Book Image

TensorFlow 1.x Deep Learning Cookbook

Book Image

TensorFlow 1.x Deep Learning Cookbook

Overview of this book

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, and autoencoders.
Table of Contents (15 chapters)
14
TensorFlow Processing Units

DBN for Emotion Detection

In this recipe, we will learn how to first stack RBMs to make a DBN, and then train it to detect emotions. The interesting part of the recipe is that we employ two different learning paradigms: first, we pretrain RBMs one by one using unsupervised learning, and then, in the end, we have an MLP layer, which is trained using supervised learning.

Getting ready

We use the RBM class we have already created in the recipe Restricted Boltzmann Machine, with just one change, we do not need to reconstruct the image after training now. Instead, our stacked RBMs will be only forward passing the data up to the last MLP layer of DBN. This is achieved by removing the reconstruct() function from the class, and replacing...