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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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Preprocessing the dataset for RNN models with Keras

Building an RNN network in Keras is much simpler as compared to building using lower=level TensorFlow classes and methods. For Keras, we preprocess the data, as described in the previous sections, to get the supervised machine learning time series datasets: X_train, Y_train, X_test, Y_test.

From here onwards, the preprocessing differs. For Keras, the input has to be in the shape (samples, time steps, features). As we converted our data to the supervised machine learning format, while reshaping the data, we can either set the time steps to 1, thus feeding all input time steps as features, or we can set the time steps to the actual number of time steps, thus feeding the feature set for each time step. In other words, the X_train and X_test datasets that we obtained earlier could be reshaped as one of the following methods:

Method...

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