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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

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Length 352 pages
Edition 1st Edition
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Author (1):
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Bharatendra Rai Bharatendra Rai
Author Profile Icon Bharatendra Rai
Bharatendra Rai
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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...
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