Learning codes and deep learning from Caffe

• 根据 http://caffe.berkeleyvision.org/tutorial/ 学习 caffe 对各种机器学习的算法的实现。

  • Layers
  • blobs
  • loss
  • solver

• 读论文，实现论文中新的结构

  • 添加新的 layer

• 学习框架的使用

  • 训练和测试 mnist 数据集 学习了一下怎么训练和测试模型，看了一下 lenet_train_test.prototxt 里面的参数
  • 学习 web_demo 的在线响应

Run jupyter notebook in docker

1. 配置 docker port mapping，名称和共享文件夹

docker run -it -p 7000:7000 --rm --name test -v $(pwd):/caffe cslzy/learning-caffe

2. 启动 jupyter notebook

jupyter-notebook --no-browser --ip 0.0.0.0 --port=7000 --allow-root

3. 在本机上打开 http://localhost:7000/tree
4. 将 docker 里面运行 jupyter notebook 产生的 token 填到网页上。

Docker image

docker image which is build by using codes from this repository.

docker pull cslzy/learning-caffe

Docker

• screen
• [ ]

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Check out the project site for all the details like

• DIY Deep Learning for Vision with Caffe
• Tutorial Documentation
• BAIR reference models and the community model zoo
• Installation instructions

and step-by-step examples.

Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.

Happy brewing!

License and Citation

Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}