Deep Learning For Vision Analytics - SAS: Analytics, Artificial Intelligence … · 2018-04-19 ·...
Transcript of Deep Learning For Vision Analytics - SAS: Analytics, Artificial Intelligence … · 2018-04-19 ·...
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Deep Learning For Vision AnalyticsSAS User Group Malaysia
12th April, 2018
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Agenda
• What?
• Use cases
• How?
• Image classification
• Basic CNN architecture
• Layer explanation (convolution/pooling/fully connected)
• Deploy
• Create/train/score/deploy model using Jupyter Notebook
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What?Use Cases
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Manufacturing Defect Detection: High Tech Manufacturing
Testing machines are expensive, slow and is usually the bottleneck!
Costs accumulates as chips go further into the production process
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Convolutional Neural
Network: Defect
Classification
Manufacturing Defect Detection: High Tech Manufacturing
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Manufacturing Defect Detection: Reduction in cost and increase in productivity
Eliminate chips with visual defectsRework chips where possible
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More Use CasesFinancial Services Industry
Cognitive computing…
• Some insurers are experimenting with the idea of on-the-spot damage assessment of motor vehicles, using image recognition software that will enable damage assessment, by identifying the make and model of the car, and the extent of damage.
• Imaging technology is being used for identifying and removing fake social accounts and such image-based fake-identification has immense potential in enriching credit-scoring and risk-modelling of banks.
• Authenticating consumer identity documents for banking purposes.
• X-rays, scans, medical reports for insurance underwriting or policy management.
• Etc…
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Workplace Safety Example: Hard hat image detection
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Image Classification
• For us, identifying whether someone is wearing a hard hat or not is effortless
• How do we create and train a machine to do the same???
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How?
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Image Classification using Deep LearningConvolutional Neural Network (CNN)
• Convolutional neural network to analyse images.
• Why?• Powerful (analyse and classify
images very well)
• Efficient (less parameters than previous methods)
• CNN takes image (volume of pixels of varying values) and outputs probabilitySource: http://sww.sas.com/saspedia/Future_documentation_of_deep_learning
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Types of CNN Architecture
• LeNet-5
• AlexNet
• VGG16
• Etc…
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Example CNN architectureConvolution Layer
Source: https://www.packtpub.com/mapt/book/big_data_and_business_intelligence/9781788397872/6/ch06lvl1sec69/common-cnn-architecture---lenet
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Convolution LayerEnables parameter sharing in a CNN
https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified/
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Convolution LayerHow is it done?
Source: https://hackernoon.com/visualizing-parts-of-convolutional-neural-networks-using-keras-and-cats-5cc01b214e59
SWAT.deeplearn.addlayer(layer={‘type’:’convo’,‘nfilters’:1,‘height’:3,‘width’:3,‘stride’:1}
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Feature ‘Detector’ & Feature Maps
Video example: https://www.youtube.com/watch?v=Gu0MkmynWkw
Source: https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
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Example CNN architecturePooling Layer
Source: https://www.packtpub.com/mapt/book/big_data_and_business_intelligence/9781788397872/6/ch06lvl1sec69/common-cnn-architecture---lenet
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Pooling LayerReduces the number of trainable parameters in a CNN
https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified/
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Pooling LayerHow is it done?
Video example: https://www.youtube.com/watch?v=mW3KyFZDNIQ
SWAT.deeplearn.addlayer(layer={‘type’:’pool’,‘height’:2,‘width’:2,‘stride’:2,‘pool’:’MAX’}
Source: https://en.wikipedia.org/wiki/Convolutional_neural_network
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Example CNN architectureFully Connected Layer
Source: https://www.packtpub.com/mapt/book/big_data_and_business_intelligence/9781788397872/6/ch06lvl1sec69/common-cnn-architecture---lenet
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Fully Connected LayerEnables high-level reasoning
• Neurons in a fully connected layer have connections to all activations in the previous layer(s), as seen in regular neural networks.
• Information flows through a neural network in 2 ways:
• Normal (Feedforward)
• Learning (Backpropogation)
https://www.youtube.com/watch?v=aircAruvnKk
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Deployment
SAS VDMML(Model studio)
SAS ESP(Event streaming)
Training
Operational
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Creating/training/scoring/deploying a CNNUsing deepLearn action sets in Jupyter Notebook on SAS Viya 3.3
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Useful Links
• What’s New In SAS Deep Learning (Documentation)
http://go.documentation.sas.com/?docsetId=casdlpg&docsetTarget=p0uhs7ywfs6e4kn160kru9w97fyz.htm&docsetVersion=8.2&locale=en
• Understanding Convolutional Neural Networks
https://adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/
• CS231n Convolutional Neural Networks for Visual Recognition
http://cs231n.github.io/