From Zero To Deep Learning With Scala
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From Zero To Deep Learning With Scala
- 1. From Zero To Deep Learning With Scala Fabio Tiriticco @ticofab ScalaLove - Harmony 18th April 2020
- 2. @ticofab ticofab.io An AI expert Who I am NOT
- 3. @ticofab ticofab.ioWho I am • Scala / Akka enthusiast • Community manager
- 5. @ticofab ticofab.ioThe Surroundings Of My Office Original airport area Second strip built Brigde over highway
- 6. @ticofab ticofab.ioView From My Office
- 7. @ticofab ticofab.ioThe Planes Idea Camera Module Raspberry PI
- 9. AI Overview
- 10. @ticofab ticofab.ioAI, Machine Learning & Deep Learning Artificial Intelligence (AI) Machine Learning (ML) Deep Learning (DL)
- 11. @ticofab ticofab.ioThe Pyramid Of Wisdom KNOWLEDGE INFORMATION RAW DATA Typical application of ML / AI WISDOM 🏆
- 12. @ticofab ticofab.ioTwo Common Uses for ML / AI Regression “How much?” “What kind?” Classification
- 13. @ticofab ticofab.ioOur Case: Classification Between Two Classes PLANE NO PLANE
- 14. @ticofab ticofab.ioHow To Build Our AI? (A revelation) AI is still at the stage of trial & error. We only know that for some problems, certain architectures work better than others.
- 15. @ticofab ticofab.ioProblems Matched By Architectures TYPE OF DEEP NETWORK TYPICAL USE CASES Unsupervised Pretrained Networks Reproduce existing models (sound, video, handwriting…) Convolutional Neural Networks CNN Image recognition (faces, animals, street signs..) Recurrent Neural Networks RNN Processes where inputs form sequences in time (speech synthesis, translation…) …. …
- 16. The Basics Of Neural Networks
- 17. @ticofab ticofab.ioThe Artificial Neuron f(w1, w2, .. wn) w1 w2 wn … a(res)res WEIGHTED INPUTS TRANSFER FUNCTION ACTIVATION FUNCTION NET INPUT
- 18. @ticofab ticofab.io T P G Multilayer Neural Network INPUT VALUES INPUT LAYER HIDDEN LAYER 1 HIDDEN LAYER 2 OUTPUT LAYER OUTPUT VALUES A B C 2 5 7 1
- 20. @ticofab ticofab.ioThe Power Of CNNs Automatic feature extraction Able to identify a complex combination of features
- 21. @ticofab ticofab.ioHow Does A Convolutional Layer Work? Learns filters that, applied to the training images, see the rabbit features The network learns the “rabbitness” of the rabbit - what makes it unique! 🐰 TRAIN WITH 1000s OF IMAGES
- 22. @ticofab ticofab.ioA Typical CNN Architecture (LeNet) Convolutional Layer Convolutional Layer Subsampling Layer Subsampling Layer Dense Layer (500 fully connected neurons) Output Layer Input Layer
- 24. @ticofab ticofab.ioTraining Images - Crop And Resize To 171 x 25 Pixels NO PLANEPLANE Split the image dataset in two: TRAINING SET 80% TEST SET 20%
- 25. @ticofab ticofab.ioSome settings 171 px 25 px /// information about the image val inputHeight = 25 /// pixels val inputWidth = 171 /// pixels val depth = 3 /// Number of input channels (RGB) /// number of possible outcomes (PLANE or NO PLANE) val numClasses = 2
