Intro to Deep Learning. Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn
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1 Intro to Deep Learning Slides Credit: Andrej Karapathy, Derek Hoiem, Marc Aurelio, Yann LeCunn
2 Why this class?
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7 Deep Features Have been able to harness the big data in the most efficient and effective manner. Lead to several state of the art results!
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9 2015 Performance: <5% Error
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14 What is deep learning?
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19 Space of Visual Features: Non Linear
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29 Deep Neural Networks
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56 Convolution Layer 32x32x3 image Filters always extend the full depth of the input volume 5x5x3 filter 32 Convolve the filter with the image i.e. slide over the image spatially, computing dot products
57 Convolution Layer 32 32x32x3 image 5x5x3 filter number: the result of taking a dot product between the filter and a small 5x5x3 chunk of the image (i.e. 5*5*3 = 75-dimensional dot product + bias) 57
58 Convolution Layer 32 32x32x3 image 5x5x3 filter activation map 28 convolve (slide) over all spatial locations
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62 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter 7 62
63 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter 7 63
64 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter 7 64
65 Closer look at Spatial 7 Dimensions 7x7 input (spatially) assume 3x3 filter 7 => 5x5 output 65
66 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter applied with stride
67 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter applied with stride
68 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter applied with stride 2 => 3x3 output! 7 68
69 Closer look at Spatial Dimensions 7 7x7 input (spatially) assume 3x3 filter applied with stride 3? 7 doesn t fit! cannot apply 3x3 filter on 7x7 input with stride 3. 69
70 F N F N Output size: (N - F) / stride + 1 e.g. N = 7, F = 3: stride 1 => (7-3)/1 + 1 = 5 stride 2 => (7-3)/2 + 1 = 3 stride 3 => (7-3)/3 + 1 = 2.33 :\ 70
71 In practice: Common to zero pad the border e.g. input 7x7 3x3 filter, applied with stride 1 pad with 1 pixel border => what is the output? 0 0 (recall:) (N - F) / stride
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73 Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Output volume size:? 73
74 Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Output volume size: (32+2*2-5)/1+1 = 32 spatially, so 32x32x10 74
75 Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Number of parameters in this layer? 75
76 Examples time: Input volume: 32x32x3 10 5x5 filters with stride 1, pad 2 Number of parameters in this layer? each filter has 5*5*3 + 1 = 76 params => 76*10 = 760 (+1 for bias) 76
77 Fully Connected Layer Connects to entire input volume similar to DNN 77
78 Fully Connected Layer Number of parameters: 10x10x10 connected to 1000 FC 1000 FC to 1000 FC. 78
79 Special Layers Pooling Contrast Normalization
80 Pooling layer - makes the representations smaller and more manageable - operates over each activation map independently: 80
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83 Local Response Normalization 83
84 Example Architectures 84
85 Case Study: LeNet-5 [LeCun et al., 1998] Conv filters were 5x5, applied at stride 1 Subsampling (Pooling) layers were 2x2 applied at stride 2 i.e. architecture is [CONV-POOL-CONV-POOL-CONV-FC] 85
86 Imagenet Network
87 Case Study: AlexNet [Krizhevsky et al. 2012] 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 87
88 Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 => Output volume [55x55x96] Q: What is the total number of parameters in this layer? 88
89 Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images First layer (CONV1): 96 11x11 filters applied at stride 4 => Output volume [55x55x96] Parameters: (11*11*3)*96 = 35K 89
90 Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images After CONV1: 55x55x96 Second layer (POOL1): 3x3 filters applied at stride 2 Q: what is the output volume size? Hint: (55-3)/2+1 = 27 90
91 Case Study: AlexNet [Krizhevsky et al. 2012] Input: 227x227x3 images After CONV1: 55x55x96 Second layer (POOL1): 3x3 filters applied at stride 2 Output volume: 27x27x96 Q: what is the number of parameters in this layer? 91
92 Case Study: AlexNet [Krizhevsky et al. 2012] 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) 92
93 Case Study: AlexNet [Krizhevsky et al. 2012] 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 batch size SGD Momentum Learning rate 1e-2, reduced by 10 manually when val accuracy plateaus - L2 weight decay 5e-4-7 CNN ensemble: 18.2% -> 15.4% 93
![Case Study: VGGNet [Simonyan and Zisserman, 2014] Only 3x3 CONV stride 1, pad 1 and](/docs-images/84/89826852/images/94-0.jpg "2x2 MAX POOL stride 2 best model 11.2% top 5 error in ILSVRC 2013 -> 7.")
