Book Image

Machine Learning with TensorFlow 1.x

By : Quan Hua, Saif Ahmed, Shams Ul Azeem
Book Image

Machine Learning with TensorFlow 1.x

By: Quan Hua, Saif Ahmed, Shams Ul Azeem

Overview of this book

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim. By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Table of Contents (13 chapters)
Free Chapter
1
Getting Started with TensorFlow

The machine learning briefcase

We just created nice, clean, pickle files with preprocessed images to train and test our classifier. However, we've ended up with 20 pickle files. There are two problems with this. First, we have too many files to keep track of easily. Secondly, we've only completed part of our pipeline, where we've processed our image sets but have not prepared a TensorFlow consumable file.

Now we will need to create our three major sets—the training set, the validation set, and the test set. The training set will be used to nudge our classifier, while the validation set will be used to gauge progress on each iteration. The test set will be kept secret until the end of the training, at which point, it will be used to test how well we've trained the model.

The code to do all this is long, so we'll leave you to review the Git repository...