caffe windows

Objective:It is easy to install Caffe on a Linux machine with a good system environment, but if the system itself is old and there is no GPU, the installation is too cumbersome and all has to be done from scratch, and this document is designed to cover as much of the pit as possible for installation.Steps:First, the Caffe is mainly written in C + + and Python. First of all, you need to install gcc,g++, thro

Affe is a deep learning library, believe in deep learning, not to use this library is to use Theano bar. The first step to using Caffe is to configure the Caffe environment. Here, I am mainly talking about how to configure the Caffe library in the Debian Linux environment. Because Python is easy to write programs, at the end of the article, I'll also specify how

Comparison between Caffe, TensorFlow, and MXnet open source libraries Recently, Google opened up its internal deep learning framework TensorFlow [1] and discussed the three open-source libraries in combination with the open-source MXNet [2] and Caffe [3, among them, only Caffe has carefully read the source code. The other two libraries only read the official docu

Importerror:no module named Skimage "Issue 1" for dual-card notebook only, through the *.run way to install Cuda, after the restart will be black screen appears unable to enter the desktop, only into the TTY workaround : Use the *.deb method to install CUDA, do not need to install the Nvidia driver (CUDA-7.0 comes with the latest driver), but also do not need to shut down the LIGHTDM service into the TTY terminal to install. Download the Deb offline package installation at Cuda official website

After long-term verification, this blog installed caffe success, in order to facilitate everyone and themselves, recorded, to the later reference1. Install dependent packages2. Disable the original graphics driver Nouveau3. Download cuda8.04, Installation cuda8.05, Installation Cudnn6. Configure Environment variables7, installation OpenCV8, installation Caffe9, install Pycaffe interface environmentThe first step installs the dependency packageInstall

(Caffe + Ubuntu14.0464bit + CUDA6.5 configuration instructions. This document uses the same NVIDIA graphics card for display and computation. If different graphics cards are used for display and computation, they may not apply .) 1. Install build-essential tials install some of the basic packages required for development sudoapt-getinstallbuild-essential2. Install the NVIDIA Driver (3.4.0) (Caffe + Ubuntu 1

This is also troubled me for a long time, before using Http://www.cnblogs.com/platero/p/3993877.html installation method, installed 567,890 times, always a problem.Later found a new method, one night plus half the morning, installed the Ubuntu system (14.04) + NVIDIA driver + CUDA + CAFFE all done. Also ran the Mnist database, Shuangshuang a little problem. Specific steps:1. Install Ubuntu, it is recommended to install English language version (I inst

Tags: modify arc mkdir around Loop 100% proof Port endEven if the installation method is found, everyone's system is somewhat different, there are always some pits to step on to know the actual situation is how. My environment is Lenovo V480 + Ubuntu 16.04 + GeForce GT 645M. The installation process is referenced in this blog--ubuntu 16.04 installation configuration Caffe graphic details. The steps to be completed are: Install related d

Assuming that you have installed all the environment, specific caffe Windows how to install the configuration, Baidu can be known a bit. Here's how to step through the caffe.Compile-select debug mode, which facilitates single-step debugging:Since the default startup project for the entire solution is the caffe below the tool, here we change to classification:such

Original linkDeep Neural Network (DNN) training is a computationally intensive project that takes days or weeks to complete on a modern computing platform. In a recent article on Intel? Xeon? In single-node Caffe scoring and training for the E5 product family, we demonstrated a 10 times-fold performance improvement in the caffe* framework based on the AlexNet topology and reduced the single-node training ti

Has not been the habit of blogging, and later found that the previous work if not pay attention to timely collation and records are often lost quickly. For me this is an important article, good habits to persevere, future days I will be resident blog Park! Because this cock level is limited, the IQ is slightly low, welcome big God come to shoot brick. End of nonsense, here is the dry goods:First of all, I spit a bit of Ben's notebook, my current notebook is still a freshman bought Dell INSPIRON

Building environmental referenceshttp://blog.csdn.net/ubunfans/article/details/47724341This tutorial is basically correct.One thing to add isMake All-j4 After that, a lot of *.bin files are generated below build/bin/to prove that the compilation was successful.The following is the run Mnist, performed to create_mnist.sh this step of the time encountered a problem:./create_mnist.sh:build/examples/mnist/convert_mnist_data.bin:not foundIt's going to change the catalog.Note: The new

Refer to the two-bit bloghttp://caffe.berkeleyvision.org/official websitehttp://blog.csdn.net/u013476464/article/details/38071075 caffe+ubunutu14.04 +cuda 6.5 Installation Guidehttp://blog.csdn.net/bebelemon/article/details/25567239 ubuntu12.04 under Configuration CaffeHttp://www.mintos.org/config/ubuntu-nvidia-prime.html (important for ubuntu14.04 's words)Error while loading shared libraries:xxx.so.x "causes and workarounds for" errorsThe 20+ has be

In the process of training and testing data sets using open-source deep learning Framework (Caffe), we will inevitably want to visualize some training data in our training process, this article mainly introduces how to use the tools of Caffe to visualize the error curve and the precision curve in the course of CIFAR10 training and testing. 0. Preparation, the CIFAR data set has been downloaded, and the form

First step, install Pycaffe Notebook interface Environment After the successful installation of Caffe in the previous step, it is possible to do the training data set through Caffe or predict various related things, just need to operate through the Caffe command at the command line, and this step Pycaffe installation and notebook environment configuration is ju

Caffe's own example of a new project, mainly the configuration include Lib DLLs are pits, but also divided into debug and release two versions.and add input items to be aware of, but also need to be compiled caffe.lib and so on a series of things to copy under the current project.Caffe's other pit is: F0519 14:54:12.494139 14504 layer_factory.hpp:77] Check failed:registry.count (t ype) = = 1 (0 vs. 1) Unknown Layer Type:input (known types:input) was originally to add header files! Http://blog.cs

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

following instructions to install: sudo make install error may occur: Error content 1:gcc-4.9:error trying to exec ' cc1plus ': execvp: no that file or directory description GCC is incompatible with the g++ version, as is the case with the GCC version: Install the lower version of GCC and g++:sudo apt-get install gcc-4.9 g++-4.9 after entering/usr/bin : Cd/usr/bin first Delete and gcc5.0 associated Gcc:sudo RM gccsudo RM g++ Build a soft connection sudo ln-s gcc-4.9 gccsudo ln-s g++-4.9 g++ Err

Generally do not want to use Caffe Matlab interface, always feel that the Linux version of MATLAB is difficult to configure, but now engage in target detection, the source code is used Caffe Matlab interface, can only bite the bullet on the.(1) Modify Caffe-master/makefile.configThis step is mainly to add the path to MATLAB in Caffe's compiled configuration file

This is the fourth example in the official Caffe document notebook examples, link address: http://nbviewer.jupyter.org/github/bvlc/caffe/blob/master/examples/03- Fine-tuning.ipynb This example is used to fine-tune flickr_style data on a trained network. Fine-tune your data with a trained Caffe network. The advantage of this approach is that with the training netw