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CNN Basics.pdf

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NiharikaThakur32

The document provides an overview of convolutional neural networks (CNNs) and their layers. It explains that CNNs take advantage of the 2D structure of input images through the use of convolutional layers, pooling layers, and fully-connected layers. Convolutional layers apply filters to local regions of the input volume to identify patterns instead of individual pixels. Pooling layers perform downsampling to reduce the spatial size of representations. The document provides examples of convolutional filters and how they are applied to an input through sliding and striding operations.

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Page 1 Convolutional neural network EECS6980:006 Social Network Analysis
Page  2 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  3 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  4 Introduction  A convolutional neural network (or ConvNet) is a type of feed-forward artificial neural network  The architecture of a ConvNet is designed to take advantage of the 2D structure of an input image.    A ConvNet is comprised of one or more convolutional layers (often with a pooling step) and then followed by one or more fully connected layers as in a standard multilayer neural network. EECS6980:006 Social Network Analysis VS
Page  5 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  6 Motivation behind ConvNets  Consider an image of size 200x200x3 (200 wide, 200 high, 3 color channels) – A single fully-connected neuron in a first hidden layer of a regular Neural Network would have 200*200*3 = 1,20,000 weights. – Due to the presence of several such neurons, this full connectivity is wasteful and the huge number of parameters would quickly lead to overfitting.  However, in a ConvNet, the neurons in a layer will only be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner. – the final output layer would have dimensions 1x1xN, because by the end of the ConvNet architecture we will reduce the full image into a single vector of class scores (for N classes), arranged along the depth dimension EECS6980:006 Social Network Analysis
Page  7 MLP VS ConvNet EECS6980:006 Social Network Analysis Input Hidden Output Input Hidden Output Multilayered Perceptron: All Fully Connected Layers Convolutional Neural Network With Partially Connected Convolution Layer
Page  8 MLP vs ConvNet A regular 3-layer Neural Network. A ConvNet arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. EECS6980:006 Social Network Analysis
Page  9 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  10 How ConvNet Works  For example, a ConvNet takes the input as an image which can be classified as ‘X’ or ‘O’  In a simple case, ‘X’ would look like: X or O CNN A two-dimensional array of pixels
Page  11 How ConvNet Works  What about trickier cases? CNN X CNN O
Page  12 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = ? How ConvNet Works – What Computer Sees
Page  13 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = x How ConvNet Works
Page  14 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 How ConvNet Works – What Computer Sees  Since the pattern doesnot match exactly, the computer will not be able to classify this as ‘X’
Page  15 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  16 ConvNet Layers (At a Glance)  CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume.  RELU layer will apply an elementwise activation function, such as the max(0,x) thresholding at zero. This leaves the size of the volume unchanged.  POOL layer will perform a downsampling operation along the spatial dimensions (width, height).  FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1xN], where each of the N numbers correspond to a class score, such as among the N categories. EECS6980:006 Social Network Analysis
Page  17 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  18 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Recall – What Computer Sees  Since the pattern doesnot match exactly, the computer will not be able to classify this as ‘X’  What got changed?
Page  19 = = =  Convolution layer will work to identify patterns (features) instead of individual pixels Convolutional Layer
Page  20 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Convolutional Layer - Filters  The CONV layer’s parameters consist of a set of learnable filters.  Every filter is small spatially (along width and height), but extends through the full depth of the input volume.  During the forward pass, we slide (more precisely, convolve) each filter across the width and height of the input volume and compute dot products between the entries of the filter and the input at any position.
Page  21 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Convolutional Layer - Filters  Sliding the filter over the width and height of the input gives 2-dimensional activation map that responds to that filter at every spatial position.
Page  22 Convolutional Layer - Strides Input Hidden Output Input Hidden Output 1-D convolution Filters: 1 Filter Size: 2 Stride: 2 1-D convolution Filters: 1 Filter Size: 2 Stride: 1 • The distance that filter is moved across the input from the previous layer each activation is referred to as the stride.
