一、报错信息
mac下使用tensorflow提示如下warning
2018-03-10 19:23:23.999284: W 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.
2018-03-10 19:23:23.999318: W 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.
2018-03-10 19:23:23.999324: W 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.
2018-03-10 19:23:23.999329: W 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.
二、解决方案
（1）下载tensorflow源码
git clone --recurse-submodules https://github.com/tensorflow/tensorflow
（2）更新bazel
brew upgrade bazel
(3)配置tensorflow 命令如下：
cd tensorflow/ (进入第一步中创建的tensorflow文件夹)
./configure
此时会弹出一些配置需要你填写，首先弹出的是选择python的路径和版本，在提示信息中会给出defult信息，由于默认是python2，因此需要改成python3
You have bazel 0.10.1-homebrew installed.
Please specify the location of python. [Default is /usr/bin/python]: /Library/Frameworks/Python.framework/Versions/3.6/bin/python3
Found possible Python library paths:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages
Please input the desired Python library path to use. Default is [/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages]
完成之后会让你输入Y/N选择tensorflow是否支持google cloud，Hadoop等，建议Hadoop选Y其他选择N即可（如果都选Y又需要额外下载很多东西）
Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: y
Hadoop File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: n
No Apache Kafka Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.
Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.
Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]: --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx --copt=-mavx2 --copt=-mfma
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
--config=mkl # Build with MKL support.
--config=monolithic # Config for mostly static monolithic build.
Configuration finished
（4）生成pip安装包 命令：bazel build -c opt --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2 --copt=-mavx --copt=-mavx2 --copt=-mfma //tensorflow/tools/pip_package:build_pip_package （时间较长，请耐心等待）
（其中msse3,msse4.1 mavx等表示cpu支持的指令集，不同机器会有差异，如果你电脑的CPU不支持AVX指令，但在此处却输出了--copt=-mavx 最后还是会报错提示：你的机器不支持此指令，所以在写这条命令时候，建议大家下载CPU-Z看看本机CPU支持的指令，然后对应写此指令）
（5）安装 命令如下：
bazel-bin /tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
到/tmp/tensorflow_pkg拷贝出来whl文件，再用pip3安装
pip3 install tensorflow-1.6.0-cp36-cp36m-macosx_10_6_intel.whl （最后是生成的软件包名，会有不同，根据自己生成的包名填写）
三、效果
对于同样一段代码，再优化之前需要800s每epoch，而优化之后大概需要310s左右，提升了一半以上，效果还是比较明显。