<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[Hi_Panda]]></title><description><![CDATA[只有坚持不懈的努力，才可以做自己喜欢的事。]]></description><link>https://blog.youkuaiyun.com/daocaorencrl</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; daocaorencrl]]></copyright><item><title><![CDATA[Caffe GNU error]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/70049200</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/70049200</guid><author>daocaorencrl</author><pubDate>Tue, 11 Apr 2017 11:11:35 +0800</pubDate><description><![CDATA[*** Aborted at 1491880114 (unix time) try "date -d @1491880114" if you are using GNU date ***
PC: @     0x7fefd5f82cde (unknown)
*** SIGSEGV (@0x0) received by PID 2769 (TID 0x7fefea08ca40) from PID 0;]]></description><category></category></item><item><title><![CDATA[install opencv with linux]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/69795746</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/69795746</guid><author>daocaorencrl</author><pubDate>Sun, 09 Apr 2017 09:21:17 +0800</pubDate><description><![CDATA[The following steps have been tested for Ubuntu 10.04 but should work with other distros as well.

Required Packages

GCC 4.4.x or laterCMake 2.8.7 or higherGitGTK+2.x or higher, including headers]]></description><category></category></item><item><title><![CDATA[Add sudo authority]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/69791861</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/69791861</guid><author>daocaorencrl</author><pubDate>Sun, 09 Apr 2017 09:10:22 +0800</pubDate><description><![CDATA[12]]></description><category></category></item><item><title><![CDATA[Caffe Layer Library]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/62043799</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/62043799</guid><author>daocaorencrl</author><pubDate>Tue, 14 Mar 2017 15:22:33 +0800</pubDate><description><![CDATA[Convolution layer# convolution
layer {
  name: "loss1/conv"
  type: "Convolution"
  bottom: "loss1/ave_pool"
  top: "loss1/conv"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2]]></description><category></category></item><item><title><![CDATA[Caffe Log Visualization]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/52690232</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/52690232</guid><author>daocaorencrl</author><pubDate>Wed, 28 Sep 2016 14:28:41 +0800</pubDate><description><![CDATA[1.Record your train/test log as a log fileTOOLS=./build/tools  
GLOG_logtostderr=0 GLOG_log_dir=deepid/deepid2/Log/ \  
$TOOLS/caffe train \  
  --solver=deepid/deepid2/deepid_solver.prototxt  2.Parse]]></description><category></category></item><item><title><![CDATA[Caffe --- SyncedMemory]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/52020926</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/52020926</guid><author>daocaorencrl</author><pubDate>Mon, 25 Jul 2016 09:59:49 +0800</pubDate><description><![CDATA[SyncedMemory类定义在syncedmem.hpp/cpp里, 主要负责caffe底层的内存管理.PS: Caffe的底层数据的切换（cpu模式和gpu模式），需要用到内存同步模块。其实个人觉得如果需要研究Blob，对于SyncedMemory的分析很重要内存分配与释放内存分配与释放由两个(不属于SyncedMemory类的)内联函数完成.  
代码简单直观: 如果是CPU模式, 那么调用m]]></description><category></category></item><item><title><![CDATA[Caffe --- blob code]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/52003714</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/52003714</guid><author>daocaorencrl</author><pubDate>Sat, 23 Jul 2016 13:02:21 +0800</pubDate><description><![CDATA[两篇非常好的文章： 
  http://blog.youkuaiyun.com/xizero00/article/details/50886829# 
  http://www.cnblogs.com/yymn/articles/5341347.html]]></description><category></category></item><item><title><![CDATA[Caffe Solver]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51983553</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51983553</guid><author>daocaorencrl</author><pubDate>Thu, 21 Jul 2016 15:46:39 +0800</pubDate><description><![CDATA[Solver
  
