SMART MANUFACTURING With Apache Spark Streaming and Deep Leaning - - PowerPoint PPT Presentation

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SMART MANUFACTURING With Apache Spark Streaming and Deep Leaning - - PowerPoint PPT Presentation

Aug 26, 2022 203 likes •667 views

ApacheCon Big Data Europe Nov 2016, Seville, Spain SMART MANUFACTURING With Apache Spark Streaming and Deep Leaning Prajod Vettiyattil, Wipro @prajods https://in.linkedin.com/in/prajod 2 AGENDA Introduction Smart Manufacturing

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SMART MANUFACTURING

With Apache Spark Streaming and Deep Leaning ApacheCon Big Data Europe Nov 2016, Seville, Spain Prajod Vettiyattil, Wipro @prajods https://in.linkedin.com/in/prajod

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AGENDA

  • Introduction
  • Smart Manufacturing
  • Applications
  • Deep learning solution
  • CNN
  • Deeplearning4j (DL4J)
  • Apache Spark
  • Deploying machine learning

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INTRODUCTION

  • Industrialization
  • Automation
  • Scale
  • Commoditization
  • Accessibility

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MEN AND MACHINES

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KEY POINT

Machines can do what humans can’t

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SMART MANUFACTURING

  • Holistic view of entire process
  • Sourcing to product sale
  • Efficiency
  • Eco friendliness
  • Predict over react
  • Autonomous machines
  • Learning and AI
  • Use data and analytics

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APPLICATIONS

  • Detecting overheating
  • Equipment
  • Finished products
  • Glass defect detection

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OVERHEATING

  • Detect temperatures above

defined boundaries

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COOLING TOWER

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ELECTRIC MOTOR

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ELECTRIC MOTOR: OVERHEATING

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OVERHEATING: LOWER RESOLUTION

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ELECTRIC MOTOR: NORMAL VIEW

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GLASS DEFECT

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ZOOMING IN

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UNDER A DIFFERENT LIGHT

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AUTOMATING DETECTION

  • Machine learning
  • Multivariate Analysis
  • Multiple categories of defects
  • Deep Learning

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DEEP LEARNING: WHAT

  • Neural Network based Machine Learning
  • Neural Network
  • 1 Input layer
  • 1 or more hidden layers
  • 1 Output layer
  • Basic unit of NN
  • Neuron or Perceptron
  • Combine perceptrons in many

ways using multiple parameters

  • Model parameters
  • Hyperparameters

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Image from: ucalgary.ca

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DEEP LEARNING: WHY

  • Can discern complex input patterns
  • Higher accuracy than most other ML methods
  • for image analysis
  • Ideally suited for spatial data analysis
  • Higher time needed for training
  • Parallel execution
  • Higher level architectures for evolving needs
  • CNN, RNN, LSTM

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CONVOLUTIONAL NEURAL NETWORKS

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CONVOLUTIONAL NEURAL NETWORK

  • Convolutional Neural Network (CNN)
  • Convolve: mix two functions
  • Matrix multiplication and addition
  • Dot product
  • Pass many filters over an image
  • Image features
  • Images are 3D features
  • Discern the important features/signals
  • Filter = kernel

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CNN: APPLICATION

  • Self driving cars
  • Robotics
  • Drones
  • Industrial automation
  • Physical security
  • Medical labs
  • Wherever images or videos are used

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IMAGE FOR CONVOLUTION

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CONVOLUTION

Filter matrix Image

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CNN ARCHITECTURE

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1st Convolution Non linear transform

(RELU, tanh etc)

Pooling Nth Convolution Non linear transform

(RELU, tanh etc)

Pooling Fully connected layers Output probabiities Feature extraction + dimension reduction Classification

…..

