配置：Ubuntu16.04+MatlabR2016b+cuda8.0+cudnn5.1+caffe
配置caffe真的不是很容易，特别是對初次接觸Linux的同學，各種報錯(ノ_；＼( ｀ロ´)，搞了好幾天才解決
caffe安裝可能出現的問題
可能會出現的問題
問題1."libcudart.so.8.0 cannot open shared object file: No such file or directory"
解決方法：
解決辦法是将一些檔案複制到/usr/local/lib檔案夾下：
注意自己CUDA的版本号！
sudo cp /usr/local/cuda-8.0/lib64/libcudart.so.8.0 /usr/local/lib/libcudart.so.8.0 && sudo ldconfig
sudo cp /usr/local/cuda-8.0/lib64/libcublas.so.8.0 /usr/local/lib/libcublas.so.8.0 && sudo ldconfig
sudo cp /usr/local/cuda-8.0/lib64/libcurand.so.8.0 /usr/local/lib/libcurand.so.8.0 && sudo ldconfig
問題2."libcudnn.so.5 cannot open shared object file: No such file or directory"
解決方法：
解決辦法是将一些檔案複制到/usr/local/lib檔案夾下
注意自己CUDA的版本号！
sudo cp /usr/local/cuda-8.0/lib64/libcudnn.so /usr/local/lib/libcudnn.so && sudo ldconfig
sudo cp /usr/local/cuda-8.0/lib64/libcudnn.so.5 /usr/local/lib/libcudnn.so.5 && sudo ldconfig
sudo cp /usr/local/cuda-8.0/lib64/libcudnn.so.5.1.5 /usr/local/lib/libcudnn.so.5.1.5 && sudo ldconfig
問題3."OSError: libcudnn.so.7.0: cannot open shared object file: No such file or directory錯誤"
解決方法：
#因為cuda的路徑可能設定錯了
sudo ldconfig /usr/local/cuda/lib64
問題4.linux下Matcaffe調用及庫連結問題的解決（mattest不通過）
解決方法：
編譯make matcaffe後，執行make mattest後，往往出現“Invalid MEX-file"問題，其原因是MATLAB和linux的庫沖突，解決的方法是用linux的庫（在編譯caffe之前大家的opencv等庫肯定也早已裝好了）
大部分的解決方法是通過export LD_LIBRARY_PATH和 LD_PRELOAD來連結，但是效果不好。最後發現，隻有直接去MATLAB下面删除庫并重新連結到x86_64-linux-gnu的方法是最好的。具體方法如下：
1.不需要降級gcc和g++，就用linux的自帶版本，否則caffe編譯不一定通過。我的是14.04的5.4（千萬不要先用5去編譯caffe再降級用4.4編譯matcaffe）
2.不要去用改LIBRARY_PATH的方法，因為很可能不成功，尤其是有倒黴催的anaconda的情況下。
3.找到你的linux庫的位置（一般是/usr/lib/x86_64-linux-gnu/)以及MATLAB庫的位置（預設是/usr/local/MATLAB/R2014a/sys/os/glnxa64/）。然後寫個sh執行下列操作
rm -rf /usr/local/MATLAB/R2014a/sys/os/glnxa64/libstdc++.so.6
ln -s /usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.21 /usr/local/MATLAB/R2014a/sys/os/glnxa64/libstdc++.so.6
rm -rf /usr/local/MATLAB/R2014a/bin/glnxa64/libopencv_core.so.2.4
ln -s /usr/lib/x86_64-linux-gnu/libopencv_core.so.2.4.9 /usr/local/MATLAB/R2014a/bin/glnxa64/libopencv_core.so.2.4
rm -rf /usr/local/MATLAB/R2014a/bin/glnxa64/libopencv_imgproc.so.2.4
sudo ln -s /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.2.4.9 /usr/local/MATLAB/R2017a/bin/glnxa64/libopencv_imgproc.so.2.4
rm -rf /usr/local/MATLAB/R2014a/bin/glnxa64/libopencv_highgui.so.2.4
sudo ln -s /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.2.4.9 /usr/local/MATLAB/R2017a/bin/glnxa64/libopencv_highgui.so.2.4
rm -rf /usr/local/MATLAB/R2014a/bin/glnxa64/libfreetype.so.6
sudo ln -s /usr/lib/x86_64-linux-gnu/libfreetype.so.6 /usr/local/MATLAB/R2017a/bin/glnxa64/libfreetype.so.6
問題5.Invalid MEX-file
