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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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Adding Layers to the Keras Model

All the layers mentioned in the previous section need to be added to the model we created earlier. In the following sections, we describe how to add the layers using the functional API and the sequential API.

Sequential API to add layers to the Keras model

In the sequential API, you can create layers by instantiating an object of one of the layer types given in the preceding sections. The created layers are then added to the model using the model.add() function. As an example, we will create a model and then add two layers to it:

model = Sequential() 
model.add(Dense(10, input_shape=(256,))
model.add(Activation('tanh'))
model.add(Dense(10))
model.add(Activation('softmax'))
...
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