Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza
Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (16 chapters)
14
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15
Index

4. Densely Connected Convolutional Network (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 let BN-ReLU-Conv2D be represented by the operation H(x), then the output of layer l is:

xl = H (x0,x1,x2, ,xl-1) (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 by Huang et al. (2017) [5]. Therefore...