Page  23 Convolutional Layer - Padding  Sometimes it is convenient to pad the input volume with zeros around the border.  Zero padding is allows us to preserve the spatial size of the output volumes EECS6980:006 Social Network Analysis Input Hidden Output 1 D convolution with: Filters: 1 Filter Size: 2 Stride: 2 Padding: 1 0 0 Input Hidden Output 1 D convolution with: Filters: 2 Filter Size: 2 Stride: 2 Padding: 1 0 0
Page  24 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example  Strides = 1, Filter Size = 3 X 3 X 1, Padding = 0
Page  25 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  26 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  27 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  28 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  29 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  30 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  31 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  32 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
Page  33 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  34 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  35 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  36 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  37 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  38 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  39 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  40 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  41 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  42 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  43 1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  44 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  45 1 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  46 1 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
Page  47 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 1 1 1 -1 1 1 55 1 1 -1 1 1 1 -1 1 1 Convolutional Layer – Filters – Computation Example
Page  48 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 Convolutional Layer – Filters – Computation Example Input Size (W): 9 Filter Size (F): 3 X 3 Stride (S): 1 Filters: 1 Padding (P): 0 9 X 9 7 X 7 Feature Map Size = 1+ (W – F + 2P)/S = 1+ (9 – 3 + 2 X 0)/1 = 7
Page  49 1 -1 -1 -1 1 -1 -1 -1 1 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 = = -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Output Feature Map  Output Feature Map of One complete convolution: – Filters: 3 – Filter Size: 3 X 3 – Stride: 1  Conclusion: – Input Image: 9 X 9 – Output of Convolution: 7 X 7 X 3
Page  50 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Output
Page  51 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  52 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.77
Page  53 0.77 0 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
Page  54 0.77 0 0.11 0.33 0.55 0 0.33 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
Page  55 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
Page  56 ReLU layer 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 0.33 0 0.11 0 0.11 0 0.33 0 0.55 0 0.33 0 0.55 0 0.11 0 0.55 0 0.55 0 0.11 0 0.33 0 1.00 0 0.33 0 0.11 0 0.55 0 0.55 0 0.11 0 0.55 0 0.33 0 0.55 0 0.33 0 0.11 0 0.11 0 0.33 0.33 0 0.55 0.33 0.11 0 0.77 0 0.11 0 0.33 0 1.00 0 0.55 0 0.11 0 1.00 0 0.11 0.33 0.33 0 0.55 0 0.33 0.33 0.11 0 1.00 0 0.11 0 0.55 0 1.00 0 0.33 0 0.11 0 0.77 0 0.11 0.33 0.55 0 0.33 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
Page  57 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  58 Pooling Layer  The pooling layers down-sample the previous layers feature map.  Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network  The pooling layer often uses the Max operation to perform the downsampling process EECS6980:006 Social Network Analysis
Page  59 1.00 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
Page  60 1.00 0.33 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
Page  61 1.00 0.33 0.55 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
Page  62 1.00 0.33 0.55 0.33 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
Page  63 1.00 0.33 0.55 0.33 0.33 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
Page  64 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
Page  65 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33 Pooling
Page  66 Layers get stacked -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
Page  67 Layers Get Stacked - Example 224 X 224 X 3 224 X 224 X 64 CONVOLUTION WITH 64 FILTERS 112 X 112 X 64 POOLING (DOWNSAMPLING)
Page  68 Deep stacking -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55
Page  69 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  70 Fully connected layer  Fully connected layers are the normal flat feed-forward neural network layers.  These layers may have a non-linear activation function or a softmax activation in order to predict classes.  To compute our output, we simply re- arrange the output matrices as a 1-D array. 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55 1.00 0.55 0.55 1.00 1.00 0.55 0.55 0.55 0.55 1.00 1.00 0.55
Page  71 Fully connected layer  A summation of product of inputs and weights at each output node determines the final prediction X O 0.55 1.00 1.00 0.55 0.55 0.55 0.55 0.55 1.00 0.55 0.55 1.00
Page  72 Putting it all together -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X O
