Convolutional neural networks (CNN) Deep Learning  Part 3
Contributors
Kaivan KamaliQuestions
What is a convolutional neural network (CNN)?
What are some applications of CNN?
Objectives
Understand the inspiration behind CNN and learn the CNN architecture
Learn the convolution operation and its parameters
Learn how to create a CNN using Galaxy’s deep learning tools
Solve an image classification problem on MNIST digit classification dataset using CNN in Galaxy
Requirements

Statistics and machine learning
 Introduction to deep learning: tutorial handson
 Deep Learning (Part 1)  Feedforward neural networks (FNN): slides slides  tutorial handson
 Deep Learning (Part 2)  Recurrent neural networks (RNN): slides slides  tutorial handson
last_modification Last modification: Jul 9, 2021
What is a convolutional neural network (CNN)?
Speaker Notes
What is a convolutional neural network (CNN)?
Convolutional Neural Network (CNN)
 Increasing popularity of social media in past decade
 Image and video processing tasks have become very important
 FNN could not scale up to image and video processing tasks
 CNN specifically tailored for image and video processing tasks
Feedforward neural networks (FNN)
 In FNN all nodes in a layer connected to all nodes in next layer
 Each connection has a weight, must be learned by learning algorithm
Limitations of FNN
 If input is 64 pixel by 64 pixel grayscale image
 Each grayscale pixel represented by 1 value, usually between 0 to 255
 Where 0 is black, 255 is white, and values in between are shades of gray
 Since each grayscale pixel represented by 1 value, we say channel size is 1
 Image represented by 64 x 64 x 1 = 4,096 values (rows x columns x channels)
 Hence, input layer of FNN has 4096 nodes
 Lets assume next layer has 500 nodes
 Since FNN fully connected, we have 4,096 x 500 = 2,048,000 weights
Limitations of FNN
 For complex problems, we need multiple hidden layers in our FNN
 Compunds the problem of having many weights
 Having too many weights
 Makes learning more difficult as dimension of search space is increased
 Makes training more time/resource consuming
 Increases the likelihood of overfitting
 Problem is further compunded for color images
 Each pixel in color image represented by 3 values (RGB color mode)
 Since each pixel represented by 3 values, we say channel size is 3
 Image represented by 64 x 64 x 3 = 12,288 values (rows x columns x channels)
 Number of weights is now 12,288 x 500 = 6,144,000
Limitations of FNN
 Clear that FNN cannot scale to larger images (Too many weights)
 Another problem with FNN
 2D image represented as 1D vector in input layer
 Any spatial relationship in the data is ignored
Inspiration for CNN
 In 1959 Hubel & Wiesel did an experiment to understand how visual cortex of brain processes visual info
 Recorded activity of neurons in visual cortex of a cat
 While moving a bright line in front of the cat
 Some cells fired when bright line is shown at a particular angle/location
 Called these simple cells
 Other cells fired when bright line was shown regardless of angle/location
 Seemed to detect movement
 Called these complex cells
 Seemed complex cells receive inputs from multiple simple cells
 Have an hierarchical structure
 Hubel and Wiesel won Noble prize in 1981
Inspiration for CNN
 Inspired by complex/simple cells, Fukushima proposed Neocognitron (1980)
 Hierarchical neural network used for handwritten Japanese character recognition
 First CNN, had its own training algorithm
 In 1989, LeCun proposed CNN that was trained by backpropagation
 CNN got popular when outperformed other models at ImageNet Challenge
 Competition in object classification/detection
 On hundreds of object categories and millions of images
 Run annually from 2010 to present
 Notable CNN architectures that won ImageNet challenge
 AlexNet (2012), ZFNet (2013), GoogLeNet & VGG (2014), ResNet (2015)
Architecture of CNN
 A typical CNN has 4 layers
 Input layer
 Convolution layer
 Pooling layer
 Fully connected layer
 We will explain a 2D CNN here
 Same concepts apply to a 1 (or 3) dimensional CNN
Input layer
 Example input a 28 pixel by 28 pixel grayscale image
 Unlike FNN, we do not “flatten” the input to a 1D vector
 input is presented to network in 2D as 28 x 28 matrix
 This makes capturing spatial relationships easier
Convolution layer
 Composed of multiple filters (kernels)
 Filters for 2D image are also 2D
 Suppose we have a 3 by 3 filter (9 values in total)
