Tensorflow环境搭建(Mac系统）

1. Python的下载及安装：

https://www.python.org/about/

详细步骤和配置问题可以参考这篇文章： https://blog.csdn.net/alice_tl/article/details/76793590

2. 进入 terminal 终端

3. 进行pip安装和验证

pip3为python3的package 安装工具，命令如下：

$ sudo easy_install pip
此步骤为安装
$ sudo python3.5 get-pip.py
此步骤为验证pip3的存在及版本
$ pip3 –V

4. 安装tensorflow包及其他依赖的相关包

官方推荐用virtualenv方式安装tensorflow：https://www.tensorflow.org/install/install_mac#installing_with_virtualenv

a) 安装virtualenv
$ sudo pip install --upgrade virtualenv

b) 创建python3独立虚拟环境
$ virtualenv -p python ~/tensorflow

$ cd tensorflow

$ source bin/activate

发现命令行提示符变成如下：
(tensorflow)$

c) 在虚拟环境下，安装tensorflow及所需的package
$ pip3 install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.2.0-py3-none-any.whl

5. 验证环境是否搭建成功

(tensorflow)$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))

输出结果:

Hello, TensorFlow!

另一段：

C02Q9VN6FVH5:~ aa$ cd tensorflow
C02Q9VN6FVH5:tensorflow aa$ source bin activate
-bash: source: bin: is a directory
C02Q9VN6FVH5:tensorflow aa$ source bin/activate
(tensorflow) C02Q9VN6FVH5:tensorflow aa$ python
Python 3.6.3 (v3.6.3:2c5fed86e0, Oct  3 2017, 00:32:08) 
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2018-04-16 18:50:12.183299: 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.
2018-04-16 18:50:12.183342: 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.
2018-04-16 18:50:12.183352: 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.
2018-04-16 18:50:12.183361: 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.
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
>>> import tensorflow as tf
>>> hello=tf.constant("hello world")
>>> a=tf.constant(60)
>>> b=tf.constant(30)
>>> sess=tf.Session()
>>> print(sess.run(a))