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Mastering TensorFlow 1.x

You're reading from   Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
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Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 2. High-Level Libraries for TensorFlow FREE CHAPTER 3. Keras 101 4. Classical Machine Learning with TensorFlow 5. Neural Networks and MLP with TensorFlow and Keras 6. RNN with TensorFlow and Keras 7. RNN for Time Series Data with TensorFlow and Keras 8. RNN for Text Data with TensorFlow and Keras 9. CNN with TensorFlow and Keras 10. Autoencoder with TensorFlow and Keras 11. TensorFlow Models in Production with TF Serving 12. Transfer Learning and Pre-Trained Models 13. Deep Reinforcement Learning 14. Generative Adversarial Networks 15. Distributed Models with TensorFlow Clusters 16. TensorFlow Models on Mobile and Embedded Platforms 17. TensorFlow and Keras in R 18. Debugging TensorFlow Models 19. Tensor Processing Units
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Neural Network Models in Keras

Neural network models in Keras are defined as the graph of layers. The models in Keras can be created using the sequential or the functional APIs. Both the functional and sequential APIs can be used to build any kind of models. The functional API makes it easier to build the complex models that have multiple inputs, multiple outputs and shared layers.

Thus as a rule of thumb, we have seen engineers use the sequential API for simple models built from simple layers and the functional API for complex models involving branches and sharing of layers. We have also observed that building simple models with the functional API makes it easier to grow the models into complex models with branching and sharing. Hence for our work, we always use the functional API.

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