A beginner's guide to Machine Learning on the Edge using Android and TensorFlow

1. Android+TensorFlow™
A beginner’s guide to Machine Learning on the Edge
@alex_dennis - Alex Dennis

3. How about
“Not Banana”?

4. First things first, show me the model

5. It’s all about the data

6. What is a Tensor?
● Basically Data
● Numbers that represent data
● A typed multi-dimensional array (from the docs)

7. Training your deep net
● Network Topology: CNN, RNN, LSTM, …
● Cost function
● Hyperparameters: Learning rate, Stride Length, Batch Size, Training Iterations, etc.
● Learning Algorithms: Gradient Descent, Stochastic Gradient Descent, Mini Batch Gradient
Descent, Adagrad, AdaDelta, etc.
● Evaluation techniques: Accuracy, Precision, Recall, F1 Score, etc.
● It can get complicated...

8. Don’t train on all your data
● Keep some, say 20% for testing to prevent overfitting
● If you are fiddling with your training model, maybe even take some out for cross validation

9. Alternatives
● Use an open source model
● Retrain an open source model for your specific needs

10. Retraining Inception v3 to recognize Bananas
● Android code labs - TensorFlow for Poets 1 & 2
● Retrained Inception V3 with my own data and labels
○ Inception has over 1,000 labels
○ I just have needed 2
○ Transfer learning

11. Optimize that model for mobile
● TensorFlow for poets 2
● Optimize the operations in the graph
● Make it fast, Make it small

12. Demo

13. Steps to greatness
● Load the TensorFlow graph file that you trained into TensorFlowInferenceInterface
● Transform your data into the shape of your input tensor
● Initiate an TensorFlow session with TensorFlowInferenceInterface
● Read results from output tensor back into your Android app and update UI or whatever else you
want to do

14. Loading the Graph
● Use the TensorFlowInferenceInterface class from TensorFlow Android library
● Uses AssetManager
● 1.3.1-alpha allows you to pass in an InputStream
○ HTTP
○ openRawResource
○ etc.

15. Transforming your data
● Need to convert into the shape of the input tensor
○ Crop
○ Preprocess
○ etc.

16. Run the data through the Graph
● Background thread
● TensorFlowInferenceInterface::feed
● TensorFlowInferenceInterface::run
● TensorFlowInferenceInterface::fetch

17. Reading the output of the graph
● Convert output into data structures appropriate for your application

18. Not Banana
● Borrows heavily from TFClassify Android sample
● Written in Kotlin
● https://github.com/alexdennis/not-banana-app

19. References
● How HBO’s Silivon Valley built “Not Hotdog”... -
https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorfl
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● TensorFlow Getting Started - https://www.tensorflow.org/get_started/get_started
● Deep Learning Series on Coursera - https://www.coursera.org/specializations/deep-learning
● Machine Learning on Coursera - https://www.coursera.org/learn/machine-learning
● Open Source TensorFlow models - https://www.youtube.com/watch?v=9ziVGkt8Gg4
● TensorFlow for Poets Android codelab -
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/#0

20. Easy? Right? Questions?