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

3. Conditional GAN

Using the same GAN as in the previous section, a condition is imposed on both the generator and discriminator inputs. The condition is in the form of a one-hot vector version of the digit. This is associated with the image to be produced (generator) or classified as real or fake (discriminator). The CGAN model is shown in Figure 4.3.1.

CGAN is similar to DCGAN except for the additional one-hot vector input. For the generator, the one-hot label is concatenated with the latent vector before the Dense layer. For the discriminator, a new Dense layer is added. The new layer is used to process the one-hot vector and reshape it so that it is suitable for concatenation to the other input of the succeeding CNN layer.

Figure 4.3.1: The CGAN model is similar to DCGAN except for the one-hot vector, which is used to condition the generator and discriminator outputs

The generator learns to generate fake images from a 100-dim input vector and a specified...