Caffe tutorial. Seong Joon Oh
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1 Caffe tutorial Seong Joon Oh
2 What is Caffe? Convolution Architecture For Feature Extraction (CAFFE) Open framework, models, and examples for deep learning 600+ citations, 100+ contributors, 7,000+ stars, 4,000+ forks Focus on vision, but branching out Pure C++ / CUDA architecture for deep learning Command line, Python, MATLAB interfaces Fast, well-tested code Tools, reference models, demos, and recipes Seamless switch between CPU and GPU Slide credit: Evan Shelhamer, Jeff Donahue, Jon Long, Yangqing Jia, and Ross Girshick
3 Reference Models Caffe offers the model definitions optimization settings pre-trained weights so you can start right away. The BVLC models are licensed for unrestricted use. GoogLeNet: ILSVRC14 winner The community shares models in the Model Zoo. Slide credit: Evan Shelhamer, Jeff Donahue, Jon Long, Yangqing Jia, and Ross Girshick
4 Main Classes Blob: Stores data and derivatives Layer: Transforms bottom blobs to top blobs Net: Many layers; computes gradients via Forward / Backward Solver: Uses gradients to update weights Slide credit: Stanford Vision CS231
5 Blobs Data Number x K Channel x Height x Width 256 x 3 x 227 x 227 for ImageNet train input Parameter: Convolution Weight N Output x K Input x Height x Width 96 x 3 x 11 x 11 for CaffeNet conv1 Parameter: Convolution Bias 96 x 1 x 1 x 1 for CaffeNet conv1 N-D arrays for storing and communicating data Hold data, derivatives and parameters Lazily allocate memory Shuttle between CPU and GPU Slide credit: Evan Shelhamer, Jeff Donahue, Jon Long, Yangqing Jia, and Ross Girshick
6 Layers Caffe s fundamental unit of computation Implemented as layers: Data access Convolution Pooling Activation Functions Loss Functions Dropout etc.
7 Forward: inference Backward: learning Net A DAG of layers and the blobs that connect them Caffe creates and checks the net from a definition file (more later) Exposes Forward / Backward methods LeNet
8 Solver Calls Forward / Backward and updates net parameters Periodically evaluates model on the test network(s) Snapshots model and solver state Solvers available: SGD AdaDelta AdaGrad Adam Nesterov RMSprop
9 Prototxt: Define Net Blobs Number of output classes Layer type
10 Prototxt: Layer Detail Learning rates (weight + bias) Set these to 0 to freeze a layer Convolution-specific parameters Parameter Initialization Example from./models/bvlc_reference_caffenet/train_val.prototxt
11 Prototxt: Define Solver Test on validation set Learning rate profile Net prototxt Snapshots during training
12 Setting Up Data Prefetching Multiple Inputs Data augmentation on-the-fly (random crops, flips) see TransformationPar ameter proto Choice of Data Layers: Image files LMDB HDF5
13 Interfaces Blob data and diffs exposed as Numpy arrays./python/caffe/_caffe.cpp: Exports Blob, Layer, Net & Solver classes./python/caffe/pycaffe.py: Adds extra methods to Net class Similar to PyCaffe in usage Demo:./matlab/demo/classification_demo.m Images are in BGR channels Jupyter notebooks:./examples
14 Compiled Caffe available Caffe root: /BS/hlcv-ss17/work/caffe/ Caffe binary: /BS/hlcv-ss17/work/caffe/build/tools/caffe Running without argument shows available options. Example usage for training a network according to solver.prototxt from a pretrained model pretrained.caffemodel using GPU 2: /BS/hlcv-ss17/work/caffe/build/tools/caffe train gpu 2 solver /path/to/solver.prototxt weights /path/to/pretrained.caffemodel
15 Recommended: Python interface (pycaffe) Importing pycaffe: pycaffe for forward passing through a trained network: classification.ipynb pycaffe for training pycaffe for fine- tuning
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