接上一条tensorflow的安装,注意版本不匹配会出现很多问题!:【吴恩达课程使用】anaconda (python 3.7) win10安装 tensorflow 1.8

源网址:https://docs.floydhub.com/guides/environments/

Below is the list of Deep Learning environments supported by FloydHub. Any of these can be specified in the floyd run command using the --env option.

If no --env is provided, it uses the tensorflow-1.9 image by default, which comes with Python 3.6, Keras 2.2.0 and TensorFlow 1.9.0 pre-installed.

Framework Env name (--env parameter) Description Docker Image Packages and Nvidia Settings
TensorFlow 1.14 tensorflow-1.14 TensorFlow 1.14.0 + Keras 2.2.5 on Python 3.6. floydhub/tensorflow TensorFlow-1.14
TensorFlow 1.13 tensorflow-1.13 TensorFlow 1.13.0 + Keras 2.2.4 on Python 3.6. floydhub/tensorflow TensorFlow-1.13
TensorFlow 1.12 tensorflow-1.12 TensorFlow 1.12.0 + Keras 2.2.4 on Python 3.6. floydhub/tensorflow TensorFlow-1.12
tensorflow-1.12:py2 TensorFlow 1.12.0 + Keras 2.2.4 on Python 2. floydhub/tensorflow
TensorFlow 1.11 tensorflow-1.11 TensorFlow 1.11.0 + Keras 2.2.4 on Python 3.6. floydhub/tensorflow TensorFlow-1.11
tensorflow-1.11:py2 TensorFlow 1.11.0 + Keras 2.2.4 on Python 2. floydhub/tensorflow
TensorFlow 1.10 tensorflow-1.10 TensorFlow 1.10.0 + Keras 2.2.0 on Python 3.6. floydhub/tensorflow TensorFlow-1.10
tensorflow-1.10:py2 TensorFlow 1.10.0 + Keras 2.2.0 on Python 2. floydhub/tensorflow
TensorFlow 1.9 tensorflow-1.9 TensorFlow 1.9.0 + Keras 2.2.0 on Python 3.6. floydhub/tensorflow TensorFlow-1.9
tensorflow-1.9:py2 TensorFlow 1.9.0 + Keras 2.2.0 on Python 2. floydhub/tensorflow
TensorFlow 1.8 tensorflow-1.8 TensorFlow 1.8.0 + Keras 2.1.6 on Python 3.6. floydhub/tensorflow TensorFlow-1.8
tensorflow-1.8:py2 TensorFlow 1.8.0 + Keras 2.1.6 on Python 2. floydhub/tensorflow
TensorFlow 1.7 tensorflow-1.7 TensorFlow 1.7.0 + Keras 2.1.6 on Python 3.6. floydhub/tensorflow TensorFlow-1.7
tensorflow-1.7:py2 TensorFlow 1.7.0 + Keras 2.1.6 on Python 2. floydhub/tensorflow
TensorFlow 1.5 tensorflow-1.5 TensorFlow 1.5.0 + Keras 2.1.6 on Python 3.6. floydhub/tensorflow TensorFlow-1.5
tensorflow-1.5:py2 TensorFlow 1.5.0 + Keras 2.1.6 on Python 2. floydhub/tensorflow
TensorFlow 1.4 tensorflow-1.4 TensorFlow 1.4.0 + Keras 2.0.8 on Python 3.6. floydhub/tensorflow
tensorflow-1.4:py2 TensorFlow 1.4.0 + Keras 2.0.8 on Python 2. floydhub/tensorflow
TensorFlow 1.3 tensorflow-1.3 TensorFlow 1.3.0 + Keras 2.0.6 on Python 3.6. floydhub/tensorflow
tensorflow-1.3:py2 TensorFlow 1.3.0 + Keras 2.0.6 on Python 2. floydhub/tensorflow
TensorFlow 1.2 tensorflow-1.2 TensorFlow 1.2.0 + Keras 2.0.6 on Python 3.5. floydhub/tensorflow
tensorflow-1.2:py2 TensorFlow 1.2.0 + Keras 2.0.6 on Python 2. floydhub/tensorflow
TensorFlow 1.1 tensorflow TensorFlow 1.1.0 + Keras 2.0.6 on Python 3.5. floydhub/tensorflow
tensorflow:py2 TensorFlow 1.1.0 + Keras 2.0.6 on Python 2. floydhub/tensorflow
TensorFlow 1.0 tensorflow-1.0 TensorFlow 1.0.0 + Keras 2.0.6 on Python 3.5. floydhub/tensorflow
tensorflow-1.0:py2 TensorFlow 1.0.0 + Keras 2.0.6 on Python 2. floydhub/tensorflow
TensorFlow 0.12 tensorflow-0.12 TensorFlow 0.12.1 + Keras 1.2.2 on Python 3.5. floydhub/tensorflow
tensorflow-0.12:py2 TensorFlow 0.12.1 + Keras 1.2.2 on Python 2. floydhub/tensorflow

二、激活之前tensorflow安装环境

\1. 查看Python环境

conda info --env可以看到所有python环境,前面有个‘*’的代表当前环境:

\2.激活环境

使用如下命令即可激活创建的虚拟环境

Linux: source activate your_env_name(虚拟环境名称)

Windows: activate your_env_name(虚拟环境名称)

例如我的:activate tensorflow10

三、安装keras 2.1.6

推荐使用pip:pip install keras==2.1.6 -i https://pypi.tuna.tsinghua.edu.cn/simple/ 【成功】

也可以使用anaconda:conda install -c anaconda keras==2.1.6 【失败】

四、安装慢-anaconda pip 换源

源地址:conda和pip常用方法,更换源,包的安装、更新、删除、查看

conda和pip可以说各有优劣。pip的模块更全更多,而conda使用更方便,安装模块时会检查环境,自动下载。conda 特别是在数据分析方面,会对某些常用的包做了专门的优化。
更换源
pip

临时更换 【http开头会出现 not a trusted or secure host 问题】

pip install <包名> -i https://pypi.douban.com/simple

上面使用的是豆瓣源,下面是其他国内源,替换上面的地址即可,都很快,随便用哪个。

阿里云 http://mirrors.aliyun.com/pypi/simple/

中国科技大学 https://pypi.mirrors.ustc.edu.cn/simple/

豆瓣(douban) http://pypi.douban.com/simple/

清华大学 https://pypi.tuna.tsinghua.edu.cn/simple/

中国科学技术大学 http://pypi.mirrors.ustc.edu.cn/simple/

永久更换

Windows
user目录中创建一个pip目录,如:C:\Users\xx\pip,新建文件pip.ini,内容如下:

[global]
index-url = http://pypi.douban.com/simple/
[install]
trusted-host=pypi.douban.com

Linux
修改 ~/.pip/pip.conf (没有就创建一个), 添加内容与上面代码一致。

mkdir ~/.pip/
vim ~/.pip/pip.conf

conda

Windows / Linux
Windows 和 Linux方法一致,并且是永久更换。

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes

包管理
安装

pip

pip install <包名>

conda

conda install <包名>

更新自己

pip

python -m pip install --upgrade pip

conda

conda update conda

查看过期的包

pip两种方法均可

pip list --outdated
pip list -o
#conda 查看所有包及其版本
conda list

单个更新包

pip 两种均可

pip install --upgrade <包名>
pip install -U <包名>

conda

conda update <包名>

批量更新包

pip
以下是python代码,需要打开Python后运行

import pip
from subprocess import call
from pip._internal.utils.misc import get_installed_distributions
for dist in get_installed_distributions():
call("pip install --upgrade " + dist.project_name, shell=True)

conda

conda update --all