26 lspci 27 aptitude install binutils ia32-libs gcc make automake autoconf libtool g++ g++-4.6 gawk gfortran freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev -y 28 apt install aptitude 29 wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb 30 sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb 31 sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub 32 sudo apt-get update 33 sudo apt-get install cuda 34 uanme -a 35 lsb_release -a 36 pip3 install --upgrade tensorflow-gpu 37 cd /usr/local/cuda-8.0/lib64/ 38 cd /usr/local/ 39 ll 40 cd cuda 41 ll 42 cd lib64 43 ll 44 cd 45 wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb 46 sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb 47 sudo apt-get update 48 sudo apt-get install cuda 49 apt-get remove cuda 50 sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb 51 sudo apt-get update 52 sudo apt-get install cuda 53 sudo apt-get search cuda 54 apt-get list cuda 55 sudo apt-get -h 56 wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run 57 sh cuda_8.0.61_375.26_linux.run 58 ll 59 sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb 60 sudo sh cuda_8.0.61_375.26_linux.run 61 sudo sh cuda_8.0.61_375.26_linux-run 62 sh cuda_8.0.61_375.26_linux-run 63 tail /tmp/cuda_install_6135.log 64 ll /tmp/ 65 ps aux|server 66 ps aux|grep server 67 rm /tmp/.X0-lock 68 tail /tmp/cuda_install_6135.log 69 sh cuda_8.0.61_375.26_linux-run 70 ll 71 rm *.deb 72 ll wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb apt-get install cuda-8-0 cd /usr/local/cuda-8.0/lib64/ ll libcudnn* cd wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1304/x86_64/cuda-repo-ubuntu1304_6.0-37_amd64.deb CUDNN_TAR_FILE="cudnn-8.0-linux-x64-v6.0.tgz" wget http://developer.download.nvidia.com/compute/redist/cudnn/v6.0/${CUDNN_TAR_FILE} tar -xzvf ${CUDNN_TAR_FILE} cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/ chmod a+r /usr/local/cuda-8.0/lib64/libcudnn* cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/ chmod a+r /usr/local/cuda-8.0/lib64/libcudnn* cd /usr/local/cuda-8.0/lib64/ cp libcudnn* /usr/local/lib/ ldconfig 105 cd /usr/local/lib/ 106 ll 107 history 27 aptitude install binutils ia32-libs gcc make automake autoconf libtool g++ g++-4.6 gawk gfortran freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev -y 28 apt install aptitude 29 wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb 30 sudo dpkg -i cuda-repo-ubuntu1604_9.0.176-1_amd64.deb 31 sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub 32 sudo apt-get update 33 sudo apt-get install cuda 34 uanme -a 35 lsb_release -a 36 ll 37 git clone https://github.com/exelban/tensorflow-cifar-10.git 38 apt install git 39 apt install tensorflow 40 pip 41 pip install tensorflow 42 git clone https://github.com/exelban/tensorflow-cifar-10.git 43 ll 44 cd tensorflow-cifar-10/ 45 ll 46 python train.py 47 pip install sklearn 48 python train.py 49 pip install scipy 50 python train.py 51 python3 52 python3 train.py 53 pip3 install numpy 54 apt install python3-pip 55 pip3 install numpy 56 pip3 install --upgrade pip3 57 pip3 install --upgrade pip 61 pip3 install tensorflow 63 pip3 install sklearn 65 pip3 install scipy 66 python3 train.py
5 chmod 777 init_machine.sh 6 ./init_machine.sh 7 cat ./init_machine.sh 8 git clone https://github.com/exelban/tensorflow-cifar-10.git 9 apt install git 10 git clone https://github.com/exelban/tensorflow-cifar-10.git 11 ll 12 cd tensorflow-cifar-10/ 13 ll 14 python3 train.py 15 pip3 install numpy 16 apt install python3-pip 17 python3 train.py 18 pip3 install numpy 19 pip3 install --upgrade pip3 pip3 install --upgrade pip pip3 install tensorflow pip3 install sklearn pip3 install scipy pip3 install tensorflow-gpu wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb apt-get install cuda-8-0 CUDNN_TAR_FILE="cudnn-8.0-linux-x64-v6.0.tgz" wget http://developer.download.nvidia.com/compute/redist/cudnn/v6.0/${CUDNN_TAR_FILE} tar -xzvf ${CUDNN_TAR_FILE} cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/ chmod a+r /usr/local/cuda-8.0/lib64/libcudnn* cp -P cuda/include/cudnn.h /usr/local/cuda-8.0/include cp -P cuda/lib64/libcudnn* /usr/local/cuda-8.0/lib64/ chmod a+r /usr/local/cuda-8.0/lib64/libcudnn* cd /usr/local/cuda-8.0/lib64/ cp libcudnn* /usr/local/lib/ ldconfig
import os import tensorflow as tf from PIL import Image #注意Image,后面会用到 import matplotlib.pyplot as plt import numpy as np cwd='/tmp/ossfs/ILSVRC2012_img_train/' classes={'n01440764','n01443537'} #人为 设定 2 类 writer= tf.python_io.TFRecordWriter("happyli.tfrecords") #要生成的文件 for index,name in enumerate(classes): class_path=cwd+name+'/' for img_name in os.listdir(class_path): img_path=class_path+img_name #每一个图片的地址 img=Image.open(img_path) img= img.resize((128,128)) img_raw=img.tobytes()#将图片转化为二进制格式 example = tf.train.Example(features=tf.train.Features(feature={ "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])), 'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])) })) #example对象对label和image数据进行封装 writer.write(example.SerializeToString()) #序列化为字符串 writer.close()
import os from PIL import Image def IsValidImage(pathfile): bValid = True try: Image.open(pathfile).verify() except: bValid = False return bValid if __name__=="__main__": directory="/mnt/ossfs/ILSVRC2012_img_train" classes = [] for subdir in sorted(os.listdir(directory)): if os.path.isdir(os.path.join(directory, subdir)): path = directory + '/'+ subdir print(path) for root, dirs, files in os.walk(path): if len(dirs) == 0: for i in range(len(files)): file_path = root+'/'+files[i] if IsValidImage(file_path) == False: print (file_path)