Lecture 9: CNN Architectures - Stanford · PDF fileFei-Fei Li & Justin Johnson &...

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - May 2, 2017Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - May 2, 20171
Lecture 9:CNN Architectures

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - May 2, 20172
Administrative
A2 due Thu May 4
Midterm: In-class Tue May 9. Covers material through Thu May 4 lecture.
Poster session: Tue June 6, 12-3pm

Last time: Deep learning frameworks
3
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 AWS
And others...

(1) Easily build big computational graphs(2) Easily compute gradients in computational graphs(3) Run it all efficiently on GPU (wrap cuDNN, cuBLAS, etc)

Modularized layers that define forward and backward pass

Define model architecture as a sequence of layers

Today: CNN Architectures
7
Case Studies- AlexNet- VGG- GoogLeNet- ResNet
Also....- NiN (Network in Network)- Wide ResNet- ResNeXT- Stochastic Depth
- DenseNet- FractalNet- SqueezeNet

Review: LeNet-5[LeCun et al., 1998]
Conv filters were 5x5, applied at stride 1Subsampling (Pooling) layers were 2x2 applied at stride 2i.e. architecture is [CONV-POOL-CONV-POOL-FC-FC]

Case Study: AlexNet[Krizhevsky et al. 2012]
Architecture:CONV1MAX POOL1NORM1CONV2MAX POOL2NORM2CONV3CONV4CONV5Max POOL3FC6FC7FC8

Input: 227x227x3 images
First layer (CONV1): 96 11x11 filters applied at stride 4=>Q: what is the output volume size? Hint: (227-11)/4+1 = 55

First layer (CONV1): 96 11x11 filters applied at stride 4=>Output volume [55x55x96]
Q: What is the total number of parameters in this layer?

First layer (CONV1): 96 11x11 filters applied at stride 4=>Output volume [55x55x96]Parameters: (11*11*3)*96 = 35K

Input: 227x227x3 imagesAfter CONV1: 55x55x96
Second layer (POOL1): 3x3 filters applied at stride 2
Q: what is the output volume size? Hint: (55-3)/2+1 = 27

Second layer (POOL1): 3x3 filters applied at stride 2Output volume: 27x27x96
Q: what is the number of parameters in this layer?

Second layer (POOL1): 3x3 filters applied at stride 2Output volume: 27x27x96Parameters: 0!

Input: 227x227x3 imagesAfter CONV1: 55x55x96After POOL1: 27x27x96...

Full (simplified) AlexNet architecture:[227x227x3] INPUT[55x55x96] CONV1: 96 11x11 filters at stride 4, pad 0[27x27x96] MAX POOL1: 3x3 filters at stride 2[27x27x96] NORM1: Normalization layer[27x27x256] CONV2: 256 5x5 filters at stride 1, pad 2[13x13x256] MAX POOL2: 3x3 filters at stride 2[13x13x256] NORM2: Normalization layer[13x13x384] CONV3: 384 3x3 filters at stride 1, pad 1[13x13x384] CONV4: 384 3x3 filters at stride 1, pad 1[13x13x256] CONV5: 256 3x3 filters at stride 1, pad 1[6x6x256] MAX POOL3: 3x3 filters at stride 2[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 10manually when val accuracy plateaus- L2 weight decay 5e-4- 7 CNN ensemble: 18.2% -> 15.4%

Historical note: Trained on GTX 580 GPU with only 3 GB of memory. Network spread across 2 GPUs, half the neurons (feature maps) on each GPU.
[55x55x48] x 2

CONV1, CONV2, CONV4, CONV5: Connections only with feature maps on same GPU

CONV3, FC6, FC7, FC8: Connections with all feature maps in preceding layer, communication across GPUs

Fei-Fei Li & Justin Johnson & Serena Y

Lecture 9: CNN Architectures - Stanford · PDF fileFei-Fei Li & Justin Johnson &...

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Transcript of Lecture 9: CNN Architectures - Stanford · PDF fileFei-Fei Li & Justin Johnson &...

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