- 26. @ticofab ticofab.ioOur Network In Scala Layer By Layer new NeuralNetConfiguration.Builder().layer(0, new ConvolutionLayer.Builder(5, 5) .nIn(depth) .stride(1, 1) .nOut(20) .build) .layer(1, new SubsamplingLayer.Builder() .kernelSize(2, 2) .stride(2, 2) .build) .layer(2, new ConvolutionLayer.Builder(5, 5) .stride(1, 1) .nOut(50) .build) .layer(3, new SubsamplingLayer.Builder() .kernelSize(2, 2) .stride(2, 2) .build) .layer(4, new DenseLayer.Builder() .nOut(500) .build) .layer(5, new OutputLayer.Builder() .nOut(numClasses) .build) .setInputType(InputType.convolutional( (inputHeight, inputWidth, depth)) .build new NeuralNetConfiguration.Builder() .layer(0, new ConvolutionLayer.Builder(5, 5) .nIn(depth) .stride(1, 1) .nOut(20) .build) .setInputType(InputType.convolutional( inputHeight, inputWidth, depth)) .build .layer(1, new SubsamplingLayer.Builder() .kernelSize(2, 2) .stride(2, 2) .build) .layer(2, new ConvolutionLayer.Builder(5, 5) .stride(1, 1) .nOut(50) .build) .layer(3, new SubsamplingLayer.Builder() .kernelSize(2, 2) .stride(2, 2) .build) .layer(4, new DenseLayer.Builder() .nOut(500) .build) .layer(5, new OutputLayer.Builder() .nOut(numClasses) .build) PSEUDO CODEConvolutional Layer Convolutional Layer Subsampling Layer Subsampling Layer Dense Layer (500 fully connected neurons) Output Layer Input Layer
- 27. @ticofab ticofab.ioTrain And Evaluate Our Network • Read images • Split them in two datasets - train + test • Decide the stopping criteria for training val tc = new MaxTimeIterationTerminationCondition(6 MINUTES) val conf = new EarlyStoppingConfiguration.Builder[MultiLayerNetwork]() .iterationTerminationConditions(tc) .modelSaver(saver) .build • Train and save the model val bestModel = new EarlyStoppingTrainer(CONF + MODEL + FILES) .fit.getBestModel PSEUDO CODE
- 28. @ticofab ticofab.ioLogs of a training run
- 29. The Pipeline
- 30. @ticofab ticofab.ioAI is only a small bit of AI projects Planes Detection Flow Tick every hour Pick earliest plane pic Pick random quote Tweet Move to TWEETED folder New picture detected Trim picture Crop image 121x75 Move to PLANE folder Detect Plane Plane detected Nothing detected Move to NO PLANE folder Manually check pics Train new model Replace old model Read log files Check against detection Evaluate metrics Save to CSV Twitter Flow Train & Performance Evaluation Flow Take pic every minute
- 31. Results
- 33. @ticofab ticofab.ioConfusion Matrix True Positive (TP) There was a plane and the model saw it 🧐 False Positive (FP) The model saw a plane but there wasn’t any 🤥 False Negative (FN) There was a plane but the model didn’t see it 🥴 True Negative (TN) There was no plane and the model didn’t see one 😅
- 35. @ticofab ticofab.ioModel Performance Recall = TP / TP + FN How well the model avoids false negatives Precision = TP / TP + FP How well the model avoids false positives When my model sees a plane, it’s probably there. Unfortunately, it misses quite a few planes.
- 36. @ticofab ticofab.ioWhen does it go wrong?
- 37. @ticofab ticofab.ioAI Bias. So Many Blue Planes!
- 38. Next Steps For You
- 39. @ticofab ticofab.ioNext Steps • Find a fun use-case and a dataset https:///github.com/ticofab/deep-learning-with-scala • Learn a few more details about AI and CNNs • Create and train your network with Deeplearning4j ! • https:///gitter.im/Machine-Learning-in-Scala/community
- 40. @ticofab ticofab.ioReferences • DeepLearning4j - https:///deeplearning4j.org/ • https:///github.com/ticofab/deep-learning-with-scala Deep Learning: A Practitioner’s Approach Josh Patterson & Adam Gibson
- 41. Thanks and stay safe! Fabio Tiriticco @ticofab ScalaLove - Harmony 18th April 2020