It's pretty straightforward to train the model but a little tricky to prepare the model for mobile deployment. Before we can start training, first make sure you already have the TensorFlow model repo (https://github.com/tensorflow/models) cloned in your TensorFlow root directory, as we did in the previous two chapters. Then download the drawing classification training dataset at http://download.tensorflow.org/data/quickdraw_tutorial_dataset_v1.tar.gz, which is about 1.1 GB, create a new folder called rnn_tutorial_data, and unzip the dataset tar.gz file to it. You'll see 10 training TFRecord files and 10 evaluation TFRecord files, as well as two files with the .classes extension, which have the same content and are just plain text for the 345 categories that the dataset can be used to classify, such...

Intelligent Mobile Projects with TensorFlow
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Intelligent Mobile Projects with TensorFlow
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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)
Preface
Getting Started with Mobile TensorFlow
Classifying Images with Transfer Learning
Detecting Objects and Their Locations
Transforming Pictures with Amazing Art Styles
Understanding Simple Speech Commands
Describing Images in Natural Language
Recognizing Drawing with CNN and LSTM
Predicting Stock Price with RNN
Generating and Enhancing Images with GAN
Building an AlphaZero-like Mobile Game App
Using TensorFlow Lite and Core ML on Mobile
Developing TensorFlow Apps on Raspberry Pi
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