Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Advanced Deep Learning with R

You're reading from   Advanced Deep Learning with R Become an expert at designing, building, and improving advanced neural network models using R

Arrow left icon
Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Length 352 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Bharatendra Rai Bharatendra Rai
Author Profile Icon Bharatendra Rai
Bharatendra Rai
Arrow right icon
View More author details
Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Revisiting Deep Learning Basics
2. Revisiting Deep Learning Architecture and Techniques FREE CHAPTER 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

Fitting the model

The code for fitting the model is as follows:

# Fit model
model_one <- model %>% fit(train_x, train_y,
epochs = 10,
batch_size = 128,
validation_split = 0.2)

For fitting the model, we will make use of a 20% validation split, which uses 20,000 movie review data from training data for building the model. The remaining 5,000 movie review training data is used for assessing validation in the form of loss and accuracy. We run 10 epochs with a batch size of 128.

When using a validation split, it is important to note that, with 20%, it uses the first 80% of the training data for training and the last 20% of the training data for validation. Thus, if the first 50% of the review data was negative and the last 50% was positive, the 20% validation split will cause model validation to be based only on positive reviews. Therefore, before using...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image