caffe windows

Disclaimer: The Caffe series is an internal learning document written by our lab Huangjiabin god, who has been granted permission to do So.This reference is made under the Ubuntu14.04 version, and the required environment for the default Caffe is already configured, and the following teaches you how to build the kaiming He residual network (residual network).Cite:he K, Zhang X, Ren S, et al residual learnin

-------------------------------------------------------------------------------- Visualization of weight values After training, the network weights can be visualized to judge the model and whether it owes (too) fit. Well-trained network weights usually appear to be aesthetically pleasing, smooth, whereas the opposite is a noisy image, or the pattern correlation is too high (very regular dots and stripes), or lack of structural or more ' dead ' areas. zz@zz-inspiron-5520:~$ CD

Deep learning is an important tool for the study of computer vision, especially in the field of image classification and recognition, which has epoch-making significance. Now there are many deep learning frameworks, and Caffe is one of the more common ones. This article describes the basic steps for configuring Caffe in the Ubuntu 14.04 (64-bit) system, referring to the official website of

This record is mainly referenced in: Http://blog.csdn.net/yhaolpz 71375762This record is based on the above reference, modified CPU version Caffe installation steps.1th Step Installation CaffeFirst, under the path you want to install, clone:clone https://github.com/BVLC/caffe.gitEnter Caffe, copy the Makefile.config.example file and rename it to Makefile.config, or call the following command directly in the

0. About Caffe (by@ cold sunny Little) Caffe is a widely used framework for depth learning in the Image field, and its model zoo has a large number of predefined models available for use. Image-related applications are heavily used to Caffe. Wall crack recommends that you use the Linux system for the following reasons. Linux system (most companies for CentOS or

Caffe Official website: http://caffe.berkeleyvision.org/Initializing the network1#include"caffe/caffe.hpp"2#include string>3#include 4 using namespaceCaffe;5 6 Char*proto ="H:\\models\\caffe\\deploy.prototxt";/*load the caffenet configuration*/7Phase Phase = TEST;/*or TRAIN*/8 Caffe::set_mode (

---restore content starts---Recent attempts to install the Caffe on Mac (OS X 10.11 El Capitan) and the Python interface have encountered some problems but the official installation tutorials did not provide a solution to these problems for a long time (mainly on the Python interface) Finally found a way out.In fact, the installation of Caffe in two steps: Install dependencies + Compile the source codeThe f

Original from: http://www.shwley.com/index.php/archives/68/ Objective To be honest, there are more layer layers in the Caffe, and the various abstractions look rather round. The official tutorial on layer is very clear, I based on this document, a simple picture, and then understand the convenience of some. Layer.hpp The header files associated with layer are: COMMON_LAYERS.HPP data_layers.hpp layer.hpp loss_layers.hpp neuron_layers.hpp vision_ Layer

;(3) Convert the positive and negative samples to the Lmdb format :There is a convert_imageset.exe file in Build->x64->debug under the Windows downgrade Caffe install root directory to make Lmdb files (some people may only Have. cpp, Then you will need to build The. exe via vs Compilation)Under linux, call the create_imagenet.sh file in examples->imagenet and rewrite it (see related blog online)Here I descr

Original address: https://www.zhihu.com/question/27982282 Gein Chen's answer many thanks —————————————————————————————————————————— 1. The first step of learning the program, first let the program run, see the results, so that there will be an intuitive feeling.Caffe's official Online Caffe | The Deep learning Framework provides a lot of examples, and you can easily start to train some existing classic models, such as lenet. I suggest starting with th

Reference website:Http://www.cnblogs.com/darkknightzh/p/5909121.html (under Linux)Http://www.mamicode.com/info-detail-1338521.html (under Windows)Http://www.cnblogs.com/denny402/p/5082341.html (the simplest and most straightforward example)http://www.2cto.com/kf/201607/527860.html (mnist data format and Lmdb conversion Code analysis)HTTP://NBVIEWER.JUPYTER.ORG/GITHUB/BVLC/CAFFE/BLOB/MASTER/EXAMPLES/00-CLASS

I only changed two numbers, and then, all errors, missing, two days toss, all is poor toss.The thing is, other than the official version, other tutorials without the official doc are bullying.Some people say that the official said Anaconda+python very simple good configuration, why, I so many errors, and finally have to use PIP, because the official configuration document is Makefile.config inside is anaconda2+ python2.7, if you install the above version, then you are very simple, but the old ve

System: ubuntu16.04 Graphics card: GTX1060 cuda8.0,cudnn8.0, opencv3.1 Before the caffe,linux in Windows have tried, but did not succeed, so take advantage of this period of time to tackle the key. After several toss, finally succeeded in setting up a good caffe in Ubuntu, this record the hole encountered, for inspection. Please post the reference post, thank th

Rgb2gray Convert the RGB graph to grayscale after reading the picture into the inputs inputs = [Rgb2gray (input) for input in inputs] Change Channel_swap: Channel_swap = [0] . py file changes This is the case, if your model is not the same, you need to take a different change, extrapolate. Prepare a picture as an input image, this image must be 28*28 size JPEG format picture, content is arbitrary, but after all, mnist for classifying numbers, we use the Win