\'/home/xw/caffeBuild/caffe-master/matlab/+caffe/private/caffe_.mexa64\':
/home/xw/caffeBuild/caffe-master/matlab/+caffe/private/caffe_.mexa64: undefined
symbol:
_ZN2cv8imencodeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_11_InputArrayERSt6vectorIhSaIhEERKSB_IiSaIiEE
Error in caffe.set_mode_cpu (line 5)
caffe_(\'set_mode_cpu\');
Error in caffe.run_tests (line 6)
caffe.set_mode_cpu();
解決方法：
root@test222:/matlab/r2016a/bin/glnxa64# mv libopencv_imgproc.so.2.4 libopencv_imgproc.so.2.4.bak
root@test222:/matlab/r2016a/bin/glnxa64# mv libopencv_highgui.so.2.4 libopencv_highgui.so.2.4.bak
root@test222:/matlab/r2016a/bin/glnxa64# mv libopencv_core.so.2.4 libopencv_core.so.2.4.bak
root@test222:/matlab/r2016a/bin/glnxa64# ln /usr/lib/x86_64-linux-gnu/libopencv_core.so.2.4.9 libopencv_core.so.2.4
root@test222:/matlab/r2016a/bin/glnxa64# ln /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.2.4.9 libopencv_highgui.so.2.4
root@test222:/matlab/r2016a/bin/glnxa64# ln /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.2.4.9 libopencv_imgproc.so.2.4
export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/:/usr/local/cuda-8.0/lib64
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libopencv_highgui.so.2.4:/usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.2.4:/usr/lib/x86_64-linux-gnu/libopencv_core.so.2.4:/usr/lib/x86_64-linux-gnu/libstdc++.so.6:/usr/lib/x86_64-linux-gnu/libfreetype.so.6
問題6.錯誤：undefined
symbol:
_ZN2cv8imencodeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEERKNS_11_InputArrayERSt6vectorIhSaIhEERKSB_IiSaIiEE
解決方法：
root@test222:/matlab/r2016a/bin/glnxa64# mv libopencv_imgproc.so.2.4 libopencv_imgproc.so.2.4.bak
root@test222:/matlab/r2016a/bin/glnxa64# mv libopencv_highgui.so.2.4 libopencv_highgui.so.2.4.bak
root@test222:/matlab/r2016a/bin/glnxa64# mv libopencv_core.so.2.4 libopencv_core.so.2.4.bak
root@test222:/matlab/r2016a/bin/glnxa64# sudo ln -s /usr/lib/x86_64-linux-gnu/libopencv_core.so.2.4.9 libopencv_core.so.2.4
root@test222:/matlab/r2016a/bin/glnxa64#sudo ln -s /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.2.4.9 libopencv_highgui.so.2.4
root@test222:/matlab/r2016a/bin/glnxa64#sudo ln -s /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.2.4.9 libopencv_imgproc.so.2.4
問題7.警告: 執行 \'caffe.Solver\' 類析構函數時，捕獲到以下錯誤:
錯誤使用 caffe_
Usage: caffe_(\'delete_solver\', hSolver)
出錯 caffe.Solver/delete (line 40)
caffe_(\'delete_solver\', self.hSolver_self);
出錯 caffe.Solver (line 17)
function self = Solver(varargin)
出錯 caffe.test.test_solver (line 22)
self.solver = caffe.Solver(solver_file);
出錯 caffe.run_tests (line 14)
run(caffe.test.test_solver) ...
