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

Emotion recognition with CNNs


One of the hardest problems to solve in deep learning has nothing to do with neural networks: it's the problem of getting the right data in the right format. However, the Kaggle platform (https://www.kaggle.com/) provides new problems, and new datasets to study.

Kaggle was founded in 2010 as a platform for predictive modeling and analytics competitions on which companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models. In this section, we show how to make a CNN for emotion detection from facial images. The train and test set of this example can be downloaded from https://inclass.kaggle.com/c/facial-keypoints-detector/data.

Figure 17: Kaggle competition page

The train set consists of 3,761 grayscale images that are 48×48 pixels in size and 3,761 labels, each with 7 elements.

Each element encodes an emotion: 0 = anger, 1 = disgust, 2 = fear, 3 = happiness, 4 = sadness, 5 = surprise, 6 = neutral...