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Building Mobile Applications with TensorFlow
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Building Mobile Applications with TensorFlow
by Pete Warden
Building Mobile Applications with TensorFlow
Building Mobile Apps with TensorFlow
Challenges of Building a Mobile App with TensorFlow
Understanding the Basics of TensorFlow
What Is TensorFlow?
What Level of Knowledge Do You Need?
What Hardware and Software Should You Have?
What Is TensorFlow Useful for on Mobile?
How Does It Fit with the Cloud?
What Should You Do Before You Get Started?
Common Model Patterns
Building TensorFlow for Your Platform
Android
Android Inference Library
iOS
Raspberry Pi
Integrating the TensorFlow Library into Your Application
Linking the Library
Global Constructor Magic
Protobuf Problems
Protobuf Compatibility Issues
Calling the TensorFlow API
C++ op API
Training On-Device
What Are the Minimum Device Requirements for TensorFlow?
Preparing Your Model File for Mobile Deployment
What’s Up with All the Different Saved File Formats?
How Do You Get a Model You Can Use on Mobile?
Using the Graph Transform Tool
What Ops Are Available on Mobile?
Optimizing for Latency, RAM Usage, Model File Size, and Binary Size
Model Size
Speed
How to Profile Your Model
Profiling Within Your Own App
Visualizing Models
Threading
Binary Size
Retrain with Mobile Data
Need to Reduce Model Loading Time or Memory Footprint?
Protecting Model Files from Easy Copying
Exploring Quantized Calculations
Quantization Challenges
Quantized Representation
Representation Drawbacks
Using Quantized Calculations in TensorFlow
Why Does Quantization Removal Matter?
Activation Ranges
Dynamic Ranges
Observed Ranges
Trained Ranges
What Next?
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Building Mobile Applications with TensorFlow
Building Mobile Applications with TensorFlow
Pete Warden
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