Deep Learning Code Generation

Generate MATLAB® code or CUDA® and C++ code and deploy deep learning networks

Use Deep Network Designer to generate MATLAB code to construct and train a network.

Use MATLAB Coder™ or GPU Coder™ together with Deep Learning Toolbox™ to generate C++ or CUDA code and deploy convolutional neural networks on embedded platforms that use Intel®, ARM®, or NVIDIA® Tegra® processors.

Functions

dlquantizerQuantize a deep neural network to 8-bit scaled integer data types
dlquantizationOptionsOptions for quantizing a trained deep neural network
calibrateSimulate and collect ranges of a deep neural network
validateQuantize and validate a deep neural network

Apps

Deep Network QuantizerQuantize a deep neural network to 8-bit scaled integer data types

Topics

Deep Learning Quantization

Quantization of Deep Neural Networks

Understand effects of quantization and how to visualize dynamic ranges of network convolution layers.

MATLAB Code Generation

Generate MATLAB Code from Deep Network Designer

Generate MATLAB code to recreate designing and training a network in Deep Network Designer.

GPU Code Generation

Deep Learning with GPU Coder (GPU Coder)

Generate CUDA code for deep learning neural networks

Code Generation for Deep Learning Networks

This example shows how to perform code generation for an image classification application that uses deep learning.

Code Generation for a Sequence-to-Sequence LSTM Network

This example demonstrates how to generate CUDA® code for a long short-term memory (LSTM) network.

Deep Learning Prediction on ARM Mali GPU

This example shows how to use the cnncodegen function to generate code for an image classification application that uses deep learning on ARM® Mali GPUs.

Code Generation for Object Detection by Using YOLO v2

This example shows how to generate CUDA® MEX for a you only look once (YOLO) v2 object detector.

Lane Detection Optimized with GPU Coder

This example shows how to generate CUDA® code from a deep learning network, represented by a SeriesNetwork object.

Integrating Deep Learning with GPU Coder into Simulink

This example shows how to integrate the CUDA® code generated for a deep learning network into Simulink®.

Deep Learning Prediction by Using NVIDIA TensorRT

This example shows code generation for a deep learning application by using the NVIDIA TensorRT™ library.

Deep Learning Prediction by Using Different Batch Sizes

This example demonstrates code generation with batch sizes greater than 1.

Traffic Sign Detection and Recognition

This example shows how to generate CUDA® MEX code for a traffic sign detection and recognition application that uses deep learning.

Logo Recognition Network

This example shows code generation for a logo classification application that uses deep learning.

Pedestrian Detection

This example shows code generation for pedestrian detection application that uses deep learning.

Code Generation for Denoising Deep Neural Network

This example shows how to generate CUDA® MEX from MATLAB® code and denoise grayscale images by using the denoising convolutional neural network (DnCNN [1]).

Code Generation for Semantic Segmentation Network

This example shows code generation for an image segmentation application that uses deep learning.

Train and Deploy Fully Convolutional Networks for Semantic Segmentation

This example shows how to train and deploy a fully convolutional semantic segmentation network on an NVIDIA® GPU by using GPU Coder™.

Code Generation for Semantic Segmentation Network by Using U-net

This example shows code generation for an image segmentation application that uses deep learning.

Deep Learning Prediction on ARM Mali GPU (GPU Coder)

This example shows how to use the cnncodegen function to generate code for an image classification application that uses deep learning on ARM® Mali GPUs.

Code Generation for a Sequence-to-Sequence LSTM Network (GPU Coder)

This example demonstrates how to generate CUDA® code for a long short-term memory (LSTM) network.

CPU Code Generation

Code Generation for Deep Learning on ARM Targets

This example shows how to generate and deploy code for prediction on an ARM®-based device without using a hardware support package.

Code Generation for Deep Learning on Raspberry Pi

This example shows how to generate and deploy code for prediction on a Raspberry Pi™ by using codegen with the MATLAB Support Package for Raspberry Pi Hardware.

Deep Learning Prediction with ARM Compute Using cnncodegen

This example shows how to use cnncodegen to generate code for a Logo classification application that uses deep learning on ARM® processors.

Deep Learning Prediction with Intel MKL-DNN

This example shows how to use codegen to generate code for an image classification application that uses deep learning on Intel® processors.

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

This example shows how to generate C++ code for the YOLO v2 Object detection network on an Intel® processor.

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

This example shows how to generate and deploy C++ code that uses the MobileNet-v2 pretrained network for object prediction.

Code Generation for Semantic Segmentation Application on Intel CPUs That Uses U-Net (MATLAB Coder)

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

Code Generation for LSTM Network on Raspberry Pi (MATLAB Coder)

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

Cross Compile Deep Learning Code for ARM Neon Targets (MATLAB Coder)

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

Load Pretrained Networks for Code Generation (MATLAB Coder)

Create a SeriesNetwork, DAGNetwork, yolov2ObjectDetector, or ssdObjectDetector object for code generation.

Deep Learning with MATLAB Coder (MATLAB Coder)

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

Featured Examples