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

Automating runs

When trying to train a classifier, we will often end up with multiple variables for which we don't know a good setting. Viewing values used by solutions for similar problems is a good starting point. However, we are often left with an array of possible values that we need to test. To make things more complicated, we often have several such parameters, resulting in numerous combinations that we may need to test.

For such situations, we suggest keeping the parameters of interest as values that can be passed into the trainer. Then, a wrapper script can pass in various combinations of the parameters, along with a unique output log subdirectory that is possibly tagged with a descriptive name.

This will allow an easy comparison of results and intermediate values across multiple tests. The following figure shows four runs' losses plotted together. We can easily...