installation Caffe Instruction Book
Environment: Linux 64-bit
Video card for Intel + AMD, non-NVIDIA graphics
No GPU
I. Installation Preparation work
1. log in as Administrator
In the top left corner, click on the icon to search for terminal (that is, terminal) and log in as Super Administrator:
Command: sudo su
Input password can be
2. Install Boost
Command: sudo apt-get install Libboost-all-dev
3. Installing BLAS(Intel MKL)
(1) Download Intel MKL Library and install it after decompression
: https://software.intel.com/en-us/intel-mkl/to apply for free software using the school mailbox/research unit address, and receive the serial number and the address of the downloaded software in the mailbox.
Go to/TMP and unzip
Command: TAR–ZXVF l_mkl_11.3.0.109.tgz
(2) Installing the MKL
Command: sudo sh install.sh
4. Dependent Library
Command:
sudo apt-get install Libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev Libhdf5-serial-dev Protobuf-compiler Liblmdb-dev
5. Glog
Command:
wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
Tar zxvf glog-0.3.3.tar.gz
CD glog-0.3.3
sudo sh./configure
sudo make && sudo make install
6. Lib Path
(1) Open/build mkl.conf file
Command: sudo gedit/etc/ld.so.conf.d/mkl.conf
Input content
/opt/intel/lib/intel64
/opt/intel/mkl/lib/intel64
(2) Open/build cuda.conf file
Command: sudo gedit/etc/ld.so.conf.d/cuda.conf
Input content:
/usr/local/cuda/lib64
/usr/local/cuda/lib
(3) Immediate effect
Command: sudo ldconfig
7. Installing protoc-c
Command: sudo apt-get install Protobuf-c-compiler Protobuf-compiler
8. Installing gflags
Command: wget https://github.com/schuhschuh/gflags/archive/master.zip
Unzip Master.zip
CD Gflags-master
mkdir Build && CD Build
Export cxxflags= "-fpic" && cmake. && make verbose=1
Make
sudo make install
9. Installing lmdb
Method One: (Until now, this download is being maintained, so I use the second way)
Command: Git clone git://gitorious.org/mdb/mdb.git
CD Mdb/libraries/liblmdb
Make
sudo make install
Method Two:
(1) Download Lmdb
: Https://github.com/LMDB/lmdb Click on the download on the right, download the ZIP format
(2) Decompression
Command: CD tmp
Unzip Lmdb_mdb.master.zip
CD Lmdb_mdb.master/libraries/liblmdb
Make
sudo make install
installation Caffe
(1) Download Caffe
Command: Git clonegit://github.com/bvlc/caffe.git
(2) Installation Caffe
Command: CD Caffe
CP Makefile.config.example Makefile.config
Two. Compiling:
1. Modify makefile.config three places
cpu_only:= 1 Comment (#) removed
Use_cudnn:=0
Blas Select Mkl
Command: VI makefile.config
At the cursor that needs to be modified, press the INSERT key to modify it (if it is wrong, do not press the left and right arrows, press ESC, then Modify), and press ESC to exit the edit.
If the modification succeeds, save the command:: Wq, do not save the command:: q!
2. compiling
Command: Make all
Make Test
Make Runtest
Three. Running Lenet in Mnist
1. get mnist data First
Command: CD Caffe
./data/mnist/get_mnist.sh
2. Create lenet
Command:./examples/mnist/create_mnist.sh
Note Be sure to run the following command in the root directory of the Caffe, otherwise you will be reported "Build/examples/mnist/convert_mnist_data.bin:not found" error.
3. Train CNN
(1) Modification
If you don't have a GPU, remember to set the Solver_mode in Caffe/examples/mnist/lenet_solver.prototxt to SOLVER_MODE:CPU.
(2) Train CNN to execute in the root directory:
Command:./examples/mnist/train_lenet.sh
Accuracy can reach 0.9912
Four. Install the Python interface
Since Python is 2.7.6 under its own Linux system, it has not been reinstalled. If the version is too old, it is recommended to reinstall it. Reference: http://blog.csdn.net/pan_tian/article/details/7684409
1. Install Pip
sudo apt-get install Python-pippython-dev build-essential
2. Configure the path, edit Makefile.config
Python_include: =/usr/include/python2.7 \
/usr/lib/python2.7/dist-packages/numpy/core/include
Python_lib: =/usr/local/lib
Include_dirs: = $ (python_include)/usr/local/include
Library_dirs: = $ (python_lib)/usr/local/lib/usr/lib
3. Run the following code to install the necessary dependencies:
sudo pip install-r./python/requirements.txt
sudo apt-get install python-numpy python-scipypython-matplotlib python-sklearn python-skimage python-h5py Python-protobufpython-leveldb python-networkx python-nose Python-pandas python-gflags Cythonipython
4. Run in the root directory of the Caffe:
Make Pycaffe
5. If Python path addition fails, consider the following methods: (Small series solved with this method)
Log in with super privileges, set environment variables
Command: sudo gedit/etc/profile
Enter at the bottom of the document: (Hint: The path entered after Pythonpath= is the Caffe path installed under Linux)
Pythonpath=caffe/python: $PYTHONPATH
Export PYTHONPATH
Command: Source/etc/profile
Python
Import Caffe
6.test:
Command: Python draw_net.py <protext> <outfile>
e.g. ./python/draw_net.py./examples/mnist/lenet_train_test.prototxt lenet.png
Note: The Graphviz and Pydot are installed first
Command: sudo apt-getinstall graphviz graphviz-doc
sudo pip install Pydot
Reference Posts:
1.http://www.cnblogs.com/sunshineatnoon/p/4535329.html
2.http://blog.csdn.net/wingfox117/article/details/46278001
3. Sharing of Yuanyuan
[Caffe] installation Caffe instruction book (Caffe (no cuda) and Python interface installed under Linux)