Book Image

Advanced Deep Learning with R

By : Bharatendra Rai
Book Image

Advanced Deep Learning with R

By: Bharatendra Rai

Overview of this book

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Revisiting Deep Learning Basics
3
Section 2: Deep Learning for Prediction and Classification
6
Section 3: Deep Learning for Computer Vision
12
Section 4: Deep Learning for Natural Language Processing
17
Section 5: The Road Ahead

Training the network

In this section, we will out training of the network. While training the network, we will save fake images and store loss values to review the training progress. They will help us assess the effectiveness of the network when creating realistic fake images.

Initial setup for saving fake images and loss values

We will start by specifying a few things that we will need for the training process. Let's take a look at the following code:

# Initial settings
b <- 50
setwd("~/Desktop/")
dir <- "FakeImages"
dir.create(dir)
start <- 1; dloss <- NULL; gloss <- NULL

From the preceding code, we can observe the following:

  • We will use a batch size (b) of 50.
  • We will save fake images...