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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning Other Books You May Enjoy Index

Tuning hyperparameters and advanced FFNNs

The flexibility of neural networks is also one of their main drawbacks: there are many hyperparameters to tweak. Even in a simple MLP, you can change the number of layers, the number of neurons per layer, and the type of activation function to use in each layer. You can also change the weight initialization logic, the drop out keep probability, and so on.

Additionally, some common problems in FFNNs, such as the gradient vanishing problem, and selecting the most suitable activation function, learning rate, and optimizer, are of prime importance.

Tuning FFNN hyperparameters

Hyperparameters are parameters that are not directly learned within estimators. It is possible and recommended that you search the hyperparameter space for the best cross-validation (http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation) score. Any parameter provided when constructing an estimator may be optimized in this manner. Now, the question is: how you...

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