在anaconda中创建tensorflow，用spyder编辑

前提是已经安装好Anaconda，本文基于1.9.2

• 创建一个新的environment 可以选择python的版本创建，也是anaconda的优势，可以根据python的不同版本创建环境

conda create -n tf-env python=3.6

• 查看已经创建的环境

conda env list

• 删除环境

conda env remove -n tfenv

• 激活环境

activate tf-env

• 导出当前环境的配置到yml，实际上就是复制自己的环境，这样即使更换电脑后也可以与之前的自己环境一致

conda env export > environment.yml
使用此环境配置：
conda env create -f environment.yaml

以上就创建并激活tf-env这个环境，python version=3.6

---

安装tensorflow

Anaconda 在安装python package时比较方便，这也是建议初学者使用它的原因，这样就避免了中间过程要安装很多依赖包的麻烦

• 这里我选择安装的是tensorflow-gpu版本，看到右侧的安装依赖包顺便帮你安装好了CUDA9.0和cuDNN7.1，这省了你很大很大麻烦，当然前提你的电脑显卡是支持的

• 到这里就已经安装好了tensorflow了，是不是很简单，用anaconda很方便。现在我们去操作下
• 在anaconda可以使用Spyder编写python，如果你喜欢使用VS，可能需要额外配置，这里就以Spyder为例
• 既然安装时tensorflow-gup版本，按道理说肯定比cpu运算快点，现在测试下

• 查看电脑GPU和CPU

import os
from tensorflow.python.client import device_lib
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "99"
 
if __name__ == "__main__":
    print(device_lib.list_local_devices())
这是我电脑的cup和gpu的情况
runfile('C:/Users/Administrator/.spyder-py3/temp.py', wdir='C:/Users/Administrator/.spyder-py3')
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 15435477216407839396
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 3082768384
locality {
  bus_id: 1
  links {
  }
}
incarnation: 7754051037352108455
physical_device_desc: "device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1"
]

• 在Spyder运行.py文件时需要输入参数，运算结果看出，gpu要比cpu快8倍左右，默认是先选择gpu运算

import sys
import numpy as np
import tensorflow as tf
from datetime import datetime
 
device_name = sys.argv[1]  # Choose device from cmd line. Options: gpu or cpu
shape = (int(sys.argv[2]), int(sys.argv[2]))
if device_name == "gpu":
    device_name = "/gpu:0"
else:
    device_name = "/cpu:0"
 
with tf.device(device_name):
    random_matrix = tf.random_uniform(shape=shape, minval=0, maxval=1)
    dot_operation = tf.matmul(random_matrix, tf.transpose(random_matrix))
    sum_operation = tf.reduce_sum(dot_operation)
 
 
startTime = datetime.now()
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
        result = session.run(sum_operation)
        print(result)
 
# It can be hard to see the results on the terminal with lots of output -- add some newlines to improve readability.
print("\n" * 5)
print("Shape:", shape, "Device:", device_name)
print("Time taken:", datetime.now() - startTime)
 
print("\n" * 5)
 
--------------------------------------------

runfile('C:/Users/Administrator/.spyder-py3/temp.py', args='cpu 1500',wdir='C:/Users/Administrator/.spyder-py3')
844857860.0
Shape: (1500, 1500) Device: /cpu:0
Time taken: 0:00:00.093600

runfile('C:/Users/Administrator/.spyder-py3/temp.py', args='gpu 1500',wdir='C:/Users/Administrator/.spyder-py3')
844695940.0
Shape: (1500, 1500) Device: /gpu:0
Time taken: 0:00:00.015600