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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

Creating a custom TensorFlow Lite model for image recognition

Once you have had a fair go at Colaboratory, we're all set up to build the custom TensorFlow Lite model for the task of recognizing plant species. To do so, we will begin with a new Colaboratory notebook and perform the following steps:

  1. Import the necessary modules for the project. Firstly, we import TensorFlow and NumPy. NumPy will be useful for handling the image arrays, and TensorFlow will be used to build the CNN. The code to import the modules can be seen in the following snippet:
!pip install tf-nightly-gpu-2.0-preview
import tensorflow as tf
import numpy as np
import os

Notice the !pip install <package-name> command used on the first line. This is used to install packages in a running Colaboratory notebook, which, in this case, installs the latest TensorFlow release that internally implements the...

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