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Advanced Deep Learning with R

You're reading from  Advanced Deep Learning with R

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Pages 352 pages
Edition 1st Edition
Languages
Author (1):
Bharatendra Rai Bharatendra Rai
Profile icon Bharatendra Rai

Table of Contents (20) Chapters

Preface 1. Section 1: Revisiting Deep Learning Basics
2. Revisiting Deep Learning Architecture and Techniques 3. Section 2: Deep Learning for Prediction and Classification
4. Deep Neural Networks for Multi-Class Classification 5. Deep Neural Networks for Regression 6. Section 3: Deep Learning for Computer Vision
7. Image Classification and Recognition 8. Image Classification Using Convolutional Neural Networks 9. Applying Autoencoder Neural Networks Using Keras 10. Image Classification for Small Data Using Transfer Learning 11. Creating New Images Using Generative Adversarial Networks 12. Section 4: Deep Learning for Natural Language Processing
13. Deep Networks for Text Classification 14. Text Classification Using Recurrent Neural Networks 15. Text classification Using Long Short-Term Memory Network 16. Text Classification Using Convolutional Recurrent Neural Networks 17. Section 5: The Road Ahead
18. Tips, Tricks, and the Road Ahead 19. Other Books You May Enjoy

Reviewing results

In this section, we will review the network losses that were obtained from 100 iterations. We will also take a look at the progress of using fake images from iteration 1 to 100.

Discriminator and GAN losses

The discriminator and GAN loss values that were obtained from our 100 iterations can be plotted as follows. The discriminator loss is based on the loss values for the fake and real images:

From the preceding plot, we can make the following observations:

  • The loss values for the discriminator network and the GAN show high variability during the first 20 iterations. This variability is an outcome of the learning process.
  • The discriminator and generator networks are competing against each other and trying...
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