科研实验室-Caffe教程
66页1、PyTorchTorch TensorFlowCaffe TheanoMXNet Deep Learning Platforms for Deep Learning Outline Introduction Experiment Pipeline in Caffe Deeper into Caffe Python Interface Define Custom Layers Introduction What is Caffe Convolution Architecture For Feature Extraction CAFFE Open framework models and worked examples for deep learning 600 citations 200 contributions 18K stars 11K forks focus on vision but branching out sequences reinforcement learning speech text What is Caffe Pure C CUDA architecture
2、for deep learning command line Python Matlab interfaces Fast well tested code Tools reference models demos and recipes Seamless switch between CPU and GPU Caffe set model Caffe GPU Official Websites Home page http caffe berkeleyvision org GitHub Installation http caffe berkeleyvision org installation html Step by step Instructions Docker setup out of the box brewing Ubuntu installation the standard platform Debian installation install caffe with a single command OS X installation RHEL CentOS Fed
3、ora installation Windows see the Windows branch led by Guillaume Dumont OpenCL see the OpenCL branch led by Fabian Tschopp AWS AMI pre configured for AWS Overview Prerequisites Compilation Hardware Windows Linux http caffe berkeleyvision org install apt html General dependencies sudo apt get install libprotobuf dev libleveldb dev libsnappy dev libopencv dev libhdf5 serial dev protobuf compiler sudo apt get install no install recommends libboost all dev CUDA BLAS Python Compilation with Make Expe
4、riment Pipeline in Caffe Architecture of Caffe Main classes blob store data and derivatives layers transforms bottom blobs to top blobs net consists of many layers computes gradients via forward backward solver uses gradients to update weights Architecture of Caffe Main files train test prototxt deploy prototxt descript structure of the network solver prototxt descript training parameters create train test prototxt create solver prototxt TrainingTesting Define Network Step 1 create a train test
5、prototxt layer name data type Data top data top label include phase TRAIN transform param mirror true crop size 28 mean file home data cifar10 cifar10 mean binaryproto data param source home data cifar10 cifar10 train lmdb batch size 128 backend LMDB layer name conv 1 type Convolution bottom data top conv 1 param lr mult 1 0 decay mult 1 0 convolution param num output 16 pad 1 kernel size 3 stride 1 weight filler type gaussian std 0 117851130198 bias filler type constant value 0 0 layer name los
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