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

Revisiting notMNIST

Let's start our effort incrementally by trying the technical changes on the notMNIST dataset we used in Chapter 2, Your First Classifier. You can write the code as you go through the chapter, or work on the book's repository at:

https://github.com/mlwithtf/mlwithtf/blob/master/chapter_02/training.py.

We will begin with the following imports:

    import sys, os 
    import tensorflow as tf 
    sys.path.append(os.path.realpath('../..')) 
    from data_utils import * 
    from logmanager import * 
    import math

There are not many substantial changes here. The real horsepower is already imported with the tensorflow package. You'll notice that we reuse our data_utils work from before. However, we'll need some changes there.

The only difference from before is the math package, which we will use for ancillary math functions, such as...