Cnn

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1. Convolutional Neural Network (CNN) 1 In the name of God Mehrnaz Faraz Faculty of Electrical Engineering K. N. Toosi University of Technology Milad Abbasi Faculty of…
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  • 1. Convolutional Neural Network (CNN) 1 In the name of God Mehrnaz Faraz Faculty of Electrical Engineering K. N. Toosi University of Technology Milad Abbasi Faculty of Electrical Engineering Sharif University of Technology
  • 2. CNN • A supervised deep learning algorithm • Not fully connected neural network • Suitable for big data and tensors – Tensor: Multidimensional array • Uses relatively little pre-processing compared to other algorithms 2
  • 3. Using CNN • Computer vision – Face recognition – Scene labelling – Image classification – Action recognition – Human pose estimation – Document analysis • Natural Language Processing – Speech recognition 3
  • 4. Using CNN 4 Face recognition Scene labelling Human pose estimation Document analysis
  • 5. CNN Using • Classification • Object detection • Segmentation 5
  • 6. CNN • Using convolutional layers • Using pooling layers • Using multiple filters in a layer – Creates different outputs in a layer • Suitable for image data 6
  • 7. Convolutional Layer • An example input volume in red (e.g. a 32x32x3 image) – Color image: Height, Width, Depth (Channels) – Each pixel has 3 channels (R,G and B) Input image: 32x32x3 Filter: 5x5x3 7 32 32 3 Height width depth 5 5 3
  • 8. Convolutional Layer • Convolving input with a filter – Convolution: Sum of element-wise multiplications – Example: 8
  • 9. Convolutional Layer 9
  • 10. Convolutional Layer 10 Input (x) Filter (w) Feature Map Stacked feature map with 10 different filters A neuron (number) T w x b
  • 11. Convolutional Layer • Stacked feature map: 11 Input Filter Filter Feature Map
  • 12. Convolutional Layer • Convolutional layer is NOT fully connected – Each neuron is connected only to a local region in the input volume spatially 12
  • 13. Convolutional Layer • Increasing number of neurons Increasing parameters and computational bourdon • Parameter sharing – Sharing of weights by all neurons in a particular feature map – Reduces the number of parameters • Local connectivity – Each neural connected only to a subset of the input image 13
  • 14. Number of Parameters 14 Input: 256x256x3 Parameters: 256*256*3+1=196,609 Parameters: 128*128*3+1=49,153 Kernel: 128x128x3 Parameter sharing
  • 15. Stride • Specifies how much we move the convolution filter at each step 15
  • 16. Stride 16
  • 17. Padding • The size of the feature map is smaller than the input • To maintain the same dimensionality – Using padding to surround the input with zero 17
  • 18. Example 18 P=0, S=1 P=2, S=1 P=1, S=2 P=1, S=2
  • 19. Example • Size of feature map: – i: size of input – K: size of kernel – p: padding – s: stride – o: size of feature map 19 2 1 i p k o s       
  • 20. Non-linearity • Adds ReLU after each convolutional layer • To introduce nonlinearity to a system that basically has just been computing linear operations during the conv layers • ReLU dose not saturate 20 Input Image Feature Maps Convolutional Layer/ Stacked feature map
  • 21. Non-linearity 21 • Convolution + ReLU
  • 22. Pooling Layer • Or subsampling layer • Periodically in-between Conv layers in a ConvNet • Reduce the amount of parameters, size of data, and computation in the network • Control overfitting • Types of pooling: – Stride – Mean pooling – Max pooling – Sum pooling 22
  • 23. Pooling Layer • Mean pooling • Max pooling 23 With stride 2
  • 24. CNN Overview • CNNs have two components: – The Hidden layers/Feature extraction part • Perform a series of convolutions and pooling operations • The convolution is performed on the input data with the use of a filter or kernel to then produce a feature map – The Classification part • Assign a probability for the object on the image being what the algorithm predicts it is 24
  • 25. CNN Overview 25
  • 26. CNN Example 26
  • 27. Training • Feed forward: 27
  • 28. Training • Back propagation: 28
  • 29. Common Architectures in CNN • Classic network architectures: – LeNet-5 – AlexNet – VGG16 • Modern network architectures: – Inception (GoogLeNet) – ResNet – ResNeXt – DenseNet 29
  • 30. LeNet-5 – 7 layers – 3 convolutional layers (C1, C3 and C5) – 2 sub-sampling (pooling) layers (S2 and S4)/ mean pooling – 1 fully connected layer (F6) – 60,000 parameters 30 LeCun et al. in 1998
  • 31. AlexNet – The general architecture is quite similar to LeNet-5 – This model is considerably larger than LeNet-5 – Opening for computer vision tasks with deep learning – 60 million parameters 31 Alex Krizhevsky et al. in 2012
  • 32. VGG16 – Offers a deeper yet simpler variant of the convolutional structures – 138 million parameters 32 Introduced in 2014
  • 33. GoogLeNet – Comprised of a basic unit referred to as an "Inception cell 33 In 2014, researchers at Google
  • 34. Inception 34
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