Page  73 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Parameters & Demos – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  74 Hyperparameters (knobs)  Convolution – Filter Size – Number of Filters – Padding – Stride  Pooling – Window Size – Stride  Fully Connected – Number of neurons
Page  75 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Parameters & Demos – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  76 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Parameters & Demos – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
Page  77 Case Studies  LeNet – 1998  AlexNet – 2012  ZFNet – 2013  VGG – 2014  GoogLeNet – 2014  ResNet – 2015 EECS6980:006 Social Network Analysis
Page  78 Thank you!! EECS6980:006 Social Network Analysis

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CNN Basics.pdf

  • 2. Page  2 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 3. Page  3 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 4. Page  4 Introduction  A convolutional neural network (or ConvNet) is a type of feed-forward artificial neural network  The architecture of a ConvNet is designed to take advantage of the 2D structure of an input image.    A ConvNet is comprised of one or more convolutional layers (often with a pooling step) and then followed by one or more fully connected layers as in a standard multilayer neural network. EECS6980:006 Social Network Analysis VS
  • 5. Page  5 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 6. Page  6 Motivation behind ConvNets  Consider an image of size 200x200x3 (200 wide, 200 high, 3 color channels) – A single fully-connected neuron in a first hidden layer of a regular Neural Network would have 200*200*3 = 1,20,000 weights. – Due to the presence of several such neurons, this full connectivity is wasteful and the huge number of parameters would quickly lead to overfitting.  However, in a ConvNet, the neurons in a layer will only be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner. – the final output layer would have dimensions 1x1xN, because by the end of the ConvNet architecture we will reduce the full image into a single vector of class scores (for N classes), arranged along the depth dimension EECS6980:006 Social Network Analysis
  • 7. Page  7 MLP VS ConvNet EECS6980:006 Social Network Analysis Input Hidden Output Input Hidden Output Multilayered Perceptron: All Fully Connected Layers Convolutional Neural Network With Partially Connected Convolution Layer
  • 8. Page  8 MLP vs ConvNet A regular 3-layer Neural Network. A ConvNet arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. EECS6980:006 Social Network Analysis
  • 9. Page  9 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 10. Page  10 How ConvNet Works  For example, a ConvNet takes the input as an image which can be classified as ‘X’ or ‘O’  In a simple case, ‘X’ would look like: X or O CNN A two-dimensional array of pixels
  • 11. Page  11 How ConvNet Works  What about trickier cases? CNN X CNN O
  • 12. Page  12 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = ? How ConvNet Works – What Computer Sees
  • 13. Page  13 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = x How ConvNet Works
  • 14. Page  14 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 How ConvNet Works – What Computer Sees  Since the pattern doesnot match exactly, the computer will not be able to classify this as ‘X’
  • 15. Page  15 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 16. Page  16 ConvNet Layers (At a Glance)  CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume.  RELU layer will apply an elementwise activation function, such as the max(0,x) thresholding at zero. This leaves the size of the volume unchanged.  POOL layer will perform a downsampling operation along the spatial dimensions (width, height).  FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1xN], where each of the N numbers correspond to a class score, such as among the N categories. EECS6980:006 Social Network Analysis
  • 17. Page  17 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 18. Page  18 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Recall – What Computer Sees  Since the pattern doesnot match exactly, the computer will not be able to classify this as ‘X’  What got changed?
  • 19. Page  19 = = =  Convolution layer will work to identify patterns (features) instead of individual pixels Convolutional Layer
  • 20. Page  20 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Convolutional Layer - Filters  The CONV layer’s parameters consist of a set of learnable filters.  Every filter is small spatially (along width and height), but extends through the full depth of the input volume.  During the forward pass, we slide (more precisely, convolve) each filter across the width and height of the input volume and compute dot products between the entries of the filter and the input at any position.
  • 21. Page  21 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Convolutional Layer - Filters  Sliding the filter over the width and height of the input gives 2-dimensional activation map that responds to that filter at every spatial position.
  • 22. Page  22 Convolutional Layer - Strides Input Hidden Output Input Hidden Output 1-D convolution Filters: 1 Filter Size: 2 Stride: 2 1-D convolution Filters: 1 Filter Size: 2 Stride: 1 • The distance that filter is moved across the input from the previous layer each activation is referred to as the stride.
