Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Dataset preparation


Our task is to build an image classifier that distinguishes between dogs and cats. We get some help from Kaggle, from which we can easily download the dataset: https://www.kaggle.com/c/dogs-vs-cats/data.

In this dataset, training set contains 20,000 labeled images, and the test and validation sets have 2,500 images.

To use the dataset, you must reshape each image to 227×227×3. In order to do this, you can use the Python code in prep_images.py. Otherwise, you can use the trainDir.rar and testDir.rar files from the repository of this book. They contain 6,000 reshaped images of dogs and cats for training, and 100 reshaped images for testing.

The following fine-tuning implementation, described in the section below, is implemented in alexnet_finetune.py , which is downloadable in the code repository of the book.