官网教程地址: https://www.tensorflow.org/install/   .

1. centos下安装

按顺序一步一步解决依赖并安装。

1.1 python

笔者使用的版本为2.7.5 .

2.1 python-pip

pip,Python Index Package。类似linux下的yum，安装并管理python软件包。

pip安装命令：yum install  python-pip python-devel

备注：不安装python-devel的话，pynum安装就会报错。这是一个py下的数学计算扩展包。

3.1 tensorflow

终于开始安装它本身了。

tensorflow 安装命令：pip install  https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.6.0-cp27-none-linux_x86_64.whl

安装之后的升级命令：pip install –upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.6.0-cp27-none-linux_x86_64.whl

安装过程需要持续近10分钟，因为有大量的编译工作。

2. windows下安装

3.测试安装成功与否

#tensorflowTest.py

import tensorflow as tf

hello = tf.constant(‘Hello,TensorFlow!’)

sess = tf.Session()

print(sess.run(hello))

a = tf.constant(10)

b = tf.constant(32)

print(sess.run(a + b))

运行 python tensorflowTest.py

，若最终结果与下面两行一致，则恭喜你安装成功！附运行结果的截图，见图4-1.

//TensorFlow.py 运行结果

Hello, TensorFlow!

42

4. 常见问题

2017-05-05 11:18:58.162500: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.162995: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.163347: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.164001: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.164352: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.164699: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.165029: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.

2017-05-05 11:18:58.165292: W c:\tf_jenkins\home\workspace\release-win\device\cpu\os\windows\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn’t compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

来源URL：http://blog.csdn.net/chuchus/article/details/51713389