TensorFlow: Data and Deployment Specialization
1. Browser-based Models with TensorFlow.js
2. Device-based Models with TensorFlow Lite
3. Data Pipelines with TensorFlow Data Services
4. Advanced Deployment Scenarios with TensorFlow
6 hours to complete
Device-based models with TensorFlow Lite
1 hour to complete
Running a TF model in an Android App
2 hours to complete
Building the TensorFLow model on IOS
2 hours to complete
TensorFlow Lite on devices
basic understanding of Kotlin and/or Swift, as well as Android Studio and/or Xcode, will help you follow along.
W1:
lightweight+low-latency+privacy+improved power consumption+efficient model ready to used
Quantization
All available CPU platforms are supported
Reducing latency and inference cost
Low memory footprint
Allow execution on hardware restricted-to or optimize for fixed-point operations
Optimized models for special purpose HW accelerator TUP
Weight pruning
Model topology transforms
Tensor decomposition
Distillation
Chrome as our internet browser, Brackets as our HTML editor and the Web Server for Chrome App as our web server.
TF.js: training and inference on browser
Chrome
CSV:
W2:
https://developer.android.com/studio
Android (cat and dog)
mobileNet classification android
MobileNet ssd up to 10 objects from 80 classes
CNN+javascript
Tf-vis
Tf.tidy() -> save memory
W3:
https://github.com/tensorflow/tfjs-models
http://www.laurencemoroney.com/wp-content/uploads/2019/04/labels.txt
Converting Models to JavaScript
Install Wget on Mac/Linux
1. Install Homebrew by running the following command in your terminal:
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
2. Install wget byrunning the following command in your terminal:
$ brew install wget
Install Wget on Windows
Download the wget.exe file from the links provided. You can download the latest version of wget for either 32-bit or 64-bit systems.
If prompted, click Run or Save.
If you chose Save, double-click the downloaded file to start installing.
W4:
Transfer learning mobileNet