深度学习
您可以将 Simulink® Coder™ 与 Deep Learning Toolbox™ 结合使用，从经过训练的卷积神经网络 (CNN) 生成代码。深度学习使用卷积神经网络 (CNN) 直接从图像中学习有用的数据表示。您可以将生成的代码部署到使用 Intel® 或 ARM® 处理器的嵌入式平台。您还可以从不依赖任何第三方库的经过训练的 CNN 生成泛型 C 或 C++ 代码。
主题
- Workflow for Deep Learning C/C++ Code Generation for Simulink Models
Overview of C/C++ code generation workflow for deep learning neural networks.
- Generate Code for Deep Learning Networks Using MATLAB Function Block
Generate code for a model containing a MATLAB Function block that uses the GoogLeNet trained deep learning network.
- Generate Code for Blocks from Deep Neural Networks Library
Generate code for a model containing the GoogLeNet trained deep learning network.
相关信息
- Simulink 深度学习 (Deep Learning Toolbox)
- 使用 MATLAB Coder 进行深度学习
精选示例
Call Generated Code Using C Caller Blocks
Call generated C code from the Simulink model by using a C Caller block.
Simulink Simulation of Deep Learning Models Using MATLAB Function Block
Simulate model that predicts responses for a LSTM network using a MATLAB Function block.
Update the Network Learnables for a Battery State of Charge Estimation Model
Update the learnables of a deep learning network while the Simulink model is simulating.
Generate Generic C/C++ for Sequence-to-Sequence Deep Learning Simulink Models
Generate C/C++ code for a sequence-to-sequence deep learning Simulink model.
Code Generation for Deep Learning Simulink Model That Performs Lane and Vehicle Detection
This example shows how to generate C++ code from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN).