In caffe.Solver (line 17)
In caffe.test.test_solver (line 22)
In caffe.run_tests (line 14)
解決方法：
https://blog.csdn.net/xiaojiajia007/article/details/72850247
40行：
if ~isempty(self.hNet_self)
caffe_(\'delete_net\', self.hNet_self);
end
if ~isempty(self.hNet_self)
caffe_(\'delete_net\', self.hNet_self);
end
if self.isvalid
caffe_(\'delete_net\', self.hNet_self);
end
問題8.matlab測試
https://blog.csdn.net/weiqi_fan/article/details/71023222
解決方法：
設定GPU
gpu_id = 0
caffe.set_mode_gpu();
caffe.set_device(gpu_id);
問題9.matlab奔潰的問題
解決方法：
https://askubuntu.com/questions/758892/doesnt-matlab-work-on-ubuntu-16-04
問題10.更換caffe版本
解決方法：
https://www.codeleading.com/article/1186958985/
使用新版本的問題：
./include/caffe/util/cudnn.hpp
./include/caffe/layers/cudnn_conv_layer.hpp
./include/caffe/layers/cudnn_relu_layer.hpp
./include/caffe/layers/cudnn_sigmoid_layer.hpp
./include/caffe/layers/cudnn_tanh_layer.hpp
./src/caffe/layers/cudnn_conv_layer.cpp
./src/caffe/layers/cudnn_conv_layer.cu
./src/caffe/layers/cudnn_relu_layer.cpp
./src/caffe/layers/cudnn_relu_layer.cu
./src/caffe/layers/cudnn_sigmoid_layer.cpp
./src/caffe/layers/cudnn_sigmoid_layer.cu
./src/caffe/layers/cudnn_tanh_layer.cpp
./src/caffe/layers/cudnn_tanh_layer.cu
儲存原來的檔案 mv cudnn.hpp cudnn.hpp.bak
layers:
mv cudnn_conv_layer.hpp cudnn_conv_layer.hpp.bak
mv cudnn_relu_layer.hpp cudnn_relu_layer.hpp.bak
mv cudnn_sigmoid_layer.hpp cudnn_sigmoid_layer.hpp.bak
mv cudnn_tanh_layer.hpp cudnn_tanh_layer.hpp.bak
src:
mv cudnn_conv_layer.cpp cudnn_conv_layer.cpp.bak
mv cudnn_conv_layer.cu cudnn_conv_layer.cu.bak
mv cudnn_relu_layer.cpp cudnn_relu_layer.cpp.bak
mv cudnn_relu_layer.cu cudnn_relu_layer.cu.bak
mv cudnn_sigmoid_layer.cpp cudnn_sigmoid_layer.cpp.bak
mv cudnn_sigmoid_layer.cu cudnn_sigmoid_layer.cu.bak
mv cudnn_tanh_layer.cpp cudnn_tanh_layer.cpp.bak
mv cudnn_tanh_layer.cu cudnn_tanh_layer.cu.bak
複制檔案： 源檔案：/home/a/public1/denglei_codeFile/caffe/
目标檔案夾：/home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/
cp /home/a/public1/denglei_codeFile/caffe/include/caffe/util/cudnn.hpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/include/caffe/util/
cp /home/a/public1/denglei_codeFile/caffe/include/caffe/layers/cudnn_conv_layer.hpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/include/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/include/caffe/layers/cudnn_relu_layer.hpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/include/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/include/caffe/layers/cudnn_sigmoid_layer.hpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/include/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/include/caffe/layers/cudnn_tanh_layer.hpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/include/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_conv_layer.cpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_conv_layer.cu /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_relu_layer.cpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_relu_layer.cu /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_sigmoid_layer.cpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_sigmoid_layer.cu /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_tanh_layer.cpp /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
cp /home/a/public1/denglei_codeFile/caffe/src/caffe/layers/cudnn_tanh_layer.cu /home/a/public1/denglei_codeFile/caffe2v/caffe-rpnbf-cudnn5/src/caffe/layers/
問題11.matlab奔潰報錯，/MATLAB/R2016b/bin/glnxa64/libboost_filesystem.so _ZNK5boost1
解決方法：
對gcc,g++版本進行降級
https://blog.csdn.net/betty13006159467/article/details/78394974
問題12.設定protobuf
解決方法：
注意重新編譯protobuf，要使用gcc5 和gvv5，不然後面通不過的
有用的部落格
github
問題13.make runtest -j32 顯示check failed error == cudasuccess (2 vs. 0) out of memory
解決方法：
使用這句話來測試
make runtest -j$(nproc)
參考連結：
很有用的部落格
安裝好caffe之後配置Matlab的接口
MatCaffe用法總結
Ubuntu16.04 Caffe 安裝步驟記錄（超詳盡）
caffe的Matlab接口的使用方法