吴恩达deeplearning课程作业环境
Deep learning课程安装
Course 4 prerequisites:
- 2017/2/14 keras /pydot / graphviz
- 2017/2/20 pandas for week 3
- 2017/2/23 opencv for week 4
conda安装
unbuntu 16
pyhton 3.6版本 miniconda
https://conda.io/docs/user-guide/tasks/manage-environments.html
sudo apt-get update
bash Miniconda3-latest-Linux-x86_64.sh
# 安装位置 /home/ubuser/miniconda3重启Terminal
基础库
创建virtualenv环境deeplearn，并且安装相应的包。(好像漏掉了conda create -n tensorflow python=2.7 # or python=3.3, etc.)
conda config --prepend channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ #使用国内源，清华或者豆瓣
mkdir DeepLearning
cd DeepLearning/
conda install -n deeplearn numpy
conda install -n deeplearn jupyter
conda install -n deeplearn matplotlib
conda install -n deeplearn h5py
# 为了支持PIL，PIL官方只支持python 2.xconda install -n deeplearn pillow
#sklearn
conda install -n deeplearn scikit-learn
conda install -n deeplearn pandas
#cv2
conda install -n deeplearn opencv pip 国内源使用方法
例如豆瓣：http://pypi.douban.com/simple/
清华：https://pypi.tuna.tsinghua.edu.cn/simple
可以在使用pip的时候加参数-i https://pypi.tuna.tsinghua.edu.cn/simple
例如：pip install -i https://pypi.tuna.tsinghua.edu.cn/simple gevent，这样就会从清华这边的镜像去安装gevent库。
永久修改
linux下，修改 ~/.pip/pip.conf
windows下，直接在user目录中创建一个pip目录，如：C:\Users\xx\pip，新建文件pip.ini，
安装TensorFlow
安装说明 https://www.tensorflow.org/install/install_linux (墙外)
清华tuna源使用说明 https://mirrors.tuna.tsinghua.edu.cn/help/tensorflow/
Take the following steps to install TensorFlow in an Anaconda environment:
Follow the instructions on the Anaconda download site to download and install Anaconda.
Create a conda environment named
tensorflowto run a version of Python by invoking the following command:$ conda create -n tensorflow python=2.7 # or python=3.3, etc.Activate the conda environment by issuing the following command:
$ **source activate tensorflow (tensorflow)$ # Your prompt should changeIssue a command of the following format to install TensorFlow inside your conda environment:
(tensorflow)$ pip install --ignore-installed --upgrade tfBinaryURLwhere tfBinaryURL is the URL of the TensorFlow Python package. For example, the following command installs the CPU-only version of TensorFlow for Python 3.4:
(tensorflow)$ pip install --ignore-installed --upgrade\ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp34-cp34m-linux_x86_64.whl
从清华页面获得安装参数(在虚机内，简单期间使用CPU版。GPU的依赖见官方安装说明)
pip install \
-i https://pypi.tuna.tsinghua.edu.cn/simple/ \
https://mirrors.tuna.tsinghua.edu.cn/tensorflow/linux/cpu/tensorflow-1.4.1-cp36-cp36m-linux_x86_64.whl正式使用安装命令(在相应的venv下执行)
pip install --ignore-installed --upgrade\
-i https://pypi.tuna.tsinghua.edu.cn/simple/ \
https://mirrors.tuna.tsinghua.edu.cn/tensorflow/linux/cpu/tensorflow-1.4.1-cp36-cp36m-linux_x86_64.whl
TensorFlow其他资料
1） 如果出现警告 “RuntimeWarning: compiletime version 3.5 of module ‘tensorflow.python.framework.fast_tensor_util’ does not match runtime version 3.6”， 可以尝试从https://github.com/lakshayg/tensorflow-build安装。这个警告截至到2017/12/18没有官方解决方案。可以不管这个警告继续运行。回避的方式是把python改成3.5或者自己重编译。
2) 未经测试的安装方式
$ pip install tensorflow # Python 2.7; 仅支持CPU
$ pip3 install tensorflow # Python 3.n; 仅支持CPU
$ pip install tensorflow-gpu # Python 2.7; 支持CPU
$ pip3 install tensorflow-gpu # Python 3.n; 支持CPU3) Intel提供了针对Intel CPU优化的tensorflow, https://software.intel.com/en-us/articles/intel-optimized-tensorflow-wheel-now-available
# Python 3.6
pip install https://anaconda.org/intel/tensorflow/1.4.0/download/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl
# Python 2.7
pip install https://anaconda.org/intel/tensorflow/1.4.0/download/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
