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Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
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Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
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Antonio Gulli
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Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 FREE CHAPTER 2. TensorFlow 1.x and 2.x 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
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...

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