  scaffolds the optimization bookkeeping and creates the training network for learning and test network(s) for evaluation.
  iteratively optimizes by calling forward / backward and updating p]]></description><category></category></item><item><title><![CDATA[Blobs, Layers, and Nets: anatomy of a Caffe model]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51983445</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51983445</guid><author>daocaorencrl</author><pubDate>Thu, 21 Jul 2016 15:39:26 +0800</pubDate><description><![CDATA[BlobsBlob作为Caffe的四大模块之一，负责完成CPU/GPU存储申请、同步和数据持久化映射。Caffe内部数据存储和通讯都是通过Blob来完成，Blob提供统一的存储操作接口，可用来保存训练数据、模型参数等。Blob 事实上是调用了SyncedMemory 类。SyncedMemory类封装了CPU/GPU内存申请、同步和释放等。所以SyncedMemory 完成了对内存的实际操作。]]></description><category></category></item><item><title><![CDATA[Python learning]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51981444</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51981444</guid><author>daocaorencrl</author><pubDate>Thu, 21 Jul 2016 11:57:15 +0800</pubDate><description><![CDATA[Packages are a way of structuring Python’s module namespace by using “dotted module names”]]></description><category></category></item><item><title><![CDATA[Python layer]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51966408</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51966408</guid><author>daocaorencrl</author><pubDate>Wed, 20 Jul 2016 11:04:07 +0800</pubDate><description><![CDATA[Fully Convolutional Networks for Semantic Segmentation 论文中公布的代码作为示例，解释python层该怎么写。 
https://github.com/shelhamer/fcn.berkeleyvision.org 
First you have to build Caffe with WITH_PYTHON_LAYER option 1. R]]></description><category></category></item><item><title><![CDATA[caffe net visualization]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51958333</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51958333</guid><author>daocaorencrl</author><pubDate>Tue, 19 Jul 2016 18:20:02 +0800</pubDate><description><![CDATA[net.blobs.items() 存储了预测图片的网络中各层的feature map的数据。
net.params.items()存储了训练结束后学习好的网络参数。
vis_square 函数视觉化data，主要是进行数据归一化，data转换为plt可视化的square结构。
plt.imshow(net.deprocess(‘data’, net.blobs[‘data’].data[4]))]]></description><category></category></item><item><title><![CDATA[caffe net implement]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51957822</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51957822</guid><author>daocaorencrl</author><pubDate>Tue, 19 Jul 2016 17:28:31 +0800</pubDate><description><![CDATA[#include <algorithm>
#include <map>
#include <set>
#include <string>
#include <utility>
#include <vector>#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/net.hpp"
#include "caffe/]]></description><category></category></item><item><title><![CDATA[caffe interface --- python]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51957674</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51957674</guid><author>daocaorencrl</author><pubDate>Tue, 19 Jul 2016 17:17:04 +0800</pubDate><description><![CDATA[#include <Python.h>  // NOLINT(build/include_alpha)// Produce deprecation warnings (needs to come before arrayobject.h inclusion).
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION#include <boost/make_]]></description><category></category></item><item><title><![CDATA[caffe interface --- matlab]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51956137</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51956137</guid><author>daocaorencrl</author><pubDate>Tue, 19 Jul 2016 15:19:16 +0800</pubDate><description><![CDATA[本文首先介绍一些基础的入门知识，然后分析一个大型工程应用caffe_，从工程的视角分析，该如何设计好一个大型的交互接口。同时，找到matlab性能的瓶颈，正是我们需]]></description><category></category></item><item><title><![CDATA[郑帅师兄的五年博士总结]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51861606</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51861606</guid><author>daocaorencrl</author><pubDate>Fri, 08 Jul 2016 15:58:26 +0800</pubDate><description><![CDATA[这五年最重要的，是渐渐知道了怎么去做一件比较大的事情。说得很大，其实就一点，心要静下来。首先，心静下来才能钻进某个领域里认真做事。现在的社交媒体太多了，各类新闻也太多，每天忙于应付这些广泛却又浅薄的信号，或是忙着去评点别人，是没有办法做成一件事情的。就比如一个人要去旅游，按图索骥地走一圈著名的景点，并不会给自己新的体悟，最多只增些与人的谈资而已。真要体会大自然的美丽，那是一定要涉足别人达不到的地方，]]></description><category></category></item><item><title><![CDATA[factor graph，potential function，Template models]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51832930</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51832930</guid><author>daocaorencrl</author><pubDate>Tue, 05 Jul 2016 17:23:26 +0800</pubDate><description><![CDATA[factor是对于variables的某种combination的fitness。在BN中factor就是conditional probability distribution(CPD)；但factor并不总对应着某种概率（当然也不一定取0~1），比如说在MRF中。和数据库table的操作类似，factor上的基本操作有factor product ,factor marginalization]]></description><category></category></item><item><title><![CDATA[dense_CRF]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51788432</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51788432</guid><author>daocaorencrl</author><pubDate>Thu, 30 Jun 2016 10:44:32 +0800</pubDate><description><![CDATA[/*
    Copyright (c) 2013, Philipp Krähenbühl
    All rights reserved.

    Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the followi]]></description><category></category></item><item><title><![CDATA[deeplab script---python]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51553187</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51553187</guid><author>daocaorencrl</author><pubDate>Wed, 01 Jun 2016 07:53:21 +0800</pubDate><description><![CDATA[import os,sys, subprocess

sys.path.insert(0, os.getcwd()+'/python/my_script/')

from tester import tester
from trainer import trainer
from crf_runner import crf_runner, grid_search
import tools

# MO]]></description><category></category></item><item><title><![CDATA[deepLab]]></title><link>https://blog.youkuaiyun.com/daocaorencrl/article/details/51535235</link><guid>https://blog.youkuaiyun.com/daocaorencrl/article/details/51535235</guid><author>daocaorencrl</author><pubDate>Mon, 30 May 2016 07:20:29 +0800</pubDate><description><![CDATA[1.matio can't find HDF5 libraries
change file /densecrf/makefile as:


g++ refine_pascal_v4/dense_inference.cpp util/Timer.h libDenseCRF.a
$(CC) refine_pascal_v4/dense_inference.cpp -o prog_refine]]></description><category></category></item></channel></rss>