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Transfer learning for IoT
- 1. Transfer Learning for IoT GEETACHAUHAN JUNE, 2017
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- 3. What is Deep Learning? AINeural Networks composed of many layers Learn like humans Automated Feature Learning Layers are like Image Filters
- 4. Tensorflow Android Things AddTensorflow Inferencing library Create Inference object, load model feed() image, run() to predict fetch() to get classification result
- 5. Transfer Learning Transfer Learning –New Classifier Inception V3
- 6. Optimizations Graph TransformTool Freeze graph (variables to constants) Quantize weights (20 M weights for IV3) Quantization (32 bit float → 8 bit float) Memory Mapping Inception v3 93 MB → 1.5 MB
- 7. Intelligent Door Bell IntelEdison + Seed Studio Kit Raspberry Pi + Pi Camera
- 8. Tensorflow IoT Pipeline Inferenceon DeviceTrain Model in Cloud
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- 10. Caltrain Rider RealtimeCaltrain arrival prediction Audio Visual pipeline on Raspberry pi Image classification for Caltrains
- 11. And Some More… Traffic sign detector for Self Driving Car Wearable assistant for Blind (Horus) Smart cameras, smart door lock Human line counter Real time exercise score on smart watch Creative Arts, Music Generators Intelligent Robots
- 12. Look into theFuture Hardware: Neural Network Chips Intel Fathom Neural Stick Nvidia Jetson
- 13. References Tensorflow forAndroid Things Sample Tensorboard hands-on Graph Transform Tool Tensorflow for Poets (Transfer learning) Tensorflow for Poets2 (Optimizations for Mobile/IoT) Cucumber Farmer Deep Learning Story Caltrain Rider story Intel Fathom Neural Stick Nvidia Jetson
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- 15. Common Problem Solutions Tensorboard is your friend – X-Ray vision Image size mismatch for the input tensor Output classifier specify correct number of classes from your model Model too large – Load in memory Missing ops -- Graph transform tool Device heating up under heavy processing load Split the model, do part detection on device, rest in cloud Reduce the frequency eg only do on movement detection
- 16. Era of AIFirst Billions of connected devices Intelligence at the Edge Increasing Computation power Edison: 500 MHz, 1 GB RAM RPi3: 1.2 GHz Quad-core Deep neural networks running on the IoT device Local inferencing → compressed insights to cloud