Deep Learning Code Generation Fundamentals

Functions, objects, and workflows that you can use to generate code for deep learning networks

You can use MATLAB^® Coder™ with Deep Learning Toolbox™ to generate C++ code from a trained CNN. You can then deploy the generated code to an embedded platform that uses an Intel^® or ARM^® processor. You can also generate generic C or C++ code from a trained CNN that does not depend on any third-party libraries.

함수

`codegen`	Generate C/C++ code from MATLAB code
`coder.loadDeepLearningNetwork`	Load deep learning network model
`coder.DeepLearningConfig`	Create deep learning code generation configuration objects
`coder.DeepLearningCodeConfig`	Parameters to configure deep learning code generation that does not depend on third-party libraries (R2021a 이후)
`coder.ARMNEONConfig`	Parameters to configure deep learning code generation with the ARM Compute Library
`coder.CMSISNNConfig`	Parameters to configure deep learning code generation with the CMSIS-NN library for Cortex-M targets (R2022a 이후)
`coder.MklDNNConfig`	Parameters to configure deep learning code generation with the Intel Math Kernel Library for Deep Neural Networks
`analyzeNetworkForCodegen`	Analyze deep learning network for code generation (R2022b 이후)
`coder.regenerateDeepLearningParameters`	Regenerate files containing network learnables and states parameters (R2021b 이후)

도움말 항목

Prerequisites for Deep Learning with MATLAB Coder
Install products and configure environment for code generation for deep learning networks.
Workflow for Deep Learning Code Generation with MATLAB Coder
Generate code for prediction from a pretrained network.
Networks and Layers Supported for Code Generation
Choose a convolutional neural network that is supported for your target processor.
Analyze Network for Code Generation
Check code generation compatibility of a deep learning network.
Code Generation for dlarray
Use deep learning arrays in MATLAB code intended for code generation.
dlarray Limitations for Code Generation
Adhere to code generation limitations for deep learning arrays.
Load Pretrained Networks for Code Generation
Create a SeriesNetwork, DAGNetwork, yolov2ObjectDetector, ssdObjectDetector, or dlnetwork object for code generation.
Generate Generic C/C++ Code for Deep Learning Networks
Generate C/C++ code for prediction from a deep learning network that does not depend on any third-party libraries.
Optimize C/C++ Code Performance for Deep Learning Applications without Deep Learning Libraries
Code generation configuration settings that optimize the performance of the generated C/C++ code for a deep learning network.
Code Generation for Deep Learning Networks with MKL-DNN
Generate C++ code for prediction from a deep learning network, targeting an Intel CPU.
Code Generation for Deep Learning Networks with ARM Compute Library
Generate C++ code for prediction from a deep learning network, targeting an ARM processor.
Cross-Compile Deep Learning Code That Uses ARM Compute Library
Generate library or executable code on host computer for deployment on ARM hardware target.
Generate int8 Code for Deep Learning Networks
Quantize and generate code for a pretrained convolutional neural network.
Generate bfloat16 Code for Deep Learning Networks
Perform learnables compression and generate C/C++ code in Brain Floating Point format, bfloat16.
Update Network Parameters After Code Generation
Perform post code generation updates of deep learning network parameters.

Deep Learning Code Generation Fundamentals

함수

도움말 항목

관련 정보

추천 예제

Generate Digit Images Using Variational Autoencoder on Intel CPUs

Cross Compile Deep Learning Code for ARM Neon Targets

Post-Code-Generation Update of Deep Learning Network Parameters