Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)

Densely connected convolutional networks (DenseNet)

Figure 2.4.1: A 4-layer Dense block in DenseNet. The input to each layer is made of all the previous feature maps.

DenseNet attacks the problem of vanishing gradient using a different approach. Instead of using shortcut connections, all the previous feature maps will become the input of the next layer. The preceding figure, shows an example of a dense interconnection in one Dense block.

For simplicity, in this figure, we'll only show four layers. Notice that the input to layer l is the concatenation of all previous feature maps. If we designate the BN-ReLU-Conv2D as the operation H(x), then the output of layer l is:

(Equation 2.4.1)

Conv2D uses a kernel of size 3. The number of feature maps generated per layer is called the growth rate, k. Normally, k = 12, but k = 24 is also used in the paper, Densely Connected Convolutional Networks, Huang, and others, 2017 [5]. Therefore, if the number of feature maps


, then the total number...