Mobile, AI and TensorFlow

Transcript

1. Mobile, AI and Tensorflow
   Supriya Srivatsa
   

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4. NEURAL NETWORKS
   Human anatomy inspired learning network.
   

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5. Neural Networks
   

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6. Neural Networks
   

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7. Neural Network – A Peek Inside
   

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8. Deep Neural Network
   

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9. PREDICTION AND INFERENCE
   How it works today. How it shall work tomorrow.
   

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10. “Transfer to Infer” Approach
    

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11. Why On-Device Prediction
    • Data Privacy
    • Poor internet connections
    • Questionable user experience
    

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12. To The Rescue…
    

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13. TensorFlow
    • Tensor: N Dimensional Arrays
    • Open source software library for numerical
    computation using data flow graphs.
    

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14. TensorFlow – Data Flow Graphs
    • Nodes represent mathematical functions
    • Edges represent tensors.
    

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15. Tensorflow – “Deferred Execution” Model
    • Graph first. Computation Afterward.
    import tensorflow as tf
    x = tf.constant(10)
    y = tf.Variable(x + 5)
    print(y)
    

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16. Tensorflow – “Deferred Execution” Model
    • Graph first. Computation Afterward.
    import tensorflow as tf
    x = tf.constant(10)
    y = tf.Variable(x + 5)
    model = tf.global_variables_initializer()
    with tf.Session() as session:
    session.run(model)
    print(session.run(y))
    

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19. Packaging the App and the Model
    

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20. QUANTIZATION
    Compress. And Compress More.
    

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21. Quantization
    • Round it up
    • Transform: round_weights
    • Compression rates: ~8% => ~70%
    • Shrink down node names
    • Transform: obfuscate_names
    • Eight bit calculations
    

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22. Quantization - Eight Bit Calculations
    

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23. Quantization - Eight Bit Calculations
    

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25. IMPLEMENTATION
    Code Away! ☺
    

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26. Implementation
    build.gradle
    buildscript {
    repositories {
    jcenter()
    }
    dependencies {
    classpath 'com.android.tools.build:gradle:2.3.0'
    }
    }
    

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27. Implementation
    1. Load
    2. Feed
    3. Run
    4. Fetch
    

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28. Implementation
    1. Load the model
    2. Feed in the input
    3. Run the model
    4. Fetch the output
    TensorFlowInferenceInterface inferenceInterface =
    new TensorFlowInferenceInterface(assetManager, modelFile);
    

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29. Implementation
    1. Load the model
    2. Feed in the input
    3. Run the model
    4. Fetch the output
    // feed(String s, float[] floats, long… longs)
    inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3);
    

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30. Implementation
    1. Load the model
    2. Feed in the input
    3. Run the model
    4. Fetch the output
    inferenceInterface.run(outputNames);
    

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31. Implementation
    1. Load the model
    2. Feed in the input
    3. Run the model
    4. Fetch the output
    // fetch(String s, float[] floats)
    inferenceInterface.fetch(outputName, outputs);
    

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32. APPLICATIONS
    Awesomeness.
    

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33. Google Translate
    

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