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

Caffe to TensorFlow

In this section, we will show you how to take advantage of many pre-trained models from Caffe Model Zoo (https://github.com/BVLC/caffe/wiki/Model-Zoo). There are lots of Caffe models for different tasks with all kinds of architectures. After converting these models to TensorFlow, you can use it as a part of your architectures or you can fine-tune our model for different tasks. Using these pre-trained models as initial weights is an effective approach for training instead of training from scratch. We will show you how to use a caffe-to-tensorflow approach from Saumitro Dasgupta at https://github.com/ethereon/caffe-tensorflow.

However, there are lots of differences between Caffe and TensorFlow. This technique only supports a subset of layer types from Caffe. Even though there are some Caffe architectures that are verified by the author of this project such as...