94 Case Study: VGGNet [Simonyan and Zisserman, 2014] Only 3x3 CONV stride 1, pad 1 and 2x2 MAX POOL stride 2 best model 11.2% top 5 error in ILSVRC > 7.3% top 5 error 94
95 INPUT: [224x224x3] memory: 224*224*3=150K params: 0 (not counting biases) CONV3-64: [224x224x64] memory: 224*224*64=3.2M params: (3*3*3)*64 = 1,728 CONV3-64: [224x224x64] memory: 224*224*64=3.2M params: (3*3*64)*64 = 36,864 POOL2: [112x112x64] memory: 112*112*64=800K params: 0 CONV3-128: [112x112x128] memory: 112*112*128=1.6M params: (3*3*64)*128 = 73,728 CONV3-128: [112x112x128] memory: 112*112*128=1.6M params: (3*3*128)*128 = 147,456 POOL2: [56x56x128] memory: 56*56*128=400K params: 0 CONV3-256: [56x56x256] memory: 56*56*256=800K params: (3*3*128)*256 = 294,912 CONV3-256: [56x56x256] memory: 56*56*256=800K params: (3*3*256)*256 = 589,824 CONV3-256: [56x56x256] memory: 56*56*256=800K params: (3*3*256)*256 = 589,824 POOL2: [28x28x256] memory: 28*28*256=200K params: 0 CONV3-512: [28x28x512] memory: 28*28*512=400K params: (3*3*256)*512 = 1,179,648 CONV3-512: [28x28x512] memory: 28*28*512=400K params: (3*3*512)*512 = 2,359,296 CONV3-512: [28x28x512] memory: 28*28*512=400K params: (3*3*512)*512 = 2,359,296 POOL2: [14x14x512] memory: 14*14*512=100K params: 0 CONV3-512: [14x14x512] memory: 14*14*512=100K params: (3*3*512)*512 = 2,359,296 CONV3-512: [14x14x512] memory: 14*14*512=100K params: (3*3*512)*512 = 2,359,296 CONV3-512: [14x14x512] memory: 14*14*512=100K params: (3*3*512)*512 = 2,359,296 POOL2: [7x7x512] memory: 7*7*512=25K params: 0 FC: [1x1x4096] memory: 4096 params: 7*7*512*4096 = 102,760,448 FC: [1x1x4096] memory: 4096 params: 4096*4096 = 16,777,216 FC: [1x1x1000] memory: 1000 params: 4096*1000 = 4,096,000 95
96 INPUT: [224x224x3] memory: 224*224*3=150K params: 0 (not counting biases) CONV3-64: [224x224x64] memory: 224*224*64=3.2M params: (3*3*3)*64 = 1,728 CONV3-64: [224x224x64] memory: 224*224*64=3.2M params: (3*3*64)*64 = 36,864 POOL2: [112x112x64] memory: 112*112*64=800K params: 0 CONV3-128: [112x112x128] memory: 112*112*128=1.6M params: (3*3*64)*128 = 73,728 CONV3-128: [112x112x128] memory: 112*112*128=1.6M params: (3*3*128)*128 = 147,456 POOL2: [56x56x128] memory: 56*56*128=400K params: 0 CONV3-256: [56x56x256] memory: 56*56*256=800K params: (3*3*128)*256 = 294,912 CONV3-256: [56x56x256] memory: 56*56*256=800K params: (3*3*256)*256 = 589,824 CONV3-256: [56x56x256] memory: 56*56*256=800K params: (3*3*256)*256 = 589,824 POOL2: [28x28x256] memory: 28*28*256=200K params: 0 CONV3-512: [28x28x512] memory: 28*28*512=400K params: (3*3*256)*512 = 1,179,648 CONV3-512: [28x28x512] memory: 28*28*512=400K params: (3*3*512)*512 = 2,359,296 CONV3-512: [28x28x512] memory: 28*28*512=400K params: (3*3*512)*512 = 2,359,296 POOL2: [14x14x512] memory: 14*14*512=100K params: 0 CONV3-512: [14x14x512] memory: 14*14*512=100K params: (3*3*512)*512 = 2,359,296 CONV3-512: [14x14x512] memory: 14*14*512=100K params: (3*3*512)*512 = 2,359,296 CONV3-512: [14x14x512] memory: 14*14*512=100K params: (3*3*512)*512 = 2,359,296 POOL2: [7x7x512] memory: 7*7*512=25K params: 0 FC: [1x1x4096] memory: 4096 params: 7*7*512*4096 = 102,760,448 FC: [1x1x4096] memory: 4096 params: 4096*4096 = 16,777,216 FC: [1x1x1000] memory: 1000 params: 4096*1000 = 4,096,000 TOTAL memory: 24M * 4 bytes ~= 93MB / image (only forward! ~*2 for bwd) TOTAL params: 138M parameters 96
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