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Mobile Fest 2018. Enrique López Mañas. TensorFlow for Mobile Poets
- 1. TensorFlow for Mobile Developers Enrique López Mañas Google Developer Expert
- 2. Ego Slide • Freelance Dev • Google Developer Expert • @eenriquelopez
- 3. Machine Learning / AI
- 4. Machine Learning / AI
- 5. Machine Learning / AI
- 6. Buzzwords classification Artificial intelligence Machine Learning The science of making machines smarts Building machines that can learn Neural Networks One of the many different algorithms in Machine Learning
- 10. Machine Learning
- 11. Machine Learning 12% of all responses on mobile
- 12. Machine Learning
- 13. Machine Learning
- 14. Machine Learning
- 16. Practical Use Cases “Recognising Beer TensorFlow”
- 17. Cloud vs. Mobile Less Traffic & Faster Response Motion Sensors
- 18. TensorFlow OpenSource library for Machine Learning tensorflow.org Today most popular ML framework
- 19. TensorFlow Lite vs. TensorFlow Mobile - TensorFlow Lite: smaller binary size, fewer dependencies, better performance - TensorFlow Lite: developer preview, not for production! - TensorFlow Lite: supports limited set of operators, not all models will work.
- 20. TensorFlow You can train: - Mac / Windows - GPU Server - GPU/TPU on Cloud Prediction: - Android - iOS - Raspberry
- 23. From Training to App (@yufengg)
- 24. Data Gathering?
- 25. From Training to App (@yufengg)
- 26. From Training to App (@yufengg)
- 27. Convolutional Neural Networks (@yufengg)
- 28. Convolutional Neural Networks (@yufengg)
- 29. Convolutional Neural Networks (@yufengg)
- 30. Convolutional Neural Networks (@yufengg)
- 31. Convolutional Neural Networks (@yufengg)
- 32. Convolutional Neural Networks (@yufengg)
- 33. Convolutional Neural Networks (@yufengg)
- 34. Convolutional Neural Networks (@yufengg)
- 35. From Training to App (@yufengg)
- 36. Optimizing model for Mobile (@yufengg)
- 37. From Training to App (@yufengg) Other inception versions (inception v1 quantised is 7 MB)
- 38. From Training to App (@yufengg) TensorFlow increases APK in 12 MB
- 39. From Training to App (@yufengg)
- 40. From Training to App (@yufengg)
- 41. TensorFlow community 1000+ contributors 22.000+ commits 18.000+ repositories with name “TensorFlow”
- 43. Building with Android Studio Uses Bazel to build Set Bazel binary location in /tensorflow/examples/ android/build.gradle Add project in tensorflor/examples/android folder to Android Studio
- 44. Building with Android Studio Installing Bazel first: Download from https://github.com/bazelbuild/bazel/releases Install instructions in https://docs.bazel.build/versions/master/install.html
- 45. Building with Android Studio Download Android SDK (23) Download Android NDK (12b) (recommend it with Android SDK Manager)
- 46. Building with Android Studio WORKSPACE FILE
- 47. Building with Android Studio Run Bazel bazel build -c opt //tensorflow/examples/android:tensorflow_de
- 48. Building with Android Studio Install APK adb install -r bazel-bin/tensorflow/examples/android/tensorflow
- 49. Building with Android Studio OR… TensorFlow AAR from JCenter YUV -> RGB less efficient Object tracking not available
- 50. Android Samples TF Classify TF Detect TF Stylize
- 51. TF Classify Uses Google Inception (v3) to label images Model easy to swap No “person label” Volume button up for statistics
- 52. TF Classify
- 53. TF Classify
- 54. TF Detect
- 55. TF Detect - Draw bounding boxes around people - Useful to count objects - No training yet
- 56. TF Style
- 57. TF Style - Real time style transfer algorithm - Pick/mix different styles - Can train Magenta models
- 58. TF Style
- 59. TensorFlow Written in C++ Android uses Kotlin/Java? HowTo? Android inference Library https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/android
- 60. Building for iOS Install requirements: - Xcode 8 - Command line tools (xcode-select install) - brew install automake - brew install lib tool - tensorflow/contrib/makefile_build_all_ios.sh (takes 20 minutes)
- 63. iOS Examples: camera - Run inception each frame - Models can be replaced
- 65. Raspberry Pi
- 67. Resources TensorFlow: https://www.tensorflow.org/ Magenta: https://magenta.tensorflow.org/welcome-to-magenta TensorFlow for Poets: https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/index.html#0
Notas del editor
- Singalurity
- Escher
- 48 layers It is too much? To provide context: in 2011, a neural network with more than 4 layers deep.
- If you try to put the model as it comes from the training into the Android platform it will not work, because there are certain operations not allowed in Android. We need to strip the model. The output of a trained model is around 84 MB. This is a Whopper XXL. We want to downsize and optimise it to Android. Graph Transform Tool. We quantize the model. What this means? The model is a file filed with numbers. Floating point numbers with 32 bits and a high precision and high space requirement. We are going to downsize it to 8 bit. Would not this affect the accuracy? Fair question. Neural Networks are designed to be tolerant of fuzzy inputs. We can save 4x in space and have a marginal decay in accuracy.
- If you develop for Android you write code in Kotlin or Java. TensorFlow is written in C++ How do you call it?
- If you develop for Android you write code in Kotlin or Java. TensorFlow is written in C++ How do you call it?