  • 23. Page  23 Convolutional Layer - Padding  Sometimes it is convenient to pad the input volume with zeros around the border.  Zero padding is allows us to preserve the spatial size of the output volumes EECS6980:006 Social Network Analysis Input Hidden Output 1 D convolution with: Filters: 1 Filter Size: 2 Stride: 2 Padding: 1 0 0 Input Hidden Output 1 D convolution with: Filters: 2 Filter Size: 2 Stride: 2 Padding: 1 0 0
  • 24. Page  24 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example  Strides = 1, Filter Size = 3 X 3 X 1, Padding = 0
  • 25. Page  25 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 26. Page  26 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 27. Page  27 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 28. Page  28 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 29. Page  29 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 30. Page  30 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 31. Page  31 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 32. Page  32 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Navigation Example
  • 33. Page  33 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 34. Page  34 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 35. Page  35 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 36. Page  36 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 37. Page  37 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 38. Page  38 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 39. Page  39 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 40. Page  40 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 41. Page  41 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 42. Page  42 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 43. Page  43 1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 44. Page  44 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 45. Page  45 1 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 46. Page  46 1 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Computation Example
  • 47. Page  47 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 1 1 1 -1 1 1 55 1 1 -1 1 1 1 -1 1 1 Convolutional Layer – Filters – Computation Example
  • 48. Page  48 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 Convolutional Layer – Filters – Computation Example Input Size (W): 9 Filter Size (F): 3 X 3 Stride (S): 1 Filters: 1 Padding (P): 0 9 X 9 7 X 7 Feature Map Size = 1+ (W – F + 2P)/S = 1+ (9 – 3 + 2 X 0)/1 = 7
  • 49. Page  49 1 -1 -1 -1 1 -1 -1 -1 1 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 = = -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Filters – Output Feature Map  Output Feature Map of One complete convolution: – Filters: 3 – Filter Size: 3 X 3 – Stride: 1  Conclusion: – Input Image: 9 X 9 – Output of Convolution: 7 X 7 X 3
  • 50. Page  50 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolutional Layer – Output
  • 51. Page  51 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 52. Page  52 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.77
  • 53. Page  53 0.77 0 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 54. Page  54 0.77 0 0.11 0.33 0.55 0 0.33 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 55. Page  55 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 56. Page  56 ReLU layer 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 0.33 0 0.11 0 0.11 0 0.33 0 0.55 0 0.33 0 0.55 0 0.11 0 0.55 0 0.55 0 0.11 0 0.33 0 1.00 0 0.33 0 0.11 0 0.55 0 0.55 0 0.11 0 0.55 0 0.33 0 0.55 0 0.33 0 0.11 0 0.11 0 0.33 0.33 0 0.55 0.33 0.11 0 0.77 0 0.11 0 0.33 0 1.00 0 0.55 0 0.11 0 1.00 0 0.11 0.33 0.33 0 0.55 0 0.33 0.33 0.11 0 1.00 0 0.11 0 0.55 0 1.00 0 0.33 0 0.11 0 0.77 0 0.11 0.33 0.55 0 0.33 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
  • 57. Page  57 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 58. Page  58 Pooling Layer  The pooling layers down-sample the previous layers feature map.  Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network  The pooling layer often uses the Max operation to perform the downsampling process EECS6980:006 Social Network Analysis
  • 59. Page  59 1.00 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
  • 60. Page  60 1.00 0.33 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
  • 61. Page  61 1.00 0.33 0.55 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
  • 62. Page  62 1.00 0.33 0.55 0.33 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
  • 63. Page  63 1.00 0.33 0.55 0.33 0.33 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
  • 64. Page  64 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 Pooling  Pooling Filter Size = 2 X 2, Stride = 2
  • 65. Page  65 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33 Pooling
  • 66. Page  66 Layers get stacked -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
  • 67. Page  67 Layers Get Stacked - Example 224 X 224 X 3 224 X 224 X 64 CONVOLUTION WITH 64 FILTERS 112 X 112 X 64 POOLING (DOWNSAMPLING)
  • 68. Page  68 Deep stacking -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55
  • 69. Page  69 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Demos & Parameters – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 70. Page  70 Fully connected layer  Fully connected layers are the normal flat feed-forward neural network layers.  These layers may have a non-linear activation function or a softmax activation in order to predict classes.  To compute our output, we simply re- arrange the output matrices as a 1-D array. 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55 1.00 0.55 0.55 1.00 1.00 0.55 0.55 0.55 0.55 1.00 1.00 0.55
  • 71. Page  71 Fully connected layer  A summation of product of inputs and weights at each output node determines the final prediction X O 0.55 1.00 1.00 0.55 0.55 0.55 0.55 0.55 1.00 0.55 0.55 1.00
  • 72. Page  72 Putting it all together -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X O
  • 73. Page  73 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Parameters & Demos – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 74. Page  74 Hyperparameters (knobs)  Convolution – Filter Size – Number of Filters – Padding – Stride  Pooling – Window Size – Stride  Fully Connected – Number of neurons
  • 75. Page  75 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Parameters & Demos – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 76. Page  76 Agenda  Introduction  Architecture Overview – How ConvNet Works  ConvNet Layers – Convolutional Layer – Pooling Layer – Normalization Layer (ReLU) – Fully-Connected Layer  Parameters & Demos – Hyper Parameters – Mnist dataset on AWS – CIFAR-10 ConvNetJS  Case Studies EECS6980:006 Social Network Analysis
  • 77. Page  77 Case Studies  LeNet – 1998  AlexNet – 2012  ZFNet – 2013  VGG – 2014  GoogLeNet – 2014  ResNet – 2015 EECS6980:006 Social Network Analysis
  • 78. Page  78 Thank you!! EECS6980:006 Social Network Analysis