 Values are randomly set to 0 or 1
 Convolution: placing 3 by 3 filter on the top left corner of image
 Multiply filter values by pixel values, add the results
 Move filter to right one pixel at a time, and repeat this process
 When at top right corner, move filter down one pixel and repeat process
 Process ends when we get to bottom right corner of image
3 by 3 Filter
Covolution operator parameters
 Filter size
 Padding
 Stride
 Dilation
 Activation function
Filter size
 Filter size can be 5 by 5, 3 by 3, and so on
 Larger filter sizes should be avoided
 As learning algorithm needs to learn filter values (weights)
 Odd sized filters are preferred to even sized filters
 Nice geometric property of all input pixels being around output pixel
Padding
 After applying 3 by 3 filter to 4 by 4 image, we get a 2 by 2 image – Size of the image has gone down
 If we want to keep image size the same, we can use padding
 We pad input in every direction with 0’s before applying filter
 If padding is 1 by 1, then we add 1 zero in evey direction
 If padding is 2 by 2, then we add 2 zeros in every direction, and so on
3 by 3 filter with padding of 1
Stride
 How many pixels we move filter to the right/down is stride
 Stride 1: move filter one pixel to the right/down
 Stride 2: move filter two pixels to the right/down
3 by 3 filter with stride of 2
Dilation
 When we apply 3 by 3 filter, output affected by pixels in 3 by 3 subset of image
 Dilation: To have a larger receptive field (portion of image affecting filter’s output)
 If dilation set to 2, instead of contiguous 3 by 3 subset of image, every other pixel of a 5 by 5 subset of image affects output
3 by 3 filter with dilation of 2
Activation function
 After filter applied to whole image, apply activation function to output to introduce nonlinearlity
 Preferred activation function in CNN is ReLU
 ReLU leaves outputs with positive values as is, replaces negative values with 0
Relu activation function
Single channel 2D convolution
Triple channel 2D convolution
Triple channel 2D convolution in 3D
Change channel size
 Output of a multichannel 2D filter is a single channel 2D image
 Applying multiple filters results in a multichannel 2D image
 E.g., if input image is 28 x 28 x 3 (rows x columns x channels)
 We apply a 3 x 3 filter with 1 x 1 padding, we get a 28 x 28 x 1 image
 If we apply 15 such filters, we get a 28 x 28 x 15
 Number of filters allows us to increase or decrease channel size
Pooling layer
 Pooling layer performs down sampling to reduce spatial dimensionality of input
 This decreases number of parameters
 Reduces learning time/computation
 Reduces likelihood of overfitting
 Most popular type is max pooling
 Usually a 2 x 2 filter with a stride of 2
 Returns maximum value as it slides over input data
Fully connected layer
 Last layer in a CNN
 Connect all nodes from previous layer to this fully connected layer
 Which is responsible for classification of the image
An example CNN
An example CNN
 A typical CNN has several convolution plus pooling layers
 Each responsible for feature extraction at different levels of abstraction
 E.g., filters in first layer detect horizental, vertical, and diagonal edges
 Filters in the next layer detect shapes
 Filters in the last layer detect collection of shapes
 Filter values randomly initialized, learned by learning algorithm
 CNN not only do classification, but can also automatically do feature extraction
 Distinguishes CNN from other classification techniques (like Support Vector Machines)
MNIST dataset
 MNIST dataset of handwritten digits
 Composed of training set of 60,000 and test set of 10,000 images
 Digits have been sizenormalized/centered in a fixedsize image (28 by 28 pixels)
 Images are grayscale
 Each pixel is represented by a number between 0 and 255
 0 for black, 255 for white, and other values for shades of gray
 MNIST dataset is a standard image classification dataset

Used to compare various Machine Learning techniques

Classification of MNIST images with CNN
 We define a CNN and train it using MNIST dataset training data
 Goal is to learn a model such that given image of a digit we predict the digit (0 to 9)
 We then evaluate the trained CNN on test dataset and plot the confusion matrix
For references, please see tutorial’s References section
 Galaxy Training Materials (training.galaxyproject.org)
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