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Intro To Convolu,onal Neural Networks Mark Scully datapraxis.com
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Intro To Convolu,onal Neural Networks
Mark Scully datapraxis.com
Why CNNs?
h@ps://papers.nips.cc/paper/4824-‐imagenet-‐classifica,on-‐with-‐deep-‐convolu,onal-‐neural-‐networks
Image Classifica,on
Object Recogni,on
h@ps://research.googleblog.com/2014/09/building-‐deeper-‐understanding-‐of-‐images.html
h@p://cs.stanford.edu/people/karpathy/deepimagesent/
Automa,c Cap,oning
h@ps://research.googleblog.com/2014/11/a-‐picture-‐is-‐worth-‐thousand-‐coherent.html
Facial Recogni,on
Y. Taigman, M. Yang, M. Ranzato, L. Wolf, DeepFace: Closing the Gap to Human-‐Level Performance in Face Verifica,on, CVPR 2014
Terminator Vision
Colorize Black & White Images
h@p://richzhang.github.io/coloriza,on/
Style Transfer
h@p://genekogan.com/works/style-‐transfer/
Mona Lisa restyled by Picasso, van Gough, and Monet
Generate An Image From A Sketch
h@ps://affinelayer.com/pixsrv/
ImageNet Challenge
Alexnet Li Fei-‐Fei: ImageNet Large Scale Visual Recogni,on Challenge, 2014
ImageNet Challenge ILSVRC+
ImageNet Classification error throughout years and groups
Li Fei-‐Fei: ImageNet Large Scale Visual Recogni,on Challenge, 2014
Alexnet Architecture -‐ 2012
Input Conv
Relu Pool
Conv
Relu
Pool
Conv
Relu
Conv
Relu
Conv Relu
Pool
FC
Dropout
FC
Dropout
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp.
1097-‐1105, 2012
FC 1000
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
ImageNet Challenge
Alexnet Li Fei-‐Fei: ImageNet Large Scale Visual Recogni,on Challenge, 2014
Tradi,onal Approach To Image Classifica,on
Input Image Hand
Extracted Features
Classifier Object Label
Issues
• Who makes the features? – Need an expert for each problem domain
• Which features? – Are they the same for every problem type?
• How robust are these features to real images? – Transla,on, Rota,on, contrast changes, etc.
Are these pictures of the same thing?
Features Are Hierarchical
• A squirrel is a combina,on of fur, arms, legs, & a tail in specific propor,ons.
• A tail is made of texture, color, and spa,al rela,onships
• A texture is made of oriented edges, gradients, and colors
Image Features
• A feature is something in the image or derived from it that’s relevant to the task
• Edges • Lines at different angles, curves, etc. • Colors, or pa@erns of colors • SIFT, SURF, HOG, GIST, ORB, etc
Edges
Ideally We’d Learn Features
Input Image
Output Label
Ideally We’d Learn Features
Input Image
Output Label
CNNs
What is a Neural Network?
• Perceptron is biologically inspired • A mental model for interpre,ng the math
h@p://cs231n.stanford.edu/index.html
Perceptron
1
x1
x2
x3
xm
Σ Output
Ac,va,on Func,on Sum
w0
w1
w2
w3
wm
Weights Inputs
Perceptron
1
x1
x2
x3
xm
Σ Output
Ac,va,on Func,on Sum
w0
w1
w2
w3
wm
Weights Inputs
wixii=0
m
∑ = w0x0 +w1x1 +w2x2 +...+wmxm
Ac,va,on Func,ons
Training: Upda,ng Weights
1
x1
x2
x3
x4
Σ Output
Ac,va,on Func,on Sum
w0
w1
w2
w3
w4
Weights Inputs
Error = Output -‐ Target
Perceptron Decision Boundary
Deep (Mul,-‐Layer) Neural Network
Backpropaga,on
• Error propagates backward and it all works via (normally stochas,c) gradient descent.
