Consolidated Source: angle_cal 2016-12-19 17:32 624 ℃ 0 Reviews The BLVC version of Caffe-windows already supports visual Studio 2015, and the following configuration process is integrated with the experience of others and is validated to ensure effectiveness.
Download Caffe-windows (BLVC): GitHubDownload good unzip.
install vs2015,cuda,cudnn,anaconda,cm
Makefile.config, the corresponding comments in this document is very clear, which need, bar corresponding to the previous "#" to remove the goodFirst take make all test, the error is as follows:MAKE:PROTOC: Command not foundMake: * * * [. build_release/src/caffe/proto/caffe.pb.h] Error 1272. Compiling and installing PROTOCdownload protobuf, download it on GitHub, unzip it Https://github.com/google/protobuf :Run$ ./autogen.shoriginally to
Caffe (convolution Architecture for Feature Extraction) as a very hot framework for deep learning CNN, for Beginners, Build Linux under the Caffe platform is a key step in learning deep learning, its process is more cumbersome, recalled the original toss of those days, then summed up the Ubuntu14.04 configuration process, convenient later novice can be less detours.1. Installing Build-essentialsInstall some
Commonsettings.prop Modify:To compile each project before setting:Properties---Property---Configuration Properties->c/c++-> preprocessor---preprocessor definitions, adding an item cpu_only4. Generate LibcaffeOpen the Caffe.sln under Caffe-mastetr/windowsAll projects need to be libcaffe.lib, so the first one to compile this project.Libcaffe has a lot of relevant libraries, and the project has been configured to download with NuGet.We need to
Caffe python feature extraction reprint http://www.cnblogs.com/louyihang-loves-baiyan/Caffe we generally use the deep learning platform is this, about Caffe training usually can be carried out by some commands, but in the Deploy phase, if the actual project, then the C + + interface will be relatively more. But Caffe i
This are based on Caffe GitHub Wiki Guide (https://github.com/BVLC/caffe/wiki/ Ubuntu-16.04-or-15.10-installation-guide)Some parts of it has been changed to suit my computer. The following guide includes the how-to instructions for the installation of Bvlc/caffe in Ubuntu 16.04 (preliminary proce Dure does not function with the current Cuda Toolkit) or 15.10 Lin
Basic EnvironmentIt is recommended to strictly follow the version-Windows 10-Visual Studio 2013-Matlab r2016b-Anaconda-CUDA 8.0.44-CuDNN V4
1. Installing Cuda 8.0After installation, the program automatically adds a CUDA_PATH environment variable:
2. Download CUDNNBefore downloading, you need to register a number on the developer online, simply fill in the basic material.The next is a compressed package, there is no way to install, the comp
root user and turn off desktop services:# service lightdm stopThen enter the following command:# vim /etc/modprobe.d/blacklist.confOn the last side of the file, add this sentence:blacklist nouveauReboot, then enter TTY1, log in to the root user:
# chmod +x Cuda*.run #获取文件权限
#./cuda*.run #执行文件安装
Be sure not to install the Nvidia driver again during the selection process, otherwise the previously installed graphics drivers will be bro
1. Installation
Mac Install Caffe can refer to a previous wiki (install Caffe under Mac), of course, if you encounter other problems, please Google.
For a variety of Linux systems, there are already a lot of tutorials on the web.
2.caffe code and Architecture level Brief int
Caffe (convolution Architecture for Feature Extraction) as a very hot framework for deep learning CNN, for Beginners, Build Linux under the Caffe platform is a key step in learning deep learning, its process is more cumbersome, recalled the original toss of those days, then summed up the Ubuntu14.04 configuration process, convenient later novice can be less detours. 1. Installing Build-essentials
Why do you learn Caffe? Because through the Caffe can comprehensive study deep Learning,cuda,spark (Caffeonspark), cudnn,openblas,mkl,c++,python,linux and other knowledge. Before installing Caffe, it is important to install Cuda,blas and so on, CuDNN and Python are optional. Caffe
-dev Libavformat-dev Libswscale-devsudo apt-get install Python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc139 4-22-devsudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev Libhdf5-serial-dev Libgflags-dev Libgoogle-glog-dev Liblmdb-dev Protobuf-compilerPS: Copy and paste too long command can be due to the browser cause
install the OPENCV, the process is about twenty or thirty minutes, slowly wait.Iv. installation and compilation of CaffeCaffe:https://github.com/bvlc/caffeAfter downloading, go to the Caffe directory to executeCP Makefile.config.example Makefile.configThen modify the Blas:= atlas in Makefile.config to Blas: = Mkl, Save the exit on the line.Finally, compile and execute the following three commands:Make allM
On a whim, I want to play all kinds of deep learning hot tools (Caffe, Theano, etc.) in the spare time of the week before the internship, but the pain of installing and configuring the environment ... It took me two days to install Caffe, and I had a lot of circles around the documentation tutorials. Incomplete statistics, some of the useful references to me are
ppa:graphics-drivers/ppasudo apt-get update3. Runsudo apt-get install nvidia-3754. Run sudo rebootHere is a very tricky point, my time is mainly spent here, looked up a lot of information, before all kinds of machine reasons I only understand a few of them, here a little tidy up:1. Choose Nvidia driver with additional drivers set in Ubuntu, fail to install completely2. Blacklist the Nouveau, use Alt+ctrl+f
system library directory. You can determine your system library directory by querying the location of your library:sudo find/-name libstdc++.so.6Re-run make mattest, problem solving ~(4) Try the interface in MATLABLinks: Http://dl.caffe.berkeleyvision.org/bvlc_reference_caffenet.caffemodelDownload it and put it in the folder/caffe-master/models/bvlc_reference_caffenet this is because a running demo will use this model.The OK, everybody cheer ~Resourc
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