Transcription of Lecture 9: CNN Architectures
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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.
[Krizhevsky et al. 2012] Architecture: CONV1 MAX POOL1 NORM1 CONV2 MAX POOL2 NORM2 CONV3 CONV4 CONV5 Max POOL3 FC6 FC7 FC8. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 10 May 2, 2017 Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 =>
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