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CNN PARAMETERS

  • Model parameters
  • Filter values
  • Weights
  • Biases

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  • Hyperparameters
  • Activation function
  • # layers
  • # nodes in each layer
  • Filter size and count
  • Regularization
  • Convolution layers
  • Maxpooling layers

Learned by NN Experience of designer

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CNN: SAMPLE CODE

  • MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()

.seed(seed).iterations(iterations).regularization(false).l2(0.003).activation(“relu") .learningRate(0.0007).weightInit(WeightInit.XAVIER) .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(Updater.RMSPROP).momentum(0.9).list() .layer(0, convInit("cnn1", channels, 50 , new int[]{5, 5}, new int[]{1, 1}, new int[]{0, 0}, 0)) .layer(1, maxPool("maxpool1", new int[]{2,2})) …. .layer(4, new DenseLayer.Builder().nOut(500).build()) .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) .nOut(numLabels).activation("softmax").build()) .backprop(true).pretrain(false).cnnInputSize(height, width, channels).build();

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DEEPLEARNING4J (DL4J)

  • Deep learning library
  • Open source
  • Apache 2.0 license
  • Java based
  • Distributed execution
  • Runs on Spark and Hadoop

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DEEPLEARNING4J: FEATURES

  • ND4J
  • N Dimensional Arrays/Tensors
  • Like numpy
  • Canova/DataVec
  • Data extraction, vectorization
  • Arbitrer
  • Evaluate and tune models
  • CPU or GPU
  • OpenBLAS, Intel MKL, Nvidia CUDA

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DEEPLEARNING4J: SUPPORT

  • Community support
  • https://gitter.im/deeplearning4j/deeplearning4j
  • Commercial support
  • www.skymind.io

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APACHE SPARK

  • Incubated in 2009 at Berkeley University
  • 100s of contributors
  • Yahoo, Intel, UC Berkeley,…,50+ orgs
  • 10 to 100 times faster than Hadoop MR
  • https://databricks.com/blog/2014/10/10/spark-petabyte-sort.html

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APACHE SPARK

  • Complements Hadoop
  • Replaces Hadoop MR
  • Adds
  • In memory processing
  • Stream processing
  • Interactive queries
  • YARN or Mesos for clustering
  • Java, Scala, Python, R

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SPARK: LOGICAL VIEW

Apache Spark Core

Spark SQL Spark Streaming MLlib GraphX

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SPARK: DEPLOYMENT VIEW

Worker Node Executor Master Node

Executor

Task

Cache

Task

Task

Worker Node Executor Executor

Executor

Task Task

Task Cache Spark’s Cluster Manager Spark Driver

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PARALLEL TRAINING: SPARK + DL4J

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Spark Worker Node 1

Spark Master Node

Spark Worker Node 2

Data Data Split 1 Data Split 2 Mini Batch 1 Mini Batch 2

Calculate model parameters Calculate model parameters Averaged parameters from workers

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THE MACHINE LEARNING PROCESS

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A LONG JOURNEY

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Select problem Non ML solution ? Analyze process Data available ? Define success KPI Classify ML type Select 2 or 3 algo Pick one Gather data Analyze data Filter data Convert data Vectorize

Select hyperparams Iterate hyperparams

Iterate data sets Evaluate perform Validation set Deploy Continuous eval

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SELECT PROBLEM

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GATHER AND FORMAT DATA

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PROGRAM

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ITERATE WITH HYPERPARAMETERS AND DATA

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CONTINUOUS EVALUATION

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SUMMARY

  • Automation and commoditization
  • Humans and machines
  • Beyond human capacity
  • Application to product quality inspection
  • Deep leaning and images: higher accuracy
  • CNN
  • DL4J + Spark
  • The Machine Learning process

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REFERENCES

  • https://en.wikipedia.org/wiki/Smart_manufacturing
  • http://karpathy.github.io/neuralnets/
  • http://cs231n.github.io/convolutional-networks/
  • https://deeplearning4j.org/convolutionalnets.html
  • https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
  • https://en.wikipedia.org/wiki/Glass_production
  • http://www.qorpak.com/pages/lab-glass-defects-inspect-packaging
  • https://en.wikipedia.org/wiki/Automated_analyser

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QUESTIONS

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Prajod Vettiyattil, Wipro @prajods https://in.linkedin.com/in/prajod