分享一下我老师大神的人工智能教程！零基础，通俗易懂！http://blog.csdn.net/jiangjunshow
也欢迎大家转载本篇文章。分享知识，造福人民，实现我们中华民族伟大复兴！
pytorch可以利用tensorboard进行可视化
步骤
1. 安装docker
sudo apt-get install docker-engine
参考链接http://blog.csdn.net/amds123/article/details/68951886
2. 安装crayon
从docker安装crayon
docker pull alband/crayon
安装pycrayon
pip install pycrayon
启动crayon
docker run -d -p 8888:8888 -p 8889:8889 --name crayon alband/crayon
3. 参考代码
from pycrayon import CrayonClientimport time# Connect to the servercc = CrayonClient(hostname="server_machine_address")# Create a new experimentfoo = cc.create_experiment("foo")# Send some scalar values to the serverfoo.add_scalar_value("accuracy", 0, wall_time=11.3)foo.add_scalar_value("accuracy", 4, wall_time=12.3)# You can force the time and step valuesfoo.add_scalar_value("accuracy", 6, wall_time=13.3, step=4)# Get the datas sent to the serverfoo.get_scalar_values("accuracy")#>> [[11.3, 0, 0.0], [12.3, 1, 4.0], [13.3, 4, 6.0]])# backup this experiment as a zip filefilename = foo.to_zip()# delete this experiment from the servercc.remove_experiment("foo")# using the `foo` object from now on will result in an error# Create a new experiment based on foo's backupbar = cc.create_experiment("bar", zip_file=filename)# Get the name of all scalar plots in this experimentbar.get_scalar_names()#>> ["accuracy"]# Get the data for this experimentbar.get_scalar_values("accuracy")#>> [[11.3, 0, 0.0], [12.3, 1, 4.0], [13.3, 4, 6.0]])4. 访问tensorboard
游览器输入 http://localhost:8888
给我老师的人工智能教程打call！http://blog.csdn.net/jiangjunshow
