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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Using TensorBoard

Now that we've completely configured TensorBoard and told our networks how to send log data to it, we can start taking advantage of it. In the remainder of the chapter, I'm going to show you some of my favorite ways to use TensorBoard. There is more to TensorBoard than this, and we will revisit additional functionality throughout the remainder of the book.

Visualizing training

Since we've written log data from both the models in Chapter 2, Using Deep Learning to Solve Regression Problems, we can use TensorBoard to compare the two models graphically. Open up TensorBoard and head to the SCALARS tab. You should see something like this. You may need to click loss and val_loss to expand the graphs...

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