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

Actual cats and dogs

We've demonstrated our new tools on the notMNIST dataset, which was helpful as it served to provide a comparison to our earlier simpler network setup. Now, let's progress to a more difficult problem—actual cats and dogs.

We'll utilize the CIFAR-10 dataset. There will be more than just cats and dogs, there are 10 classes—airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks. Unlike the notMNIST set, there are two major complexities, which are as follows:

  • There is far more heterogeneity in the photos, including background scenes
  • The photos are color

We have not worked with color datasets before. Luckily, it is not that different from the usual black and white dataset—we will just add another dimension. Recall that our previous 28x28 images were flat matrices. Now, we'll have 32x32x3 matrices...