文章里还提供了修改venv配置和使用intel版numpy的方法。
使用使用Intel为主python环境的使用方法
https://software.intel.com/en-us/articles/using-intel-distribution-for-python-with-anaconda
单纯安装intel tensorflow在vmware虚机内，性能不会改善。
其他开源包
- keras / pydot
course 4 week 02 作业
pip install keras
pip install pydotpydot需要graphviz支持，没有的话，会出现这个错误 “FileNotFoundError: [Errno 2] No such file or directory: ‘dot’”
sudo apt-get install graphviz 安装测试
TensorFlow测试
# Python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))使用测试
source activate deeplearn
jupyter notebook进入notebook环境
import numpy as np
A = np.array([[1, 2], [3, 4]])
print(A)从主机访问虚拟机
如果ssh映射到本地，参考文档
bivisw里面配置C2S端口映射(port forwording)
Listen interface/port是主机的
Destination host/port是虚拟机的
两个IP可以都是127.0.0.1或者localhost
虚拟机的端口是8888(notebook默认端口)，主机端口任意。
另外一种方法，直接改变监听的IP
jupyter notebook --help
jupyter notebook --ip=0.0.0.0 # listen all network interface
Viewing a list of the packages in an environment
To see a list of all packages installed in a specific environment:
If the environment is not activated:
conda list -n myenv
If the environment is activated:
conda list
导出Export your active environment to a new file:
conda env export > environment.yml
name: deeplearn
channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
- defaults
dependencies:
- bleach=1.5.0=py36_0
- certifi=2016.2.28=py36_0
- cycler=0.10.0=py36_0
- dbus=1.10.20=0
- decorator=4.1.2=py36_0
- entrypoints=0.2.3=py36_0
- expat=2.1.0=0
- fontconfig=2.12.1=3
- freetype=2.5.5=2
- glib=2.50.2=1
- gst-plugins-base=1.8.0=0
- gstreamer=1.8.0=0
- h5py=2.7.0=np112py36_0
- hdf5=1.8.17=2
- html5lib=0.9999999=py36_0
- icu=54.1=0
- ipykernel=4.6.1=py36_0
- ipython=6.1.0=py36_0
- ipython_genutils=0.2.0=py36_0
- ipywidgets=6.0.0=py36_0
- jbig=2.1=0
- jedi=0.10.2=py36_2
- jinja2=2.9.6=py36_0
- jpeg=8d=2
- jsonschema=2.6.0=py36_0
- jupyter=1.0.0=py36_3
- jupyter_client=5.1.0=py36_0
- jupyter_console=5.2.0=py36_0
- jupyter_core=4.3.0=py36_0
- libffi=3.2.1=1
- libgcc=5.2.0=0
- libgfortran=3.0.0=1
- libiconv=1.14=0
- libpng=1.6.30=1
- libsodium=1.0.10=0
- libtiff=4.0.6=2
- libxcb=1.12=1
- libxml2=2.9.4=0
- markupsafe=1.0=py36_0
- matplotlib=2.0.2=np112py36_0
- mistune=0.7.4=py36_0
- mkl=2017.0.3=0
- nbconvert=5.2.1=py36_0
- nbformat=4.4.0=py36_0
- notebook=5.0.0=py36_0
- numpy=1.12.1=py36_0
- olefile=0.44=py36_0
- opencv=3.1.0=np112py36_1
- openssl=1.0.2l=0
- pandas=0.20.3=py36_0
- pandocfilters=1.4.2=py36_0
- path.py=10.3.1=py36_0
- pcre=8.39=1
- pexpect=4.2.1=py36_0
- pickleshare=0.7.4=py36_0
- pillow=3.4.2=py36_0
- pip=9.0.1=py36_1
- prompt_toolkit=1.0.15=py36_0
- ptyprocess=0.5.2=py36_0
- pygments=2.2.0=py36_0
- pyparsing=2.2.0=py36_0
- pyqt=5.6.0=py36_2
- python=3.6.2=0
- python-dateutil=2.6.1=py36_0
- pytz=2017.2=py36_0
- pyzmq=16.0.2=py36_0
- qt=5.6.2=2
- qtconsole=4.3.1=py36_0
- readline=6.2=2
- scikit-learn=0.19.0=np112py36_0
- scipy=0.19.1=np112py36_0
- setuptools=36.4.0=py36_1
- simplegeneric=0.8.1=py36_1
- sip=4.18=py36_0
- six=1.10.0=py36_0
- sqlite=3.13.0=0
- terminado=0.6=py36_0
- testpath=0.3.1=py36_0
- tk=8.5.18=0
- tornado=4.5.2=py36_0
- traitlets=4.3.2=py36_0
- wcwidth=0.1.7=py36_0
- wheel=0.29.0=py36_0
- widgetsnbextension=3.0.2=py36_0
- xz=5.2.3=0
- zeromq=4.1.5=0
- zlib=1.2.11=0
- pip:
- enum34==1.1.6
- ipython-genutils==0.2.0
- jupyter-client==5.1.0
- jupyter-console==5.2.0
- jupyter-core==4.3.0
- keras==2.1.3
- markdown==2.6.10
- prompt-toolkit==1.0.15
- protobuf==3.5.0.post1
- pydot==1.2.4
- pyyaml==3.12
- tensorflow==1.4.1
- tensorflow-tensorboard==0.4.0rc3
- werkzeug==0.13
prefix: /home/ubuser/miniconda3/envs/deeplearn