Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

Encoder-Decoder architecture – seq2seq

The example of a many-to-many network we just saw was mostly similar to the many-to-one network. The one important difference was that the RNN returns outputs at each time step instead of a single combined output at the end. One other noticeable feature was that the number of input time steps was equal to the number of output time steps. As you learn about the encoder-decoder architecture, which is the "other," and arguably more popular, style of a many-to-many network, you will notice another difference – the output is in line with the input in a many-to-many network, that is, it is not necessary for the network to wait until all of the input is consumed before generating the output.

The Encoder-Decoder architecture is also called a seq2seq model. As the name implies, the network is composed of an encoder and a decoder part, both RNN-based, and capable of consuming and returning sequences of outputs corresponding to...