Book Image

Intelligent Mobile Projects with TensorFlow

By : Jeff Tang
Book Image

Intelligent Mobile Projects with TensorFlow

By: Jeff Tang

Overview of this book

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.
Table of Contents (14 chapters)

Retraining using the Inception v3 model

In the TensorFlow source that we set up in the previous chapter, there's a Python script, tensorflow/examples/image_retraining/retrain.py, that you can use to retrain the Inception v3 or MobileNet models. Before we run the script to retrain the Inception v3 model for our dog breed recognition, we need to first download the Stanford Dogs Dataset (http://vision.stanford.edu/aditya86/ImageNetDogs), which contains images of 120 dog breeds (you only need to download the Images in the link, not the Annotations).

Untar the downloaded dog images.tar file in ~/Downloads, and you should see a list of folders in ~/Downloads/Images, as shown in the following screenshot. Each folder corresponds to one dog breed and contains about 150 images (you don't need to supply explicit labels for images as the folder names are used to label the images...