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

Drinking from the firehose

As you did earlier, you should grab the code from https://github.com/mlwithtf/MLwithTF/.

We will be focusing on the chapter_05 subfolder that has the following three files:

  • data_utils.py
  • translate.py
  • seq2seq_model.py

The first file handles our data, so let's start with that. The prepare_wmt_dataset function handles that. It is fairly similar to how we grabbed image datasets in the past, except now we're grabbing two data subsets:

  • giga-fren.release2.fr.gz
  • giga-fren.release2.en.gz

Of course, these are the two languages we want to focus on. The beauty of our soon-to-be-built translator will be that the approach is entirely generalizable, so we can just as easily create a translator for, say, German or Spanish.

The following screenshot is the specific subset of code:

Next, we will run through the two files of interest from earlier line by...