• (wave hands)
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
CNN Layer Architecture
Input
Convolu,on
Nonlinearity
Pooling (op,onal)
Dropout (op,onal)
CNN Layer Architecture
Input
Convolu,on
Nonlinearity
Pooling (op,onal)
Dropout (op,onal)
Input: Pixels Are Just Numbers
h@ps://medium.com/@ageitgey/machine-‐learning-‐is-‐fun-‐part-‐3-‐deep-‐learning-‐and-‐convolu,onal-‐neural-‐networks-‐f40359318721
CNN Layer Architecture
Input
Convolu,on
Nonlinearity
Pooling (op,onal)
Dropout (op,onal)
Goals
• Need to detect the same feature anywhere in an image
• Reuse the same weights over and over • What we really want is one neuron that detects a feature that we slide over the image
Neuron = Filter
• Act as detectors for some specific image feature
• Take images as inputs and produce image like feature maps as outputs
Convolu,on
• Like sliding a matrix over the input and performing dot products
• It’s all just matrix mul,plica,on
Convolu,on
Convolu,on
Filters (or Kernels)
Sharpen
Filters (or Kernels)
Box Blur
Filters (or Kernels)
Edge Detec,on
Feature Map
Alexnet Architecture
Convolu,ons
CNN Layer Architecture
Input
Convolu,on
Nonlinearity
Pooling (op,onal)
Dropout (op,onal)
Nonlinearity
CNN Layer Architecture
Input
Convolu,on
Nonlinearity
Pooling (op,onal)
Dropout (op,onal)
Max Pooling Example
Alexnet Architecture
3x3 stride 2 Max Pooling
Pooling
• Allows us to look at more of the image • Max, sum, and L2 pooling • A type of downsampling
CNN Layer Architecture
Input
Convolu,on
Nonlinearity
Pooling (op,onal)
Dropout (op,onal)
Alexnet Architecture -‐ 2012
Input Conv
Relu Pool
Conv
Relu
Pool
Conv
Relu
Conv
Relu
Conv Relu
Pool
FC
Dropout
FC
Dropout
FC 1000
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp.
1097-‐1105, 2012
Dropout
Dropout
h@p://cs231n.github.io/neural-‐networks-‐2/
• Randomly disable some neurons on the forward pass
• Prevents overfiong
Let’s Predict Something!
• We have all these features, how do we learn to label something based on them?
Alexnet Architecture -‐ 2012
Input Conv
Relu Pool
Conv
Relu
Pool
Conv
Relu
Conv
Relu
Conv Relu
Pool
FC
Dropout
FC
Dropout
FC 1000
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp.
1097-‐1105, 2012
Fully Connected
Fully Connected Layers
• Each neuron is connected to all inputs • Standard mul,layer neural net • Learns non-‐linear combina,ons of the feature maps to make predic,ons
Alexnet Architecture
Alexnet Architecture -‐ 2012
Input Conv
Relu Pool
Conv
Relu
Pool
Conv
Relu
Conv
Relu
Conv Relu
Pool
FC
Dropout
FC
Dropout
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp.
1097-‐1105, 2012
FC 1000
Which Class Is It Again?
• FC-‐1000 gives us 1000 numbers, one per class, how do we compare them?
Soqmax
• Mul,-‐class version of logis,c func,on • Outputs normalized class “probabili,es” • Takes m inputs and produces m outputs between zero and one, that sum to one
• Cross-‐entropy loss • Differen,able
h@ps://papers.nips.cc/paper/4824-‐imagenet-‐classifica,on-‐with-‐deep-‐convolu,onal-‐neural-‐networks
Image Classifica,on
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
Layer 1
Learned Filters – Layer1
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
Layer 2
Learned Filters – Layer2
Visualizing and Understanding Convolu,onal Networks -‐ Zeiler & Fergus, ECCV 2014
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
Layer 3
Learned Filters -‐ Layer3
Visualizing and Understanding Convolu,onal Networks -‐ Zeiler & Fergus, ECCV 2014
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
Layer 4 Layer 5
Learned Features – Layers 4 & 5
Alexnet Architecture -‐ 2012
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp. 1097-‐1105,
2012
Alexnet Architecture -‐ 2012
Input Conv
Relu Pool
Conv
Relu
Pool
Conv
Relu
Conv
Relu
Conv Relu
Pool
FC
Dropout
FC
Dropout
ImageNet Classifica,on with Deep Convolu,onal Neural Networks Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton Advances in Neural Informa,on Processing Systems 25 eds.F. Pereira, C.J.C. Burges, L. Bo@ou and K.Q. Weinberger pp.
1097-‐1105, 2012
FC 1000
VGG16
h@ps://blog.heuritech.com/2016/02/29/a-‐brief-‐report-‐of-‐the-‐heuritech-‐deep-‐learning-‐meetup-‐5/
Google’s Incep,on Module
To Learn More
• h@p://colah.github.io/posts/2014-‐07-‐Understanding-‐Convolu,ons/
• h@ps://adeshpande3.github.io/adeshpande3.github.io/The-‐9-‐Deep-‐Learning-‐Papers-‐You-‐Need-‐To-‐Know-‐About.html
• h@p://cs231n.github.io/ • h@p://course.fast.ai/
Ques,ons?
