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deep learning with gpu coder -凯发k8网页登录

generate cuda® code for deep learning neural networks

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. neural networks combine multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems. deep learning models are trained by using a large set of labeled data and neural network architectures that contain many layers, usually including some convolutional layers.

you can use gpu coder™ in tandem with the deep learning toolbox™ to generate code and deploy cnn on multiple embedded platforms that use nvidia® or arm® gpu processors. the deep learning toolbox provides simple matlab® commands for creating and interconnecting the layers of a deep neural network. the availability of pretrained networks and examples such as image recognition and driver assistance applications enable you to use gpu coder for deep learning, without expert knowledge on neural networks, deep learning, or advanced computer vision algorithms.

apps

generate gpu code from matlab code
verify and set up gpu code generation environment

functions

codegengenerate c/c code from matlab code
generate code for a deep learning network to target the arm mali gpu
load deep learning network model
create deep learning code generation configuration objects
analyze deep learning network for code generation
regenerate files containing network learnables and states parameters

objects

parameters to configure deep learning code generation with the cuda deep neural network library
parameters to configure deep learning code generation with the nvidia tensorrt library
configuration parameters for cuda code generation from matlab code by using gpu coder
create configuration object containing the parameters passed to coder.checkgpuinstall for performing gpu code generation environment checks

model settings

(simulink)

basics

deep learning in matlab (deep learning toolbox)

discover deep learning capabilities in matlab using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on gpus, cpus, clusters, and clouds.

(deep learning toolbox)

an introduction to convolutional neural networks and how they work in matlab.

pretrained deep neural networks (deep learning toolbox)

learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.

training

image data workflows (deep learning toolbox)

use pretrained networks or create and train networks from scratch for image classification and regression

code generation overview

overview of cuda code generation workflow for convolutional neural networks.

supported networks, layers, and classes

networks, layers, and classes supported for code generation.

check code generation compatibility of a deep learning network.

use deep learning arrays in matlab code intended for code generation.

adhere to code generation limitations for deep learning arrays.

architecture of the generated cnn class and its methods.

topics

matlab


  • create a seriesnetwork, dagnetwork, yolov2objectdetector, ssdobjectdetector, or dlnetwork object for code generation.

  • generate code for pretrained convolutional neural networks by using the cudnn library.

  • generate code for pretrained convolutional neural networks by using the tensorrt library.

  • generate c code for prediction from a deep learning network targeting an arm mali gpu processor.

  • analyze and optimize the performance of the generated cuda code for deep learning networks.

  • perform post code generation updates of deep learning network parameters.

  • fundamental data layout considerations for authoring example main functions.

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

  • quantize and generate code for a pretrained convolutional neural network.
  • lane detection optimized with gpu coder
    develop a deep learning lane detection application that runs on nvidia gpus.

  • generate cuda mex for a traffic sign detection and recognition application that uses deep learning.

  • generate code and classify an input image into 32 logo categories.
  • code generation for semantic segmentation network that uses u-net
    generate cuda code for the u-net deep learning network for image segmentation.

  • code generation for the segnet image segmentation network.

  • generate cuda mex from matlab code and denoise grayscale images by using the denoising convolutional neural network.

simulink

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