原文鏈接：http://blog.sciencenet.cn/blog-1583812-844177.html

?????? Caffe作為DL的一個學習框架，Caffe is a deep learning framework made with expression, speed, and modularity in mind.It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.Yangqing Jia created the project during his PhD at UC Berkeley.Caffe is released under theBSD 2-Clause license.

? ? ?? 為什么是深度學習？............

現有的DL框架：

Caffe的不同之處：使用純C++作為底層庫，開放Python、Matlab接口，基于CUDA

Caffe的網絡結構：一個塊作為數據結構

Caffe單層網絡定義（數據結構）：

Caffe的基本Blob結構：

如何訓練Caffe網絡：參數配置在solver.prototxt中

生成Caffe的數據結構：

Examples:

Logistic Regression (in Python):
http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/hdf5_classification.ipynb Learn LeNet on MNIST:
http://caffe.berkeleyvision.org/gathered/examples/mnist.html

參數調整 ： --將一個訓練好的模型?參數調整?到一個新任務中......

方法：使用ImageNet訓練的模型到任務，只需在定義里有一小點改變......

? #設定好模型路徑和想要分類的圖片

方法一：直接初始化....

Model_File = caffe_root + "examples/baby/deploy.prototxt" PreTrained = caffe_root + "examples/baby/caffenet_train_iter_6001.caffemodel" net = caffe.Classifier(Model_File,PreTrained);

方法二：New一下就可以了....

Net = new Caffe::Net("style_solver.prototxt") Net.CopyTrainedNetFrom(pretrained_model); solver.Solve(net);

調整參數?使 CaffeNet
Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data http://tutorial.caffe.berkeleyvision.org/gathered/examples/finetune_flickr_style.html

SoftMax?函數損失層：

SigMoid?交叉熵?損失函數：

歐式損失：

多重?損失：

Caffe Demo :http://demo.caffe.berkeleyvision.org/ Feature Visualization :http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/filter_visualization.ipynb

How to transform models in Caffe: http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/net_surgery.ipynb
Related projects: R-CNN: Regions with CNN Ross Girshick et al.Rich feature hierarchies for accurate object detection and semanticsegmentation. CVPR14. http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/detection.ipynb
Full scripts: https://github.com/rbgirshick/rcnn
Visual Style Recognition: Karayev et al.Recognizing Image Style. BMVC14. Caffe fine-tuning example Demo:http://demo.vislab.berkeleyvision.org/
Latest Roast: Model Zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo - BVLC reference models - VGG Devil + ILSVRC14 modelsin the zoo - Network-in-Network / CCCP modelin the zoo
Caffe + cuDNN
Parallel / distributed training across CPUs, GPUs, and cluster nodes https://github.com/BVLC/caffe/pull/1148

參考：DIY Deep Learning for Vision with Caffe slides

https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/preview?sle=true#slide=id.p

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