Deep Learning withMATLABCoder

Generate C++ code for deep learning neural networks (requires Deep Learning Toolbox™)

Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. The learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Deep learning uses convolutional neural networks (CNNs) to learn useful representations of data directly from images.

You can useMATLAB^®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.

Deep Learning withMATLAB Coderis not supported inMATLAB Online™.

Functions

`codegen`	Generate C/C++ code fromMATLABcode
`coder.loadDeepLearningNetwork`	Load deep learning network model
`coder.DeepLearningConfig`	Create deep learning code generation configuration objects
`coder.ARMNEONConfig`	Parameters to configure deep learning code generation with theARMCompute Library
`coder.CMSISNNConfig`	Parameters to configure deep learning code generation with the CMSIS-NN library for Cortex-M targets
`coder.MklDNNConfig`	Parameters to configure deep learning code generation with theIntelMath Kernel Library for Deep Neural Networks
`analyzeNetworkForCodegen`	Analyze deep learning network for code generation
`coder.regenerateDeepLearningParameters`	Regenerate files containing network learnables and states parameters

Topics

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 aSeriesNetwork,DAGNetwork,yolov2ObjectDetector,ssdObjectDetector, ordlnetworkobject 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.
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.
Update Network Parameters After Code Generation
Perform post code generation updates of deep learning network parameters.

Related Information

Get Started with Deep Learning Toolbox(Deep Learning Toolbox)
Deep Learning with GPU Coder(GPU Coder)

Featured Examples

Deep Learning Code Generation on Intel Targets for Different Batch Sizes

Generate code for deep learning networks, targeting an Intel processor.

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Generate Digit Images Using Variational Autoencoder on Intel CPUs

为一个训练有素的生成代码VAE dlnetwork to generate hand-drawn digits.

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Code Generation for Deep Learning on ARM Targets

Code generation with ARM Compute Library when you do not have a hardware support package.

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Deep Learning Prediction with ARM Compute Using codegen

Generate code for deep learning networks, targeting an ARM processor.

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Code Generation and Deployment of MobileNet-v2 Network to Raspberry Pi

Use the MobileNet-V2 network for object prediction on external hardware.

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Generate C++ Code for Object Detection Using YOLO v2 and Intel MKL-DNN

Generate code for object detection on an Intel CPU.

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Code Generation for Semantic Segmentation Application on Intel CPUs That Uses U-Net

Generate a MEX function that performs image segmentation by using the deep learning network U-Net on Intel CPUs.

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Code Generation for Semantic Segmentation Application on ARM Neon Targets That Uses U-Net

Generate a static library that performs image segmentation by using the deep learning network U-Net on ARM targets.

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Code Generation for LSTM Network on Raspberry Pi

Generate code for a pretrained long short-term memory network to predict Remaining Useful Life (RUI) of a machine.

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Code Generation for LSTM Network That Uses Intel MKL-DNN

Generate code for a pretrained LSTM network that makes predictions for each step of an input timeseries.

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Code Generation for Convolutional LSTM Network That Uses Intel MKL-DNN

Generate a MEX function for a deep learning network containing both convolutional and BiLSTM layers that classifies videos

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Cross Compile Deep Learning Code for ARM Neon Targets

Generate library or executable code on host computer for deployment on ARM hardware target.

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Generate INT8 Code for Deep Learning Network on Raspberry Pi

Generate code for deep learning network that performs inference computations in 8-bit integers.

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Generate INT8 Code for Deep Learning Network on Cortex-M Target

Generate code for deep learning network that performs inference computations in 8-bit integers.

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Generate Generic C/C++ Code for Sequence-to-Sequence Regression That Uses Deep Learning

Generate C/C++ code for a trained CNN that does not depend on any third-party libraries.

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Generate Code for LSTM Network and Deploy on Cortex-M Target

生成一个Processor-In-the-Loop(公益诉讼)执行that runs on an STM32F746G-Discovery board.

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Post-Code-Generation Update of Deep Learning Network Parameters

Incrementally update the network learnables of a deep learning network application running on edge devices such as Raspberry Pi. This example uses a cart-pole reinforcement learning application to illustrate:

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Prune Filters in a Detection Network Using Taylor Scores

Reduce network size and increase inference speed by pruning convolutional filters in a you only look once (YOLO) v3 object detection network.

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