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Lecture 9: CNN Architectures

Fei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 2017 Fei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 20171 Lecture 9:CNN ArchitecturesFei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 20172 AdministrativeA2 due Thu May 4 Midterm: In-class Tue May 9. Covers material through Thu May 4 session: Tue June 6, 12-3pmFei-Fei Li & Justin Johnson & Serena YeungLecture 9 -May 2, 2017 Last time: Deep learning frameworks3 Caffe (UC Berkeley)Torch (NYU / Facebook)Theano (U Montreal)TensorFlow (Google)Caffe2 (Facebook)PyTorch (Facebook)CNTK (Microsoft)Paddle (Baidu)MXNet (Amazon)Developed by U Washington, CMU, MIT, Hong Kong U, etc but main framework of choice at AWSAnd Li & Justin Johnson & Serena YeungLecture 9 -May 2, 20174(1)Easily build big computational graphs(2)Easily compute gradients in computational graphs(3)Run it all efficiently on GPU (wrap cuDNN, cuBLAS, etc)Last time.

[4096] FC6: 4096 neurons [4096] FC7: 4096 neurons [1000] FC8: 1000 neurons (class scores) Details/Retrospectives: - first use of ReLU - used Norm layers (not common anymore) - heavy data augmentation - dropout 0.5 - batch size 128 - SGD Momentum 0.9 - Learning rate 1e-2, reduced by 10 manually when val accuracy plateaus - L2 weight decay 5e-4

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