network for x86-64 Platforms Using Advanced Vector Instructions
• Code Generation for Deep Learning Networks with ARM Compute Library
• Generate Code and Deploy SqueezeNet Network to Raspberry Pi
• Deep Learning Prediction with ARM Compute Using codegen
• Generate Generic C/C++ Code for Deep Learning Networks
• Deep Learning with GPU Coder (GPU Coder)